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Original research article, affective learning in digital education—case studies of social networking systems, games for learning, and digital fabrication.

• Faculty of Education, University of Oulu, Oulu, Finland

Technological innovations, such as social networking systems, games for learning, and digital fabrication, are extending learning and interaction opportunities of people in educational and professional contexts. These technological transformations have the ability to deepen, enrich, and adaptively guide learning and interaction, but they also hold potential risks for neglecting people's affective learning processes—that is, learners' emotional experiences and expressions in learning. We argue that technologies and their usage in particular should be designed with the goal of enhancing learning and interaction that acknowledges both fundamental aspects of learning: cognitive and affective. In our empirical research, we have explored the possibility of using various types of emerging digital tools as individual and group support for cognitively effortful and affectively meaningful learning. We present four case studies of experiments dealing with social networking systems, programming with computer games, and “makers culture” and digital fabrication as examples of digital education. All these experiments investigate novel ways of technological integration in learning by focusing on their affective potential. In the first study, a social networking system was used in a higher education context for providing a forum for online learning. The second study demonstrates a Minecraft experiment as game-based learning in primary school education. Finally, the third and the fourth case study showcases examples of “maker” contexts and digital fabrication in early education and in secondary school. It is concluded that digital systems and tools can provide multiple opportunities for affective learning in different contexts within different age groups. As a pedagogical implication, scaffolding in both cognitive and affective learning processes is necessary in order to make the learning experience with emerging digital tools meaningful and engaging.

Introduction

Current technological transformations in society bring new abilities for sensing, adapting, and providing information to users within their environments ( Laru et al., 2015 ; Chang et al., 2018 ; Huang et al., 2019 ). This can, for example, deepen, enrich, and guide educational and professional interactions ( Rummel, 2018 ; Stracke and Tan, 2018 ). Technologies have already been used to improve participants' cognitive learning experiences, to create efficient and constructive communication, and to effectively use shared resources, as well as to find and build groups and communities ( Jeong and Hmelo-Silver, 2016 ).

However, research has also shown that technology can alter social interactions. For instance, technology can affect the self-disclosure and identity management of individuals ( Yee and Bailenson, 2007 ) as well as provide an arena for bullying (Santiago and Siklander, in review), thus running the risk of inhibiting productive social interactions or providing less than optimal support for them. In terms of group interactions and technologically enhanced collaborations in particular, challenges may relate to a cognitive load too excessive to efficiently handle content and task related activities simultaneously with social and technological factors ( Bruyckere et al., 2015 ; May and Elder, 2018 ; Pedro et al., 2018 ) or the lack of available important social cues for social information processing, particularly in text-based communications ( Kreijns et al., 2003 ; Walther, 2011 ; Terry and Cain, 2016 ). This discussion of technology's challenges is particularly relevant in bigger online learning communities and social networking systems, but also in small group collaboration ( Bodemer and Dehler, 2011 ; Davis, 2016 ), such as in the context of games for learning, digital fabrication, and “maker” education.

Social networking systems, games for learning, and digital fabrication (making) will be further examined in this paper with case study examples. These case examples are chosen with regard to their likely impact on learning and instruction in current and future educational designs ( Woolf, 2010 ; Chang et al., 2018 ; Huang et al., 2019 ). One of the main challenges that teachers face in the context of adopting contemporary technologies to support learning activities is the fact that professional knowledge and competencies are needed in both technology and pedagogy ( Valtonen et al., 2019 ). This means that in addition to technical aspects, it is important that teachers understand and consider the basic processes of how people learn as an individual and as part of collaborative group ( Häkkinen et al., 2017 ). Therefore, it is essential to explore and characterize learning and interaction processes, including cognitive and affective components, when digital tools and learning environments are implemented in educational contexts.

This paper is grounded in the premise that technologies should enhance the cognitive and affective learning processes in collaboration. Emotional experiences and expressions are recognized as an especially central part of successful collaborative learning ( Baker et al., 2013 ). The use of potential technological enhancements in collaboration necessitates an interdisciplinary understanding of the social factors and emotional dynamics influencing the learning and interaction processes. We argue that when the affective interactions are more thoroughly accounted for and enhanced through technology, they can have positive implications for cognitively effortful and affectively meaningful collaborations, thus contributing to better competence building, social equity, and participation in group workings ( Järvenoja and Järvelä, 2013 ; Isohätälä et al., 2017 ; Järvenoja et al., 2018 ).

Collaborative Learning as a Cognitive and Affective Learning Process

Collaborative learning is a specific type of learning and interaction process in which learners in a group share their overall learning process by negotiating their goals for learning and coordinating their mutual learning processes together ( Roschelle and Teasley, 1995 ). Since the process of collaborative learning consists of discussions, negotiations, and reflections on the task at hand, it has the potential to lead to deeper information processing than individuals would achieve alone ( Dillenbourg, 1999 ; Baker, 2015 ). The premise for successful collaborative learning is that group members are actively engaged in building, monitoring, and maintaining their shared learning processes on cognitive and affective levels ( Barron, 2003 ; Näykki et al., 2017b ; Isohätälä et al., 2019a ). This means that interpreting and understanding who you are working with, what is being worked on, and how your actions and emotions affect others is essential to obtain successful collaborative learning ( Linnenbrink-Garcia et al., 2011 ; Miyake and Kirschner, 2014 ). We follow the conceptualization that views successful collaborative learning as a combination of an outcome (deeper understanding and developed individual and group learning skills), and an experience (a student's own evaluation and interpretation of how [s]he succeeded) ( Baker, 2015 ).

In general, affective processes play an important role in individuals' learning as well as in groups' learning and interaction processes ( Linnenbrink-Garcia et al., 2011 ; Järvenoja et al., 2015 ; Polo et al., 2016 ; Isohätälä et al., 2019b ). Students' emotions, such as enjoyment, boredom, pride, and anxiety, are seen to affect achievement by influencing their involvement and attitude toward learning and learning environments (e.g., Pekrun et al., 2002 ; Boekaerts, 2003 , 2011 ; Pekrun and Linnenbrink-Garcia, 2012 ). These emotional experiences naturally have a great effect on how students and/or groups work on their task assignments. In our research, we have been particularly interested in the role of emotions as a part of groups' coordinated learning processes—how group members experience emotions and how they express their emotions in order to maintain and restore (when needed) a socio-emotionally secure atmosphere for learning and collaboration ( Näykki et al., 2014 ). This has been done by observing student groups' interaction processes to understand how emotions are expressed, reflected, and shaped by social interaction ( Baker et al., 2013 ; Isohätälä et al., 2017 ; Näykki et al., 2017a ).

We ground this study in the increasing empirical understanding of the multifaceted interaction processes involved in collaborative learning, integrating cognitive, and affective components as the core of collaboration ( Volet et al., 2009 ; Järvel et al., 2010 , 2013 ; Näykki et al., 2014 ; Ucan and Webb, 2015 ; Sobocinski et al., 2016 ; Isohätälä et al., 2019a ; Vuopala et al., 2019 ). In theory, collaborative learning requires group members to be aware of and to coordinate with their cognitive, metacognitive, motivational, and emotional resources and efforts ( Hadwin et al., 2018 ). In practice, this involves students sharing their thinking and understanding, as well as showing verbally and behaviorally their commitment to the task and to the group ( Järvelä et al., 2016 ; Isohätälä et al., 2017 ).

How to Enhance Opportunities for Cognitive and Affective Learning Processes With Pedagogical Designs and Digital Tools

Prior research has suggested that students need a scaffolding to engage with and progress in active and effective collaborative learning ( Kirschner et al., 2006 ; Belland et al., 2013 ). In order to favor the emergence of productive interactions and thus to improve the quality of collaborative learning, different pedagogical models, and design approaches have been developed in collaborative learning research ( Hämäläinen and Häkkinen, 2010 ). One example of a strategy to enhance the process of collaboration is to structure learners' actions with the aid of scripted cooperation ( Fischer et al., 2013 ). Scripting is defined as “a set of instructions prescribing how students should perform in groups, how they should interact and collaborate and how they should solve the problem” ( Dillenbourg, 2002 , p. 63). In other words, scripts support collaborative processes by specifying, sequencing, and distributing the activities that learners are expected to engage in during collaboration ( Dillenbourg, 2002 ; Kollar et al., 2006 ). Scripts typically aim to smooth coordination and communication, but there are also scripts that aim to promote high-level socio-cognitive activities—e.g., explaining, arguing, and question asking ( Weinberger et al., 2005 ; Fischer et al., 2013 ; Tsovaltzi et al., 2017 )—or acknowledge and promote socio-emotional activities ( Näykki et al., 2017a ).

In addition to designing certain learning activities with the scripting approach, previous research in the field of technologically enhanced learning has demonstrated how technology can function as a tool for individuals' and groups' learning, allowing meaningful learning interactions to occur ( Jeong and Hmelo-Silver, 2016 ; Rosé et al., 2019 ). Recently, more generic digital tools such as social networking tools, games, or mobile phones have been increasingly popular among educators and instructional designers ( Ludvigsen and Mørch, 2010 ; Laru et al., 2015 ). Such tools are being progressively more used in educational contexts but are not usually specifically designed to help students to engage in cognitively effortful interaction such as problem solving, collaborative knowledge construction, or inquiry learning ( Gerjets and Hesse, 2004 ). Nor are these tools often designed for affectively meaningful interactions such as expression and reflection of emotional experiences ( Jones and Issroff, 2005 ; Jeong and Hmelo-Silver, 2016 ).

Altogether, these tools rarely offer specific instructional guidance concerning collaborative learning ( Kirschner et al., 2006 ). Instead, both generic and specific cognitive tools ( Kim and Reeves, 2007 ) typically provide an open problem space, where learners are left to their own devices. In such spaces, learners are free to choose (a) what activities to engage in with respect to the problem at hand and (b) how they want to perform those activities ( Kollar et al., 2007 ). Modern social networking systems, games for learning, and contexts for digital fabrication and making can be categorized into open problem spaces where learning is often supported without tightly structured socio-technological instructional design ( Laru et al., 2015 ; Hira and Hynes, 2018 ).

Case Examples in Digital Education

We present and explore four cases ( Table 1 ) involving social networking systems, games for learning, and digital fabrication where emergent and contemporary technologies are used to support collaborative learning in open problem spaces, especially focusing on cognitively effortful and affectively meaningful learning in groups. These emergent digital tools, with their respective socio-technical designs, were selected because they each represent different ways to provide opportunities for affective learning—for experiencing and expressing emotions as well as for supporting equal participation and a safe group atmosphere (cf. Baker et al., 2013 ). Traditionally all these technologies and activities have mainly been present in informal contexts as associated with social lives of the users, and thus, it can be assumed that this is one reason why they are able to access emotions in powerful ways. These technologies also hold the potential for learning in formal education as well, as a part of learning activities organized by educational institutions ( Pedro et al., 2018 ).

Table 1 . Summary of the case examples: social networking systems, games for learning, maker education, and digital fabrication.

CASE 1: Social Networking Systems for Supporting Equal Participation and Collaborative Argumentation

Social Networking Sites (SNS), such as Facebook, Twitter, and Instagram, are widely used communication platforms worldwide because of easy access and unrestricted interactivity ( Bowman and Akcaoglu, 2014 ). They are mostly used for informal, everyday communication, but these platforms also offer possibilities to education by allowing idea sharing and a knowledge co-construction process ( Laru et al., 2012 ; Vuopala et al., 2016 ; Tsovaltzi et al., 2017 ) where learners are interacting and building new frameworks to extend the knowledge and understanding of each individual student ( Janssen et al., 2012 ). These productive interactional processes include sharing ideas, negotiating, asking thought-provoking questions, and providing justified arguments ( Vuopala et al., 2016 ). Studies have also shown that the use of SNS can be beneficial for learning purposes by, for example, fostering affective interactions in academic life, allowing students to share emotional experiences, and providing support for socio-emotional presence ( Pempek et al., 2009 ; Bennett, 2010 ; Ryan et al., 2011 ; Wodzicki et al., 2012 ; Bowman and Akcaoglu, 2014 ).

However, previous studies have proven that in SNS the level of knowledge co-construction and argumentation is often superficial, lacking solid arguments as well as affective interaction ( Bull et al., 2008 ; Dabbagh and Reo, 2011 ). Engaging in these cognitive and affective processes is not necessarily spontaneous, therefore, it is essential to support students' learning processes. One way to promote productive collaborative learning is through the use of pedagogical scripts that have been used for guiding learners to engage both in knowledge co-construction and in affective processes ( Dillenbourg, 2002 ; King, 2007 ; Fischer et al., 2013 ; Näykki et al., 2017a ; Wang et al., 2017 ).

This case study presents research in which Facebook was used as a platform for argumentation. Higher education students ( N = 88) from one German and two Finnish universities participated in a seven week long online course named “CSCL, Computer Supported Collaborative Learning” ( Puhl et al., 2017 ). The course included the following learning topics: scripting, motivation and emotions, and metacognition. Students worked in ten groups with four participants in each. The first phase of the course was orientation and introduction (1 week). The main aim of the orientation week was to allow group members to meet each other (online) and to create a safe group atmosphere. After the orientation phase, each small group had a 2 week period to discuss each presented topic (overall, 6 weeks) in their own closed Facebook group.

Small group collaboration was supported with a micro-script ( Weinberger et al., 2007 ; Noroozi et al., 2012 ), which guided learners into knowledge co-construction and argumentation. The study was particularly focused on exploring how different preassigned roles and sentence openers supported argumentation ( Weinberger et al., 2010 ) and contributed to the groups' affective interactions especially by encouraging students to participate equally and motivating the group atmosphere. The roles given to each student were especially designed to prompt not only productive argumentation but also socio-emotional processes. The roles assigned to the students were: captain (motivated the group members' participation), contributor (identified and elaborated pro-arguments), critic (identified and elaborated counter-arguments), and composer (constructed a synthesis of the pro- and counter-arguments). To support their enactment of the named role, the students were given specific sentence openers, such as: “Have you all understood what is meant by…” (captain), “My claim is…” (contributor), “Here is a different claim I think needs to be taken into account …” (critic) and “To combine previously mentioned perspectives it can be concluded…” (composer). The script was faded out as the course proceeded. During the first 2 weeks, both the roles as well as the sentence openers were used to guide productive collaboration. Next, only the roles were given as a script, without sentence openers. However, students got a different role compared to the first week. And after that, the whole script was faded out; it was expected that, by that time, the learners had internalized the script and were thus able to interact purposefully without external support ( Wecker and Fischer, 2011 ; Noroozi et al., 2017 ).

To reach an understanding of how the students interacted during the course, all discussion notes on Facebook were analyzed ( Puhl et al., 2017 ). This was done by categorizing the discussion notes according to their transactivity to the following categories: quick consensus building, integration-oriented consensus building and conflict-oriented consensus building and in terms of their epistemic dimension: coordination, own explanation, misconception, learning content ( Weinberger and Fischer, 2006 ). In general, students participated equally in the joint discussions according to the roles given to them, but the actual use of the sentence openers was more random. The main results indicated that, with this design, students engaged actively in argumentative knowledge co-construction, and that there were no significant differences in terms of the amount of activity between the differently scripted studying phases. All the assigned roles were treated as equally important in terms of both cognitive and affective aspects of learning even though they promoted different aspects of socio-emotional processes. However, during the course it came clear that the role of captain was especially crucial in promoting a good group atmosphere and keeping the motivation level high. The following examples from group discussions illustrate the captain's contributions:

“Thanks for your comments. These are all interesting thoughts. I agree with you that there is not a ‘one fits for all' solution. While regarding thought on ‘obligation’, well I agree that there is that component as well in any learning situation.”

“If you have some questions while you are reading, if something is unclear or something is just interesting, I'd like to encourage you to post something into the group that we can talk about it. So, enjoy the rest of your weekend and have a nice week.”

These examples illustrate how the captain encouraged group members to participate in joint discussions by giving positive feedback, and by making suggestions how to proceed. The results showed that the roles functioned also for affective level learning by, for example, managing the discourse, inducing conflicts through pro- and counter-arguments, and resolving the conflicts by bringing the different perspectives together. To conclude, in this case example, the roles assisted equal participation, feelings of belonging, and good working relationships between learners. The students' interaction was supportive, and arguments were well-structured. Furthermore, roles kept the discussion on task and there was no confusion about the responsibilities ( Bruyckere et al., 2015 ; May and Elder, 2018 ; Pedro et al., 2018 ).

This example of Facebook as a SNS shows how an actively used “everyday digital tool” provided easy access to and a familiar platform for productive collaborative learning. While students used Facebook regularly for informal communication, they actively followed study-related discussions at the same time. It was obvious that in this case informal and formal communication and collaboration supported each other. The students in this study were asked to follow a specific micro-script, and thus their opportunities for designing their own learning activities were rather limited. Another way to integrate informal and formal education and to provide more open opportunities for creative thinking and problem solving is the use of games for learning, as will be described in the following example.

CASE 2: Games for Learning as Supporting Students' Creativity, Problem Solving, and Programming Skills

Currently, there is an increasing interest in implementing games in an educational context ( Nebel et al., 2016 ; Qian and Clark, 2016 ). Connolly et al. (2012) found in their systematic literature review that playing computer games is linked to a range of perceptual, cognitive, behavioral, affective, and motivational impacts and outcomes. However, previous studies have shown that the game environment itself does not guarantee deep learning and meaningful learning experiences ( Lye and Koh, 2014 ; Mayer, 2015 ). The challenge is that many educational games follow simple designs that are only narrowly focused on academic content and provide drill and practice methods similar to worksheets or stress memorization of facts ( Qian and Clark, 2016 ).

Careful pedagogical design is needed in order to implement an educational game environment as a holistic problem-solving environment. For example, game design elements can provide opportunities for learners' self-expression, discovery, and control. These types of playing activities can create a learning environment that supports students' cognitively effortful and affectively meaningful learning, for example in terms of programming skills, creativity, problem solving ( Kazimoglu et al., 2012 ; Qian and Clark, 2016 ), and motivational engagement ( Bayliss, 2012 ; Zorn et al., 2013 ; Pellas, 2014 ).

This study was designed to integrate informal and formal learning activities for students in the context of an after-school Minecraft club. Minecraft is a multiplayer sandbox game designed around breaking and placing blocks. Unlike many other games, when played in its traditional settings, Minecraft does allow players the freedom to immerse themselves into their own narrative: to build, create, and explore. Minecraft, along with modification software (“mods”), has the tools for teaching and learning programming ( Zorn et al., 2013 ; Risberg, 2015 ; Nebel et al., 2016 ).

The participants in this case study were primary school students ( N = 16, 11 boys, 5 girls, 11 years old) who participated in the after-school Minecraft club ( Ruotsalainen et al., 2020 ). The club included eight 90-min sessions of face-to-face meetings as well as unlimited collaboration time in the virtual space between the meetings. Minecraft gameplay was based on a storyline wherein pirates tried to survive after a shipwreck, escape, and expand their territories to other islands. To be able to escape from the island, several main quests (tasks) had to be solved: tutorial (weeks 1–2), electrical power (week 3), area and volume calculations (week 4), survival of zombie apocalypse (week 5), European flags (week 6), programming (week 7), and a final meeting (week 8). The majority of these quests were ill-structured and challenging problems. Therefore, the designed structure included repetitive pedagogical phases with teacher scaffolding (described below), but also full access to all content at any time (but not guided and explained).

Each week followed a similar structure:

a) Introduction (club meeting), a basic introduction to the session's theme.

b) Guided in-game tour (club meeting) where the respective main quest was presented, trained, and materials were distributed. The Captain (teacher) provided scaffolding for pirate students.

c) Main Quest (club meeting; between meetings, students performed task(s), e.g., building structures or coding).

d) Reflection (club meeting), a group discussion at the end of each session to reflect on task design and game experiences.

e) Free to Play (gameplay between meetings), the phase where students were able to continue their existing activities or explore the game on their own.

f) Captain's Quest (gameplay between meetings), which was similar to the main quest, but tasks were voluntary for students.

g) Presentation(s) for Rewards (next club meeting), an activity where students presented what they had done in the main quest and the Captain's quest. After successfully completing quests, student pirates received rewards in the form of Minecraft objects. Without rewards, student pirates were not able to survive, form society on the island, build better houses, or complete (“win”) the game.

The tools that were designed for the club were the Minecraft game, island map, and three Minecraft modifications ( Figure 1 ). The game map was designed to include problem-based puzzles (quests) and a narrative about escaping from the deserted island after a shipwreck. Modifications enabled teachers to change Minecraft's 18 game rules, alter game content, redesign textures, and give players new abilities within the game ( Kuhn and Dikkers, 2015 ). While the island map provided context for game narrative and gameplay itself, modifications worked as an engine, which enabled real electrical power simulation (ElectricalAge), programming (ComputerCraft), and easy redesign of the learning experiences (WorldEdit) during the game. The three major structures were: a deserted island with a sunken ship (home for the students' characters), the hall of quests, which was a building on the island (main quests were presented here), and the science center located outside of the island (a place with free access to formal lessons and informal training). Collaborative learning was regarded as a fundamental element of the activity in Minecraft gameplay. Therefore, many structural elements were designed to support collaborative game experience; for example, border blocks forced students' avatars to live in a small area next to each other. However, there were no detailed structures or scaffolds designed as a support for collaboration. Students were inhabitants of the Minecraft world, where collaboration is necessary to survive. The following example explains how one student described his/her experienced reasons for collaboration in an interview that were conducted right after the each face to face meeting. In this example one student describes his actions in the main quest “survival of zombie apocalypse.”

“We all came together at the ‘hall of quests’, it was safe and we had time to make up a plan together since there were no zombies. All players were here and we discussed what to do to survive. Most of my friends helped me and I helped them to survive. We had to trust each other, to survive you do teamwork.”

Figure 1. (A) Island at the start of the game when students' ship has wrecked. (B) Island after students have created their society (game activity between club meetings, Captain's quests). (C) Hall of quests, which was the place for information sharing, reflection, and teleportation to the science center. (D) Science center (main quests were played here) with a view into the coding quest.

Overall, the Minecraft game in this study was designed so that knowledge acquisition was prompted (e.g., about electricity), skill acquisition was supported (e.g., programming and collaboration), and affective and motivational outcomes were rewarded (e.g., strategies to accomplish quests and reflections during the meetings). Degrees of freedom guaranteed that the original constructionist gameplay was available for more advanced players, which was needed to avoid frustration or domination during the game ( Connolly et al., 2012 ; Nebel et al., 2016 ). The students underlined in an interview how emotional the game playing experience was for them: “I usually do not really like these guys, but I am kind of sad that this experiment is over. I'm going to miss our village and society a lot. I am pretty sure I won't speak to half of the players anymore.”

To conclude, Minecraft is an example of a constructivist gaming experience in which players can play, modify the game, or even create their own games for learning ( Kafai and Burke, 2015 ). In this case study, the students modified the game. This type of gaming approach has a strong pedagogical connection with another contemporary digital education phenomena: “maker's culture,” making and digital fabrication. While Minecraft is about a block-based world of “digital making,” digital fabrication and making enables learners to design their own artifacts in the situated (unstructured and open-ended) problem solving contexts.

CASE 3: Digital Fabrication and Makers Education for Supporting Collaborative Learning

Making is a central concept in the maker education approach. In practice, making is “a class of activities focused on designing, building, modifying, and/or repurposing material objects, for playing or useful ends, oriented toward making a ‘product' that can be used, interact with, or demonstrated” ( Martin, 2015 , p. 31). Digital fabrication is a concept in parallel with making that is commonly used to describe a process of making physical objects by utilizing digital tools for designing. Digital fabrication activities can be conducted in the context of Fab Lab, that is, a technical prototyping platform “comprised of off-the-shelf, industrial-grade fabrication and electronics tools, wrapped in open source software” ( Fab Foundation, n.d. ).

The basic idea of maker culture and digital fabrication places the learner firmly at the center of the learning process with a focus on a connection to real-world issues and meaningful problems. In the context of digital fabrication and Fab Labs, complex, undefined, open-ended, and unstructured problem-solving activities are typical ( Halverson and Sheridan, 2014 ; Chan and Blikstein, 2018 ). Prior studies in educational contexts have found that maker culture activities hold great potential for developing a sense of personal agency, improving self-efficacy and self-esteem, and supporting learners in becoming an active member of a learning community ( Halverson and Sheridan, 2014 ; Chu et al., 2017 ; Hira and Hynes, 2018 ). Taylor (2016) has concluded that the activities in “makerspaces” can be transformed into classroom projects that match the goals of twenty-first-century education. In other words, the overall learning experience through making can be empowering and can nurture students' creativity and inventiveness among other twenty-first-century skills ( Blikstein, 2013 ; Iwata et al., 2019 ; Pitkänen et al., 2019 ).

This case study presents research that was conducted in an early education context ( Siklander et al., 2019 ). Four to 5 year-old children ( N = 16) took part in the making process in indoor and outdoor making environments: kindergarten, a forest, and Fab Lab facilities at the university ( https://www.oulu.fi/fablab/ ).

In this case study, a narrative was built about an owl, a hand puppet, who asked for the children's help. The topic for learning was healthy food, and the aim was that the children learn to identify healthy and unhealthy food and to create a healthy plate through making, playing, and discussions. The experiment followed the playful learning process ( Hyvönen, 2011 ; Hyvönen et al., 2016 ) and started with an orientation phase that aimed to support the children's activation of prior knowledge by creating a concept map about the topic of “good health.” In other words, the starting point for children's making activities was their own investigations of the concept and events closely connected with their living environments and personal experiences. After the orientation, the hand puppet owl asked for the children's assistance in creating a healthy plate. In the first making activity, children searched for and cut out figures representing healthy food and created a healthy plate by using the selected figures. Next, the owl asked the children to cook food in the nearby forest and to serve it to the forest animals. The children orienteered to the forest, collected items in accordance with the recipe, cooked the food, and laid the table on the ground. After feasting with the children, the owl asked children to feed all the forest animals. This challenging task requested children to prepare fabricated food.

The next phase of the experiment was conducted in the FabLab. The researchers' role ( Hyvönen, 2011 ) was to understand and support the children's cognitive, emotional, and social views on making activities, although the environment was technical, noisy, and adult sized. The aim was to provide an emotionally and physically safe atmosphere and to encourage children to interact, enjoy, and express themselves while working together. After using the different senses (e.g., the smell of burning wood diffusing from the laser cutter), and taking a look at the facilities, technological equipment, and displayed outcomes, the owl's request was discussed. First, a big plate out of plywood was laser cutted. Research assistants guided the activities, and they let each child test the steering device and press the buttons. The children watched the cutting process very intensely, and were delighted while the plate was done, wanting also to touch and smell it. Finally, each child chose his or her favorite Muumin character and laser cut it to take home.

The process ended with the elaboration phase, in which the photo-elicitation method was used ( Dockett et al., 2017 ) for reflecting on and discussing the entire process with the children. They chose photos which they felt were interesting and inspiring during the process; thus, these photos represent positive emotions. They chose photos taken from the forest trip and the FabLab activities. The most meaningful objects in the forest were the map, which facilitated orienteering, the recipe, which allowed them to find items and count them, and the fire, which they set for cooking. These elements combine affective and cognitive learning with physical actions. Children held the map each by each, and carefully looked at it and the path ahead ( Pictures 1 , 2 ).

Picture 1 . Children cooking according to the recipe. Written informed consent was obtained from the parents of all depicted children for the publication of these images.

Picture 2 . Children at the FabLab presenting their ideas for the owl, other children, and adults around. Written informed consent was obtained from the parents of all depicted children for the publication of these images.

The Fab Lab was regarded also as a meaningful makerspace. With its many technologies, it provided totally new experiences for the children. It was experienced as exciting and activated the children's collaboration, imagination, interest, and inspiration. During the experiment, the children's interaction was filled with humor and evolved in the process of thought bouncing.

In this case study, making activities and the playfulness of this process ( Hyvönen, 2011 ; Hyvönen et al., 2016 ) denoted affectivity in two ways: first, the process of making was designed to allow children to experience emotions such as curiosity, joy, agency, acceptance, and excitement, but also negative feelings such as impatience, frustration, and disappointment (see also Hyvönen and Kangas, 2007 ). Secondly, during the activities and interaction, children were able to learn to recognize, and regulate their emotions. This was evident particularly in collaborative situations when children had to wait their turns, or when they were together and excited to express their ideas. To conclude, it can be said that, for children, making is not a specific type of activity, but rather the natural way of playfully being and engaging in any activity, including their own emotions, other people, and playthings ( Duncan and Planes, 2015 ).

CASE 4. Supporting Fab Lab Facilitators to Develop Pedagogical Practices to Improve Learning in Digital Fabrication Activities

This case study was conducted also in the context of Fab Lab. The aim of this case study was to explore what technology experts should take into consideration in planning and facilitating students' learning processes in digital fabrication. This was done to provide research evidence about the design and implementation of digital fabrication activities. In practice, current undertakings in the local Fab Lab were explored from two perspectives: how current practices consider novice students' learning and how facilitators and teachers provide scaffolding in unstructured problem solving ( Pitkänen et al., 2019 ).

The local Fab Lab was established in 2015 (see https://www.oulu.fi/fablab/ ). Since then, Fab Lab has arranged different types of digital fabrication activities for school groups. The activities have typically included 2D and 3D design and manufacturing, prototyping with electronics, programming, and utilizing tools and machines to fabricate prototypes ( Georgiev et al., 2017 ; Iwata et al., 2019 ; Laru et al., 2019 ; Pitkänen et al., 2019 ).

In this case study (Iwata et al., in review), three schools participated in digital fabrication activities in Fab Lab ( Table 2 ). The school participants, in total 41 students (aged 12–15 years old) and five teachers, were from three secondary schools. The activities were facilitated by two technology experts (facilitators), who work in the Fab Lab. In order to understand the making and digital fabrication activities, the participants were observed during the practice, and interviews of 14 students, the five teachers, and the two facilitators were conducted both during and at the end of the activities. Furthermore, the perspectives of the two expert groups (school teachers and Fab Lab facilitators) were investigated with focus group interviews.

Table 2 . The three schools participating in digital fabrication activities.

The students worked on projects in teams with different design briefs and required conditions provided by facilitators and/or the teachers. All student projects were complex and required knowledge and skills in multiple subjects, such as mathematics, physics, and art (STEAM concept) ( Table 2 ). Yet, these projects were difficult for them to complete without collaborative problem solving. The following excerpt is from a teacher's interview:

“One girl said that in normal group activities in school, she would have taken like the whole control, but this one was so huge, and she realized that she couldn't do that. So, she had to delegate. That was precious that she had to trust the team and that she can't control everything.”

Based on the interviews six factors were identified which influenced students' learning in the Fab Lab:

1) The tasks were complex and multidisciplinary.

2) Computers and digital tools were used frequently.

3) Students' own roles and responsibilities were emphasized in the guidance given.

4) Opportunities for reflection were supported.

5) Trial and error was encouraged.

6) An appropriate range of flexibility was embraced with time frame.

The following example shows how the school teacher explained the digital fabrication activities:

“You go and just try and error and it doesn't even matter if you totally succeed or fail on the product.… the important thing is what kind of cognitive skills and how you reflect, what you learn in the process, and if you came back, what would you do better.”

However, not all students who participated in these digital fabrication activities had previous knowledge and experience in the field. Moreover, many of them were not used to applied work methods that require competencies such as self-regulation, self-efficacy, and persistence. Based on the results, there is a need for defining clear learning goals and instructions, which would help students to engage in unstructured, open-ended, problem-solving activities. Furthermore, the lack of structure in the activities made both the teachers and facilitators point out the need to scaffold learning. The following is an excerpt from the interview of a teacher which underlines this need:

“….I feel like that we should guide them more…. giving them more guidance in choosing appropriate tasks they want to learn, because sometimes the tasks they choose might be too demanding for them to learn in a limited period time.”

Based on the analysis of the observations and interviews, several suggestions can be provided for integrating instructional scaffolding in the activities, taking into consideration novice learning, and the nature of unstructured problem solving activities. The first two elements relate to developing pedagogical practices in the activities: we recommend that teachers consider cognitive and affective processes of learning as a base for activity design and provide instructional scaffolding to improve opportunities for cognitively effortful and affectively meaningful learning. The next two elements suggest designing the activities in collaboration to enhance the application of digital fabrication to formal education, recommending that we familiarize teachers with Fab Labs and digital fabrication activities and increase collaboration between Fab Lab facilitators and school teachers.

Discussion—How to Design Cognitively Effortful and Affectively Meaningful Learning

Case studies of SNS, games for learning, makers education, and digital fabrication showed different ways of organizing digital education and illustrated in particular how different types of pedagogical design and digital tools have been used to support cognitively effortful and affectively meaningful learning in groups. In other words, in addition to knowledge co-construction, argumentation, and problem solving, opportunities for positive affective learning processes were provided, such as experiencing and expressing emotions in learning.

The first example, SNS, presented a learning environment that is familiar for students as an everyday communication tool. It provided an interaction arena to discuss and debate the course topics with the support of a micro-script ( Noroozi et al., 2012 ). In terms of the cognitive and affective potential of SNS, it can be concluded that structured roles functioned as a support for affective interactions by managing the discourse, inducing and resolving conflicts, and assisting in creating equal participation and feelings of belonging between students ( Isohätälä et al., 2017 ). However, as this case study was tightly pre-structured with a specific micro-script, the following examples presented open-ended collaborative problem-solving spaces. The second case study, the Minecraft game environment, showed how a commercial game was further designed and implemented in a primary school after school club. This was an example of a constructivist game approach where learners played but also modified their own games ( Kafai and Burke, 2015 ). This study showed how game experience prompted students' knowledge acquisition as well as supported students' learning skills in terms of programming and collaboration. Furthermore, the study also indicated that the experience was highly emotionally engaging for the students, based on the students' descriptions of their emotional experiences of playing the game and the experiences they had when the game was over.

Minecraft is a block based world of “digital making”; digital fabrication and making enables a more thorough design experience to plan and fabricate students' own artifacts in the situated (unstructured and open-ended) problem solving contexts ( Halverson and Sheridan, 2014 ; Martin, 2015 ; Taylor, 2016 ). Two different examples that were selected to illustrate maker education and digital fabrication showed the making activities in practice. The example from an early education context showed young children making in several contexts, including outdoor, and indoor locations ( Siklander et al., 2019 ). These activities were observed to contribute to affectivity by allowing children to experience several different types of emotions while learning, such as curiosity, joy, and excitement, but also negative feelings such as impatience, frustration, and disappointment ( Hyvönen and Kangas, 2007 ). These emotional expressions were particularly visible in their collaborative situations. The last case example turned the focus toward the teachers' and facilitators' point of view, investigating how they see making activities and how they understand what kind of support students need from them during these activities. This study, through the design principles of the Fab Lab activities, characterized the important factors that help teachers and facilitators to engage and support students' learning, such as implementing complex tasks, using digital tools, highlighting students' own roles and responsibilities, providing opportunities for reflection, encouraging trial and error, and providing flexibility in the timeframe ( Blikstein, 2013 ; Georgiev et al., 2017 ; Hira and Hynes, 2018 ; Iwata et al., 2019 ). In addition to these principles, this study pointed out that adequate scaffolding is needed to improve opportunities for cognitively effortful and affectively meaningful learning. This is especially important in the situations where maker activities and digital fabrication procedures are introduced to novice makers, since they need to be familiarized with making culture as well as possibilities and tools for making ( Gerjets and Hesse, 2004 ; Blikstein, 2013 ; Chu et al., 2017 ). Fab Lab and maker education differ in the use of social networking tools and games for learning, because digital tools are part of the making process and the learning environment is situated in the physical fabrication laboratory instead of online context ( Kim and Reeves, 2007 ; Qian and Clark, 2016 ).

In general, SNS, digital gaming, and maker education have become increasingly interesting as a learning context in a modern education, mixing technological and creative skills, exploration and discovery, problem-solving and playfulness, as well as formal and informal education ( Connolly et al., 2012 ; Davies and West, 2014 ; Georgiev et al., 2017 ). These types of learning opportunities have the potential to impact current and future educational practices and pedagogy. However, when critically evaluating these learning contexts' opportunities for cognitive and affective learning, it can be noted that the implementation of digital tools and environments alone is not enough ( Gerjets and Hesse, 2004 ). Therefore, planning and facilitating learning activities in digital education requires knowledge of both technology and pedagogy ( Laru et al., 2015 ; Häkkinen et al., 2017 ; Valtonen et al., 2019 ). For example, when designing learning with digital tools, it is important that technologies are embedded into the environment and that their use is designed prior the activities but also facilitated during the learning activities ( Kirschner et al., 2006 ; Dillenbourg, 2013 ). This is the case especially in the maker education context where tools and devices for various kinds of fabrication need to be provided for the use of students with heterogeneous skills, knowledge, and aims ( Blikstein, 2013 ; Chan and Blikstein, 2018 ).

In addition to pre-structured and facilitated learning activities, more spontaneous collaborative activities are recommended. This means that students should be provided opportunities to engage in learning activities which places students' needs, interests, and experiences as the starting point for their explorations. This type of learner-centered approach creates a learning environment that is built around creativity and allows personal emotional experiences, such as fun and enjoyment ( Hyvönen and Kangas, 2007 ; Hyvönen, 2011 ; Hyvönen et al., 2014 ). A sound learning environment also guides and supports students' interest and promotes their active involvement in learning ( Baker, 2015 ; Järvelä et al., 2016 ; Hadwin et al., 2018 ). In order to support learning activities in the ways described above, pedagogically sound practices will need to be established, and teachers' professional development will need to focus more on using technology to improve learning—not just on changing teachers' attitudes and abilities in more general ways ( Davies and West, 2014 ). To conclude, we agree with Lowyck ( 2014 , p. 15), who argues that “both learning theories and technology are empty concepts, when not connected to actors such as instructional designers, teachers and learners.” He continues with the image of teachers and learners as co-designers, which is well-aligned with the case studies presented in this paper, by claiming that “…they are co-designer of learning processes, which affect knowledge-construction, and management as well as products that result from collaboration in distributed knowledge environments.” Finally, this paper reinforces the idea suggested by Roschelle (2003) that we should focus on rich pedagogical practices and simple digital tools. In the context of the four case studies described in this paper, we can summarize that applying digital tools for education is meaningful when the aim is to provide opportunities for interactions and sharing ideas and thus increase students' opportunities to turn an active mind to multiple contexts.

This paper introduced studies that implemented the exploratory case approach and thus it can be criticized due to the lack of generalizability of the results. As case descriptions afford details and context specific illustrations, the possibility to draw general conclusions is limited ( Stake, 1995 ; Yin, 2013 ). In these case studies a various different types of methods were used. For example, discussion notes from Facebook group discussions were analyzed, interviews after the each face to face meeting during the Minecraft experiment were conducted, and photo elicitation interviews as a method in a Fab Lab working was used as well as observations and teacher and student interviews were done during a second Fab lab experiment. All these case studies and related data collections illustrate participants' experiences during the digital learning. As research of affective learning in digital education emerges, a key direction for future studies is to explore how tools and technologies support affective learning and interaction, but also how different types of pedagogical designs can scaffold affective learning ( Näykki et al., 2017a ). Design studies could explore and develop tools and design principles to support the use of social media tools in learning, the design and use of games for learning, and the involvement of makers and digital fabrication activities in educational settings. The current study provides interesting research questions based on our observations of the case studies to be explored in the future studies. For example, it can be explored how to design tools to support affective learning in gaming or making contexts where learning designs are not usually the main focus of the activity. The contexts of the cases were unstructured or open problem spaces, although special pedagogical designs were implemented. However, much remains to be understood regarding the types and configurations of technological and pedagogical support that best promote cognitive and affective processes of collaborative learning.

The results obtained from these case studies are applicable to formal education, such as early childhood education, primary school education, teacher education, and in-service training, but also to informal learning contexts, such as game designing and Fab Lab facilitation. Engagement in creative making activities, productive group work, and seamless use of technology are essential twenty-first-century skills needed in all fields of work and in life in general. Teachers at all educational levels have an especially crucial role in developing these skills in their students, and therefore future teachers have to be offered opportunities to experience and learn within various collaborative environments.

Data Availability Statement

The datasets generated for this study will not be made publicly available Studies involving human subjects.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Additional Requirements

Written informed consent was obtained from all adult participants and the parents of non-adult participants for the purposes of research participation. The raw data supporting the conclusions of this manuscript can be made available by the authors, from request, to any qualified researcher.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study was supported by the Academy of Finland (316129) and Nordplus Horizontal (NPHZ-2018/10123).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: affective learning, collaborative learning, digital education, digital fabrication, maker education, social networking systems

Citation: Näykki P, Laru J, Vuopala E, Siklander P and Järvelä S (2019) Affective Learning in Digital Education—Case Studies of Social Networking Systems, Games for Learning, and Digital Fabrication. Front. Educ. 4:128. doi: 10.3389/feduc.2019.00128

Received: 03 June 2019; Accepted: 16 October 2019; Published: 01 November 2019.

Reviewed by:

Copyright © 2019 Näykki, Laru, Vuopala, Siklander and Järvelä. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Piia Näykki, piia.naykki@oulu.fi

This article is part of the Research Topic

Affective Learning in Digital Education

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Please note you do not have access to teaching notes, how do social networks influence learning outcomes a case study in an industrial setting.

Interactive Technology and Smart Education

ISSN : 1741-5659

Article publication date: 8 June 2012

The purpose of this research is to shed light on the impact of implicit social networks to the learning outcome of e‐learning participants in an industrial setting.

Design/methodology/approach

The paper presents a theoretical framework that allows the authors to measure correlation coefficients between the different affiliations that exist in an organization and the final learning outcome. The correlation between learning outcome and the communication intensity in the implicit social network of the e‐learning participants is also observed. For the quantification of the communication intensity and affiliation network position of e‐learning participants, the methods from the graph theory are applied.

The values of the correlation coefficients between communication intensity and learning outcome show the significance which motivates the authors for further research on engineering of the social networks in the e‐learning environment.

Research limitations/implications

This case study is performed in an industrial setting.

Practical implications

The results of this case study influence the further development of the e‐learning system that has been used in the experimental setup in this paper, especially the user management module. The algorithm for matching the trainees with tutors is in development.

Originality/value

The impact analysis of the influence of the social network position of the learner in e‐learning environment by comparing the test results before taking the e‐learning course and after taking the course (learning outcome) is provided by measurements of the correlations between the social network position and communication intensity of the learner with the learning outcome.

• Implicit social networks
• Learning outcome
• Social networks
• Computer based learning
• Learning methods

Maglajlic, S. and Helic, D. (2012), "How do social networks influence learning outcomes? A case study in an industrial setting", Interactive Technology and Smart Education , Vol. 9 No. 2, pp. 74-88. https://doi.org/10.1108/17415651211242224

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Copyright © 2012, Emerald Group Publishing Limited

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• Published: 22 July 2022

Nursing students’ use of social media in their learning: a case study of a Canadian School of Nursing

• Catherine M. Giroux   ORCID: orcid.org/0000-0003-1352-8501 1 &
• Katherine A. Moreau   ORCID: orcid.org/0000-0002-5955-1689 2  

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Social media has diverse applications for nursing education. Current literature focuses on how nursing faculty use social media in their courses and teaching; less is known about how and why nursing students use social media in support of their learning.

The purpose of this study was to explore how nursing students use social media in their learning formally and informally.

This exploratory qualitative case study of a Canadian School of Nursing reports on the findings of interviews ( n  = 9) with nursing students to explore how they use social media in their learning. Data were analyzed using a combined deductive and inductive coding approach, using three cycles of coding to facilitate category identification.

Results and conclusions

The findings demonstrate that participants use social media for formal and informal learning and specifically, as a third space to support their learning outside of formal institutional structures. Social media plays a role in the learning activities of nursing students studying both face-to-face and by distance. Accordingly, social media use has implications for learning theory and course design, particularly regarding creating space for student learning communities.

Peer Review reports

Social media are online platforms that allow users to connect with other users, curate lists of connections, and interact with each other within the same online platform [ 1 ]. They have applications for both formal and informal learning in health professions education (HPE). Formal learning refers to planned educational experiences, such as courses or assignments [ 2 ] whereas informal learning refers to what is learned through extracurricular activities [ 3 , 4 ]. With social media, formal learning may include such activities as using YouTube videos in class, while informal learning may involve students scrolling through Twitter and finding relevant learning content on their leisure time. Within the HPE literature, social media have been shown to facilitate electronic communication, networking, and real-time collaboration [ 5 , 6 ]. They have also assumed key educational and communicative roles during the COVID-19 pandemic [ 7 , 8 , 9 ]. Furthermore, they continue to allow individuals to engage in independent, informal learning on their own terms and in places of formal education, work, or broader social circles [ 10 ]. Several studies demonstrated how social media can be used to facilitate clinical and professional performance tasks, question-and-answer sessions, and the exploration of complex topics collaboratively; social media can also provide professional learning opportunities and facilitate networking with international practitioners [ 11 , 12 , 13 , 14 ]. Moreover, instructors have used Twitter to provide students with formative feedback in assessment, stimulate reflection and sharing, share daily learning goals, hold journal clubs, notify students of recent topical publications, and orient learners to clinical sites and educational rotations [ 15 , 16 , 17 ]. The literature suggests that the connections that students make using social media can translate to opportunities for mentorship and scholarship [ 18 ]. Moreover, social media may also engage geographically dispersed individuals to create or share content, collaborate in groups, and ultimately form a virtual community [ 19 , 20 ].

Within the nursing education literature, social media is well described as a tool selected by faculty for diverse formal teaching and learning purposes. For instance, several studies described using blogging to facilitate reflections as a teaching strategy for topics such as cultural competence, empathy, the therapeutic relationship, transitions to practice, and self-care [ 21 , 22 , 23 , 24 , 25 ]. The feedback system of the blogging interface provided students with opportunities to practice their reflection and problem-solving skills [ 26 , 27 ]. Some studies used social media to simulate patient encounters or transition experiences for nurses [ 25 , 28 , 29 ]. For example, Thomas et al. used a blog to simulate a new nurse who had just transitioned to practice; the blog was written from the new nurse’s perspective to help final year nursing students consider issues of delegating and supervising, adapting to change, risk and quality management, and legal and ethical issues as they prepared to transition to practice [ 25 ]. Students had to read the blog and post responses. Other studies focused on using Facebook or YouTube as collaborative and interactive tools to help nursing students prepare for examinations like the National Council Licensure Examination (NCLEX) [ 30 , 31 , 32 ]. Still, issues of professionalism arose in the nursing education literature, with some studies noting concerns about students’ online behaviour and potential implications for their reputations and licensure [ 5 , 33 ]. A 2021 narrative review found that learning about digital professionalism concepts as they relate to social media influenced how students behaved online [ 34 ]. Despite these potential professionalism implications, social media appears to be an effective tool to support formal learning in nursing education. A 2018 systematic review explored the effectiveness of using social media in nursing and midwifery education [ 35 ]. The authors found that the collaborative, interactive, and semi-synchronous nature of social media platforms may support knowledge and skill acquisition in nursing students.

Much of the extant undergraduate nursing education literature explores how social media is used in formal learning, specifically from the perspectives of the faculty who select the platforms to suit specific assignments or learning goals. Studies that focus on undergraduate students’ use of social media tended to explore specific platforms used and data analytics (i.e., hashtags used, number of views or shares). Less is known about how and why undergraduate nursing students themselves select social media platforms as an adjunct to their formal and informal learning activities. Thus, this exploratory qualitative case study aimed to address how and why undergraduate nursing students use social media to support their learning.

Theoretical considerations

Social learning theories like social constructivism are appropriate for framing studies involving social media because they view learning as an active and collaborative process [ 36 , 37 , 38 ]. Social constructivism is based on three assumptions: (1) meanings are constructed by humans as they engage with the world they are interpreting; (2) humans engage with their world and make sense of it based on their historical and social perspectives; and (3) the basic generation of meaning is social, arising from the interaction with a human community [ 36 ]. Social constructivism claims knowledge is acquired when subjective meanings are created in interaction with others, drawing on material from previous experiences to guide learning [ 36 , 37 , 38 ]. This study was informed by social constructivism, which influenced our research questions, data collection instruments, and approaches to data analysis.

Research design

The objective of this study was to explore how students at one Canadian School of Nursing used social media to support their learning. We addressed this objective through an exploratory qualitative single case study. Yin [ 39 ] describes a case study as an empirical inquiry that investigates a contemporary phenomenon in depth within its real-world context even when the boundaries between the context and phenomenon may not be evident. Case studies comprise an all-encompassing method, which influences the logic of design, data collection techniques, and approaches to data analyses. Case study research is particularly useful for answering how and why questions; single case studies are appropriate for cases that are critical, unusual, revelatory, and longitudinal [ 39 ]. Our study site represented a critical case since the variety of program delivery methods and modalities were critically aligned with social constructivism. The study site also represented an unusual case, with four distinct program options – including a distance program – for students to achieve a Bachelor of Science in Nursing (BScN) degree. This was a unique program in Canada at the time of the study. The study site did not have any social media policies published to their public-facing website during the time of the study, nor did they have any public-facing references to using social media formally in their programs published on their website.

Case study site and participants

This study took place at a small, relatively northern, Canadian university with a student population of approximately 5,090 students [ 40 ]. The School of Nursing, which includes 1191 students, offers four distinct, English-language, options for students to complete their BScN degree. These options include: 1) a standard four-year direct-entry nursing program; 2) an onsite Registered Practical Nurse (RPN) to BScN bridging program for students who previously obtained an RPN diploma and who are looking to subsequently obtain their BScN degree; 3) a part-time blended learning RPN to BScN bridging program for students currently working as RPNs who are looking to obtain their BScN; and 4) a second entry accelerated program for students who previously obtained an undergraduate degree. Only two of the nursing programs occur at the case study site itself. The second entry program is held in a large city to the south of the case study site. Additionally, students who partake in the RPN to BScN bridging program through blended learning live geographically dispersed throughout the province in which the case study site is located. Given the different program options, the participants in this study consisted of a mixture of face-to-face students and distance students. Additionally, due to the nature of the program options, some participants had pursued their nursing program as their first degree while others were already working as RPNs and had returned to school to obtain their BScN degree.

Participant recruitment and data collection

Participants were purposively recruited from a previous study, which consisted of a digital artifact collection that explored what content nursing students shared to their Twitter, Facebook, and Instagram accounts related to learning [ 41 ]. The twenty-four nursing students who participated in our previous study were contacted by email and invited to participate in this qualitative case study exploring how and why nursing students use social media to support their learning. These students were identified as potential participants because they had confirmed using social media for learning and thus, would be information-rich interviewees for the present study. All potential participants were provided with a Participant Information Letter and Informed Consent form. The data for this study were collected using semi-structured interviews. All interviews were conducted virtually via Zoom in the Fall of 2019, using a semi-structured interview guide that had been developed based on the research questions, our theoretical framework, and the literature (refer to Additional file 1 : Appendix). Prior to using the interview guide, it was piloted with two registered nurses. This pilot involved conducting two mock interviews and debriefing the interview guide with the participants to discuss the feasibility and appropriateness of the interview questions. The average interview length was 32 min, with the shortest being 21 min and the longest being 44 min long. We piloted the interview guide with two registered nurses prior to commencing the study. Each interview was audio-recorded and transcribed verbatim. Interview participation was incentivized with a $20 gift card to a local coffee chain.

Data analysis

We took a combined deductive and inductive approach to coding and analyzing the interview transcripts. We sought to achieve theoretical sufficiency, which is the stage at which codes and categories manage new data without requiring further modification [ 42 ]. To do this, we conducted three cycles of coding in MAXQDA (v.18.2). In the first cycle, a preliminary codebook − which was informed by our research question, theoretical framework, and the literature − facilitated descriptive and process coding [ 43 ]. In the second cycle of coding, we each independently inductively coded the data using both process coding and in vivo coding (i.e., using the participants’ own words) and compared and discussed our coding. In the third cycle of coding, we grouped these summaries into categories, themes, or constructs [ 43 ]. A combination of matrices and networks visually displayed the data and facilitated category identification [ 43 , 44 ].

Reflexivity and trustworthiness

Neither author is a Registered Nurse nor is affiliated with the case study site. Both authors have expertise in conducting educational research within the health professions and were involved in the study conceptualization, data collection, and analysis. We also took steps to ensure that our analyses were credible, dependable, confirmable, and transferable [ 45 , 46 ]. To establish credibility, we engaged in member-checking, wherein we provided the participants a copy of their interview transcripts so that they could ensure that their statements were accurately represented during transcription [ 45 , 47 ]. We also engaged in peer debriefing. In terms of dependability, each of us inductively coded the data, compared our coding, and discussed and resolved any inconsistencies. In addition, we used audit trails as a strategy to ensure confirmability. These audit trails documented each of our decisions made during the research process and would allow an independent auditor to follow our steps and decisions to establish the same conclusions about the data. Lastly, through purposeful sampling and information-rich interviewees, we were able to obtain thick descriptions of how and why the students use social media to support their learning. We also included detailed descriptions of our research processes. This level of description allows others to judge the contextual similarity and transferability of our study findings.

Ethical considerations

The interviews received formal institutional ethical approval (S-08–18-921) and approval from the study site (101916) in August 2019. We reviewed the informed consent form with each participant prior to commencing the interview and addressed any questions that they had. All participants verbally consented to participate in an interview and participants’ consent was recorded using Zoom video conference software, in accordance with our research ethics board approval.

Nine nursing students ( n  = 9) participated in the individual interviews. All participants were female and ranged in age from 18 to 49. Five participants attended classes online in a blended program format that occurred by distance and four participants attended classes face-to-face. The findings demonstrate that participants used social media in numerous ways for both formal and informal learning purposes. Table 1 provides a thematic overview of how the nursing student participants use social media in support of their learning.

Formal learning

Participants reported using social media for a variety of purposes pertaining to formal learning. Table 2 provides exemplary participant quotes outlining their experiences using social media for formal learning purposes.

Sharing and clarifying course content

Several participants reported using social media to share content related to their courses and to clarify course content. Participant 7 explained how “when it comes to having, like, a large quantity of information, I think Facebook’s a better platform for that. Um, you’re able to share different links, you’re able to share pictures, videos, news articles, almost anything, it seems now”. Two participants (Participants 05 and 07) shared contrasting experiences with using social media formally in their distance classes to clarify course concepts. In this instance, a professor had shared YouTube videos in the course. While Participant 7 appreciated the inclusion of videos, Participant 5 found this approach to be lazy, especially since the professor did not create the videos but rather included videos that, according to Participant 5, students would likely search for on their own to assist their learning.

Supplementing university services

Eight participants indicated that Facebook was a good platform to supplement or highlight existing university services. Participant 5 explained how, as a distance student, they used Facebook to learn about the services available to students, like the university’s tutoring service, which Participant 5 found helpful for statistics. Participant 6 described how they used Facebook specifically for sharing course resources, since that platform might be easier at times than the typical learning management system.

Assignments and exams

The participants described using social media as a mechanism to complete their course assignments and to study for course exams and the National Council Licensure Examination (NCLEX). Social media appeared to be involved in the process of completing assignments; it also appeared to be the product of some assignments. Participant 5 described how “any group projects that we have to do would, which in an online program seems a little silly to me to do group projects but, um, we’d have to find a way to collaborate and it was often over Facebook or that sort of thing”. Participant 1 described creating a social media campaign for their community health class to help parents access vision care for their school-age children. Participant 3 shared how they found posts about how to pass the NCLEX the first time shared to social media and Participant 2 explained how they use social media to review for their course exams by dividing course content up amongst a small group of students and sharing review notes and summaries online. Participant 2 also described using social media platforms like Reddit to understand the patient experience based on what patients choose to share to these sites.

Informal learning

Participants indicated that they used social media for diverse informal learning purposes. Table 3 provides an overview of participants’ experiences using social media for informal learning.

Creating community

By far, students shared the value of social media for connecting with peers and the nursing community most frequently. Four participants spoke about how social media promotes connection between distance students. Participant 3 shared how social media “gives you that camaraderie that you’re missing in a classroom environment”. Four out of the five students who identified as an online student cited Facebook groups as being an important mechanism for connecting with their classmates who were spread throughout the province. One participant explained how “there is a group online, uh, [School Name] distance ed students so I use that quite a bit, um, just to get information on classes, um, what to expect from different professors, etc.” Five participants shared how social media helped them combat isolation in their learning. Participant 2 emphasized the importance of social media for connecting distance students, which was important since they did not have the same opportunities to meet their classmates face-to-face. Participant 1 described how participating in Facebook groups helped enhance both the academic and social aspects of their face-to-face learning experience. Participant 4 explained how “we find it’s been really useful, or even like finding little things, like finding rides to clinical and stuff like that. Like obviously not all of us can afford vehicles and stuff like that so just by helping each other out”. In fact, every participant who identified as a face-to-face student ( n  = 4) spoke about the importance of Facebook groups to their learning experience since they contributed to building community and sharing resources.

Similarly, six participants shared how social media connected them with the broader nursing community, outside of their programs and university. Participant 6 described how social media could connect people across the country with experts in the field and the resources they have created. Participant 9 explained how social media could be used to “take my learning outside of the avenues that can be addressed and presented within a program or any program, really. So, it allows you to kind of step outside of that, see what’s happening with other people, how they’re learning…” Participant 1 described how social media allows them to connect with the nursing community on both social and academic levels through sharing memes and experiences on platforms like Instagram and Facebook. Participant 3 shared how social media “probably gives a good, like, um, a good alternative perspective on things, other than the teacher’s”.

‘Behind the scenes’ knowledge sharing

While the participants often spoke about content that was publicly available to them on social media, they also shared how they used social media for informal learning purposes in private or ‘behind the scenes’ ways. Five nursing students reported using social media to buy, sell, and share PDF versions of textbooks. Participant 5 shared how “people share PDFs of textbooks and all that sort of stuff, so it’s definitely saved me several hundred dollars”. Two participants expressed how they prefer social media to textbooks. Participant 9 described how their professors are “not the biggest towards textbooks because they said that the second they are printed they are out of date because of how fast information is changing within healthcare”. In this sense, Participant 9 found social media to be a helpful way to stay up to date with information that textbooks did not provide.

Similarly, three participants described using social media to discuss which professors were the best for each class. Participant 2 explained how “we often talk about which professors are the best for specific courses and so those classes tend to fill up really fast”. Participant 5 described how they use social media to ask questions about the university, share their perceptions of certain professors, and discuss which classes should or should not be taken at the same time. While eight of the nine interview participants actively participated in social media groups, three participants shared that the absence of faculty members in the social media groups could be problematic. Participant 2 suggested using more of the collaborative tools available on the university’s learning management system to eliminate some of the need for the social media groups and better include the faculty members. Participant 5 also found the absence of faculty members in the social media groups to be a problem and recommended involving faculty members in the private groups to correct misinformation and answer questions.

Scaffolding knowledge

In addition to sharing resources, three students indicated that the Facebook groups were essential for giving and receiving support throughout their nursing programs. Similarly, five nursing students shared how they use social media to review their clinical skills. Three participants used social media to review IV insertion. Participant 7 described how “I use Instagram, I follow someone, she, her, her tag is IV Queen or something like that, but she gives a lot of intravenous tips on how to insert IVs and how to care for them”. Participant 3 also described using YouTube videos to review IV compatibility. Participant 1 shared how they used YouTube to practice for their IV therapy lab. Participant 1 also described how “we have used some YouTube videos and tutorials and stuff in our labs where we’re able to view, like, for example just last week we were learning about central lines, um, so we looked at a video about how to do the dressing change for a central line”. Participant 1 also described how they use YouTube to learn about skills like ambulating patients prior to starting their surgical rotation so that they would understand what they were about to do on the rotation.

Why use social media

The study participants presented several reasons to use social media in support of their formal and informal learning activities; similarly, they also presented several reasons to be cautious of using social media for these purposes. Table 4 presents an overview of exemplary participant quotations presented thematically.

Credibility and relevance of sources

Seven participants discussed the credibility and relevance of the sources they found on social media. Participant 7 indicated that they find their friends and followers on social media do not tend to share a lot of content that “I don’t consider real, like the fake news, but it’s a lot of more credible sources, like major journal articles and stuff like that”. Participant 4 expressed that students are taking a risk in depending on social media rather than on their books and their notes. Other students, like Participant 6, emphasized the importance of developing critical thinking skills and being able to filter social media posts so that they could appropriately determine which sources were accurate or credible. Participant 8 indicated that relying on social media links provided by course professors was helpful since “you know if the instructors are posting those videos, then you know that they’re credible sources.”

Professors and professionalism

All nine nursing students shared how their professors, programs, and the importance of professionalism influenced their use of social media. Four participants shared that, perhaps with the exception of YouTube videos, their professors did not use social media in their teaching and discouraged its use by nursing students. Participant 6 explained that “social media is kind of shunned a lot in nursing because of that whole idea of don’t post anything, don’t share your clinical experiences and don’t, you know, breach privacy.” In some instances, participants reported that their professors did not use social media in their teaching but encouraged students to use it to complete course assignments, like learning portfolios. Participant 4 shared that “[the professors] really like the idea of us working together on things and utilizing each other to keep on track”, especially as it related to support during clinical placements.” Other participants described their professors incorporating podcasts, videos, and Reddit into their courses, which encouraged their use of social media for learning. Still, several participants expressed concerns related to professionalism on social media. Participant 3 explained how “I definitely avoid posting about like, things that involve substance use. I feel like there’s added pressure on people in certain, in various professions like healthcare and police that you should avoid because you’re supposed to uphold a certain image of the profession.”

Convenience and accessibility

Several participants discussed the convenience of social media. Two participants shared how it was easier for communication purposes than other methods (i.e., emails, calls, texts). Other participants described how social media provided a central repository for resources that could be easily accessed by classmates. However, Participants 3 and 5 highlighted some challenges to accessibility because of using social media for learning, notably poor internet connection and lack of transcriptions or alternative formats.

Engagement and distraction

Four participants shared how they found social media to be an engaging platform for learning in their nursing education. Participant 4 explained how social media helps highlight major class concepts in a variety of formats, which can be helpful for different learners. Several participants spoke about growing up with social media and how their previous experiences motivated them to use it as a tool to support their nursing education. Participant 6 explained how “I kind of grew up with technology and grew up with social media that I just know how to use it and know how to access it and don’t have a problem filtering out what I don’t need.” Despite how participants felt about social media’s potential for engagement, they also found it potentially distracting. This was a common theme amongst both face-to-face and distance students. Participant 2 described ending up in a “Facebook vortex, where I end up being on it for 2 h, not necessarily on that [program specific] group.”

The nursing student participants described multiple ways of using social media to support their learning. None of the students in this study described using social media for the same creative formal experiences as those published by Thomas et al. [ 25 ] wherein a course instructor developed a simulated student on Facebook for nursing students to interact with online. However, a couple of students outlined their experiences being required to use sites like Reddit to learn about the patient experience. Additionally, some participants described how they used social media to develop patient-oriented health advocacy campaigns for healthcare organizations, effectively demonstrating how social media is being used in their formal nursing education. The ways in which the nursing students use social media to support their formal learning demonstrate social media’s collaborative capacity for knowledge and information exchange for both on-campus and distance students [ 6 , 48 , 49 ]. The study participants used social media creatively to support their formal education; for instance, participants referenced program-specific Facebook groups where they could collectively decide on questions that they needed to ask their professors in class. This finding is consistent with that of Junco et al. [ 50 ], where they found social media to be a low-stress method for students to ask questions of their peers and educators.

Informally, participants indicated using social media to refresh their clinical skills before applying them in lab settings or during clinical rotations. While the findings of this study do not directly touch on the use of social media at the point-of-care, studies like that conducted by Hay et al. [ 51 ] demonstrate social media’s potential utility for enhanced clinical learning and patient safety. In this study, two participants described how they use social media, specifically YouTube videos, to help with patient education at the bedside. Moreover, the participants indicated that they took a cautious approach to using social media in their formal and informal learning out of concern for professionalism implications. Several students indicated that they had been warned about the repercussions of unprofessional online behaviour and had adjusted their behaviour accordingly. This finding is similar to that of a previous conducted narrative review by O’Connor et al. [ 34 ] that found that students were likely to change what content they shared using social media after learning about issues of professionalism.

Importantly, the participants in this study appeared to use social media as a third space. Aaen and Dalsgaard [ 52 ] describe the ‘third space’ as being one that emerges in boundaries or overlaps across spheres; they explain that third spaces emerge from a need for discourses that are unavailable or cannot be filled in existing settings. Participants described creating their own Facebook groups for their classes, cohorts, study groups, clinical groups, and programs. The students explained that faculty members were not present in their Facebook groups, although they did sometimes encourage students to join the groups to stay up to date on information. The participants shared that they used the groups to fill gaps in their education. Others described using the Facebook groups to create a sense of community they felt was missing in their distance learning. In fact, this study found that nursing students use social media in their education in several ways that are often hidden or ‘behind the scenes’. Aaen and Dalsgaard [ 52 ] found that Facebook formed a ‘third space’ that combined elements of academic, personal, and social communication that does not typically take place within conventional university structures or spaces. The findings of this study are similar in the sense that the nursing student participants used social media as a mechanism to collaborate, communicate, teach, and learn when traditional university avenues were unavailable to them.

This study has implications for learning theory in connected teaching and learning. Learning theories – and thus, approaches to teaching – have moved from behaviourist to constructivist in the age of technology [ 53 ]. Indeed, social learning theories like connectivism [ 54 ], Communities of Practice [ 55 ], and social constructivism [ 36 ] can reflect the realities of connected teaching and learning because they focus on collective learning and knowing in both physical and digital spaces. In the present study, social constructivism, specifically Vygotsky’s Zone of Proximal Development, is evident in the participants’ use of social media for formal and informal learning purposes. Vygotsky [ 56 ] defines the Zone of Proximal Development as “the distance between the actual development level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with capable peers” (p. 86). The participants in this study described using online social media groups to share information about course requirements, assignment information, and exam tips. Social media also appeared to be a method for students to consolidate, share, and engage in their learning as part of a larger social process. Several participants described experiences of scaffolding learning for their peers either within their own cohort or in cohorts behind them using social media groups. Scaffolding is a key component of Vygotsky’s Zone of Proximal Development and has applications for online course design; technical scaffolding allows learners to experience just-in-time instruction and be provided with resources to solve problems and generate new learning and understanding collaboratively online [ 57 ]. Thus, the online learning environment should provide learners with the resources, tools, and supports they need to build their own knowledge; scaffolding fades as learners develop their own knowledge and expertise [ 53 ].

Implications for nursing education policy and practice

This study demonstrates that nursing students are using social media in their educational practices both formally and informally. This use of social media has implications for teaching and learning in nursing education. Faculty members must consider the purposes for which nursing students are using social media, especially informally. One finding of this study suggested that nursing students turned to social media to fill perceived gaps – both academic and social – in their learning experience. If faculty members and schools of nursing are aware that social media is being used by nursing students for formal and informal teaching and learning purposes, it can be leveraged to achieve specific competencies and learning objectives. Based on this study, we have highlighted recommendations for nursing education policy and practice.

At the policy level, professional and appropriate social media communication could be included as an educational competency in nursing education programs, if not already stated in guiding curriculum frameworks. The purpose of this recommendation is not to discourage social media use but rather to develop competent online communicators who are equipped to use social media for teaching, learning, advocacy, and knowledge translation purposes. At the institution level, increased training for both faculty members and students on digital literacies, identifying credible online sources, and managing misinformation could help ensure faculty and students feel equipped to use digital tools like social media effectively in their teaching and learning. Finally, at the course level, some participants valued using social media to extend their learning while others were more reluctant to use it; thus, approaching the use of social media with flexibility and allowing for choice is essential. Providing optional opportunities to extend learning may help encourage participation on social media and help students discover how social media platforms can be used as learning tools informally within the nursing profession.

Limitations and future directions

This exploratory qualitative case study included individual semi-structured interviews with nursing students from one Canadian School of Nursing. Despite incentivizing interview participation, we were only able to recruit 9 of the 24 possible participants. It is also probable that those who participated were more interested in social media than those who did not participate. The interviews consisted of self-reported data from the perspectives of the participants. Although participants spoke about how their professors used social media in their courses, the professors’ perspectives were not included in this study, leaving a potential imbalance and area for future research. Moreover, our small qualitative sample did not allow for a stratified analysis based on the program delivery method. This type of analysis would be interesting to conduct with a larger, quantitative dataset. Lastly, while the interview guide included questions about the nursing student participants’ experiences using social media, it did not include questions about their cultural backgrounds. In future, it would be interesting to explore how students’ culture backgrounds influence how and why they use of social media.

Conclusions

The nursing students in this study described and demonstrated using social media to support their formal and informal learning. The participants also used social media as a third space, one that is separate from the traditional confines of the university. Within this space, participants merged their personal and academic discussions to collaborate, share resources, mentor one another, and connect with nursing experts and professional institutions. This use of social media has implications for teaching and learning in nursing education, especially regarding learning theory, scaffolding, and online course design.

Availability of data and materials

Due to the qualitative case study nature of this research, the data generated and analyzed during the current study are not publicly available to maintain the anonymity of the study participants. Data are available from the corresponding author on reasonable request.

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C. G. and K. M. were equally responsible for conceptualizing the study, conducting data collection and analysis, writing the main manuscript text and reviewing the manuscript. All the authors read and approved the final manuscript.

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This study received formal ethical approval from the University of Ottawa Social Sciences and Humanities Research Ethics Board (S-08–18-921) and approval from the study site (101916) in August 2019. We reviewed the informed consent form with each participant prior to commencing the interview and addressed any questions that they had. All participants verbally consented to participate in an interview and participants’ consent was recorded using Zoom video conference software, in accordance with our REB approval from the University of Ottawa Social Sciences and Humanities REB (S-08–18-921). We followed Tri-Council ethics guidelines. Our reporting aligns with the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.

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Giroux, C.M., Moreau, K.A. Nursing students’ use of social media in their learning: a case study of a Canadian School of Nursing. BMC Nurs 21 , 195 (2022). https://doi.org/10.1186/s12912-022-00977-0

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Motivating Students to Learn AI Through Social Networking Sites: A Case Study in Hong Kong

• Tsz Kit Ng University of Hong Kong
• Kai Wa Chu University of Hong Kong

In Hong Kong, after-school activities have long been used to foster friendships and to allow students to pursue their interests in an informal setting. This case study reports on a three-phase action research process in which information technology teachers delivered after-school activities focused on artificial intelligence during the COVID-19 transition to remote learning. Using semi-structured interviews, a motivational questionnaire, and lesson observations, this study describes how extracurricular activities were delivered online using social networking sites and how students perceived the new experience. Our results suggest that, in order to deploy meaningful activities via social media, teachers need to build collaborative environments that facilitate social engagement among students. These findings have implications for new practices in social media and other blended technologies, and can help students strike a healthy balance between their academic and non-academic life during this challenging period.

Author Biographies

Tsz kit ng, university of hong kong.

Mr. Davy Ng is the IT Panel Head Convener at Hong Kong Chinese Women’s Club College and a PhD student in the Faculty of Education, the University of Hong Kong. He holds a MEd in Educational Psychology, BS in Computer Science and Postgraduate in IT in Education from the Chinese University of Hong Kong (CUHK). His research interests lie in the areas of STEM Education and technology-enhanced pedagogic innovation. It is informed by recent research on blended learning, motivational practices to learn STEM via flight simulators, and developing computational thinking through digital Making. He is the principal investigator of Jockey Club (JC) Youth Project - FlipMusic.HK and Quality Education Funded (QEF) Project to promote STEAM learning through Aviation. He is currently a Committee Member of Hong Kong FlippEducators, Hong Kong Tertiary Putonghua Recitation Society, Hong Kong Air Cadet Corps (CUHK). He received awards including Deputy Commanding Officer’s Service Award from the HKACC in 2019 and Yunus Social Business Award in 2020.

Kai Wa Chu, University of Hong Kong

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Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research

• Daniel Z. Grunspan
• Benjamin L. Wiggins
• Steven M. Goodreau

Address correspondence to: Daniel Z. Grunspan ( E-mail Address: [email protected] ).

*Department of Anthropology, University of Washington, Seattle, WA 98185

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Department of Biology, University of Washington, Seattle, WA 98185

Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.

INTRODUCTION

Social relationships are a major aspect of the undergraduate experience. While groups on campus exist to facilitate social interactions, the classroom is a principle domain wherein working relationships form between students. These relationships, and the larger networks they create, have significant effects on student behavior. Network analysis can inform our understanding of student network formation in classrooms and the types of impacts these networks have on students. This set of theoretical and methodological approaches can help to answer questions about pedagogy, equity, learning, and educational policy and organization.

Social networks have been successfully used to test and create paradigms in diverse fields. These include, broadly, the social sciences ( Borgatti et al. , 2009 ), human disease ( Morris, 2004 ; Barabási et al. , 2011 ), scientific collaboration ( Newman, 2001 ; West et al. , 2010 ), social contagion ( Christakis and Fowler, 2013 ), and many others. Network analysis entails two broad classes of hypotheses: those that seek to understand what influences the formation of relational ties in a given population (e.g., having the same major, having relational partners in common), and those that consider the influence that the structure of ties has on shaping outcomes, at either the individual level (e.g., grade point average [GPA] or socioeconomic status) or the population level (e.g., graduation rates or retention in science, technology, engineering, and mathematics [STEM] disciplines). A growing volume of research on social influences at the postsecondary level exists, examining outcomes such as overall GPA and academic performance ( Sacerdote, 2001 ; Zimmerman, 2003 ; Hoel et al. , 2005 ; Foster, 2006 ; Stinebrickner and Stinebrickner, 2006 ; Lyle, 2007 ; Carrell et al. , 2008 ; Fletcher and Tienda, 2008 ; Brunello et al. , 2010 ), cheating ( Carrell et al. , 2008 ), drug and alcohol use ( Duncan et al. , 2005 ; DeSimone, 2007 ; Wilson, 2007 ), and job choice ( Marmaros and Sacerdote, 2002 ; De Giorgi et al. , 2009 ). The impacts are often significant, perhaps not surprisingly; this research has many implications, including the importance that randomly determined relationships such as roommate or lab partner can have on undergraduates’ behavioral choices and, consequently, their college experiences.

One key direction for education researchers is to study network formation within classrooms, in order to elucidate how the realized networks affect learning outcomes. Network analysis can give a baseline understanding of classroom network norms and illuminate major aspects of undergraduate learning. Educators interested in changing curriculum, introducing new teaching methods, promoting social equity in student interactions, or fostering connections between classrooms and communities can obtain a more nuanced understanding of the social impacts different pedagogical strategies may have. For example, we know active learning is effective in college classrooms ( Hake, 1998 ; O’Sullivan and Copper, 2003 ; Freeman et al. , 2007 ; Haak et al. , 2011 ), but the full set of causal pathways is unclear. Perhaps one important change introduced by active learning is the facilitation of student networks to be stronger, less centralized, or structured in some other new way to maximize student learning. Social network analysis (SNA) can help us assess these types of hypotheses.

Recent research in physics education has found that a student's position within communication and interaction networks is correlated with his or her performance ( Bruun and Brewe, 2013 ). An informal learning environment was found to be facilitative in mixing physics students of diverse backgrounds ( Fenichel and Schweingruber, 2010 ; Brewe et al. , 2012 ). However, these exciting initial steps into network analysis in STEM education still leave many hypotheses to explore, and SNA provides a diverse array of tools to explore them.

The goal of this paper is to enable and encourage researchers interested in biology education, and education research more generally, to perform analyses that use relational data and consider the importance of learning relationships to undergraduate education. In doing so, we first introduce some of the many basic concepts and terms in SNA. We outline methods and concerns for data collection, including the importance of gaining approval from your local institutional review board (IRB). We briefly discuss a straightforward way to organize data for analysis, before performing a brief analysis of a classroom network along three avenues: descriptive analysis of the network, exploration of network evolution, and analysis of network position as a predictor of individual outcomes. This paper is aimed at serving as an initial primer for education researchers rather than as a research paper or a comprehensive guide. For the latter, see Further Resources , where we provide a list of additional resources.

INTRODUCTION TO THE CASE STUDY

In introducing network analysis, we draw our example from a subset of a 10-wk introductory biology course with 187 students who saw the course to completion as an example. Each student in this course attended either a morning or afternoon 1-h lecture of ∼90 students four times a week and attended one of eight student labs of ∼24 students each, which met once a week for 3 h and 20 min. This course used a heavy regimen of active learning, including a significant amount of guided student–student interaction in both lecture and lab. The total percentage of active-learning activities used in this lecture course was greater than 65% of classroom time, including audience response–device questions. The data we collected included who students studied with for the first three exams, all of their class grades, the lecture and lab sections to which they belonged, and general demographic information from the registrar.

Network Concepts

In this section, we lay out some of the foundations of SNA and introduce concepts and measurements commonly seen in network studies.

Social Network Basics.

SNA aims to understand the determinants, structure, and consequences of relationships between actors. In other words, SNA helps us to understand how relationships form, what kinds of relational structures emerge from the building blocks of individual relationships between pairs of actors, and what, if any, the impacts are of these relationships on actors. Actors , also called nodes , can be individuals, organizations, websites, or any entity that can be connected to other entities. A group of actors and the connections between them make up a network.

The importance of relationships and emergent structures formed by relationships makes SNA different from other research paradigms, which often focus solely on the attributes of actors. For example, traditional analyses may separate students into groups based on their attributes and search for disproportional outcomes based on those attributes. A social network perspective would focus instead on how individuals may have similar network positions due to shared attributes. These similar network positions may present the same social influences on both individuals, and these social influences may be an important part of the causal chain to the shared outcome. In situations in which a presence or absence of social support is suspected to be important to outcomes of interest, such as formal learning within a classroom, the SNA paradigm is appealing.

Network Types.

One way to categorize networks is by the number of types of actors they contain. Networks that consist of only one type of actor (e.g., students) are referred to as unipartite (or sometimes monopartite or one-mode ). While not discussed in detail here, bipartite (or sometimes two-mode ) networks are also possible, linking actors with the groups to which they belong. For example, a bipartite network could link scholars to papers they authored or students to classes they took, differing from a unipartite network, which would link author to author or student to student.

Networks can also be categorized by the nature of the ties they contain. For example, if ties between actors are inherently bidirectional, the network would be referred to as undirected . A network of students studying with one another is an example of an undirected network; if student A studies with student B, then we can be certain that student B also studied with student A, creating an undirected tie. If the relational interest of a network has an associated direction, such as student perceptions of one another, then it is referred to as a directed network; if student A perceives student B as smart, it does not imply that student B perceives student A as smart; without the latter, we would have one directed tie from A to B.

Ties can also be binary or valued . Binary ties represent whether or not a relation exists, while valued ties include additional quantitative information about the relation. For example, a binary network of student study relations would indicate whether or not student A studied with student B, while a valued network would include the number of hours they studied together. Binary networks are simpler to collect and analyze. Valued networks include a trade-off of more information in the data versus increased analytical and methodological complexity. Using the example of a study network, the added complexity of valued networks would allow an investigation regarding a threshold number of study hours necessary for a peer impact on learning gains, while a binary network would treat any amount of study time with a peer equally.

Network Data Collection.

Collecting network data requires deciding on a time frame for the relationships of interest. Real-world networks are rarely static; ties form, break, strengthen and weaken over time. At any given time, however, a network takes on a given cross-sectional realization. Network data collection (and subsequent analyses) can be categorized, then, by whether it considers a static network, a cross-sectional realization of an implicitly dynamic network, or an explicitly dynamic network. The last of these may take the form of multiple cross-sectional snapshots or of some form of continuous data collection. Measuring and analyzing dynamic networks introduces a host of new challenges. Because the set of actors in a classroom population is mostly static for a definite period of time (i.e., a semester or quarter), while the relational ties among them may change over that period, all three options are feasible in this setting. The type of collection should, of course, be driven by the research question at hand. For example, our interest in the evolution of study networks inspired a longitudinal network collection design. Examining the impact of network ties on subsequent classroom performance, on the other hand, could be done with a single network collection.

Beyond considering the time frame of collection, it is also important to consider how to sample from a population. Egocentric studies focus on a sample of individuals (called “egos”) and the local social environment surrounding them without explicitly attempting to “connect the dots” in the network further. Typically, respondents are asked about the number and nature of their relationships and the attributes of their relational partners (called “alters”). In some fields, the term “egocentric data collection” implies that individual identifiers for relational partners are not collected, while in other fields this is not part of the definition. By either definition, egocentric studies tend to be easier to implement than other methods, both in terms of data collection and ethics and human subjects review. Egocentric data are excellent first descriptors of a sample and, in many situations, may be the only form of data available. A wide range of important hypotheses can be tested using egocentric data, although questions about larger network structure cannot. Asking a sample of college freshmen to list friends and provide demographic information about each friend listed would represent egocentric network collection.

At the other end of the spectrum, census networks, sometimes referred to as whole networks, collect data from an entire bounded population of actors, including identifiable information about the respondents’ relational partners. These alters are then identified among the set of respondents, yielding a complete picture of the network. This results in more potential hypotheses to be tested, due to the added ability to look at network structures. In our classroom study, we asked students to list other students in that same classroom with whom they studied; this is an example of a census network whose population is bounded within a single classroom.

High-quality census networks are rare, due to the exhaustive nature of the data collection, as well as the need for bounding a population in a reasonable way. It is worth noting that census networks may lack information on potentially influential relations with actors who are not a part of the population of interest; for example, important interactions between students and teaching assistants will be absent in a census network interested in student–student interactions, as would any students outside the class with whom students in the class studied. In the case of longitudinal studies, an added challenge arises—handling students who withdraw from the class or who join after the first round of data collection has been conducted. Census data collection also presents a nonresponse risk, which may result in a partial network. Nonresponse is more acute in complete network studies than other kinds of data collection because many of the commonly used analytical methods for complete networks consider the entire network structure as an interactive system and assume that it has been completely observed. Educational environments such as classrooms are fairly well bounded and have unique and important cultures between relatively few actors; they are thus prime candidates for census data collection, although the above issues must still be attended to.

Network Level Concepts and Measures.

Network analysis entails numerous concepts and measurements absent in more standard types of data analyses. Perhaps the most basic measurement in network analysis is network density . The density of a network is a measurement of how many links are observed in a whole network divided by the total number of links that could exist if every actor were connected to every other actor. These measurements are frequently small but vary by the type and size of the network. Density measurements are often hard to interpret without comparable data from other similar networks.

Density is a global metric that simply indicates how many ties are present. A long list of network concepts are further concerned with the patterns of who is connected with whom . One pervasive concept in the latter realm is homophily ( McPherson et al. , 2001 ), a propensity for similar actors to be disproportionately connected in a relation of interest. If we are interested in who studies with whom, and males disproportionately studied with other males and females with other females, this would exemplify some level of homophily by gender. Likewise, we could see homophily by ethnicity, GPA, office-hours attendance, or any other characteristic that can be the same or similar between two students. Understanding and researching homophily in classroom and educational networks may be central for several reasons. For example, two reasonable hypotheses are that relationships of social support in classrooms are more likely to be seen between students with similar backgrounds and that having sufficient social support is important for STEM retention. Testing these hypotheses by looking for homophily in networks with relation to STEM retention would provide valuable information regarding the lower STEM retention rates of underrepresented groups. Confirming these hypotheses, then, would inform improved classroom behavioral strategies for educators to emphasize.

Finding a pattern of homophily for certain research questions is interesting on its own. Note, however, that a pattern of homophily can emerge from multiple processes. Two examples of these are social selection and social influence . Social selection occurs when a relationship is more likely to occur due to two actors having the same attributes, while social influence occurs when individuals change their attributes to match those of their relational partners, due to influence from those partners. As an example, we can imagine a hypothetical college class in which a network of study partners reveals that students who received “A's” disproportionately studied with other students receiving “A’s.” If “A”-level students seek out other “A”-level students to study with, this would be social selection; if studying with an A-level student helps raise other students’ grades, this would be social influence. Depending on the goals of a study, disentangling between these two possibilities may or may not be of interest. Doing so is most straightforward when one has longitudinal data, so that event sequences can be determined (e.g., whether student X became an “A” student before or after studying with student Y).

Analyzing ties between two individuals independently, such as in studies of homophily, falls into the category of dyad-level analysis. When one has a census network, however, analysis at higher levels such as triads is possible. Triads have received considerable interest in network theory ( Granovetter, 1973 ; Krackhardt, 1999 ) due to their operational significance. Triads are any set of three nodes and offer interesting structural dynamics, such as one node brokering the formation of a tie between two other nodes, or one node acting as a conduit of information from one node to the other. One version of classifying triads in an undirected network (commonly called the undirected Davis-Leinhardt triad census) is shown in Figure 1 .

Figure 1. Davis and Leinhardt triad classifications for undirected networks.

In a study network, a class exhibiting many complete triads may indicate a strong culture of group study compared with a class that exhibits comparatively few complete triads. One way to examine this would be a triad census —a simple count of how many different triad types exist in a network. Another way to measure this would be to look at transitivity , a value representing the likelihood of student A being tied to C, given that A is tied to B and B is tied to C. Transitivity is a simple, local measure of a more general set of concepts related to clustering or cohesion, which may extend to much larger groups beyond size three.

In directed networks, transitivity can take on a different meaning, pointing to a distinct pair of theoretical concepts. When three actors are linked by a directed chain of the form A→B→C, then there are two types of relationships that can close the triad: either A→C or C→A (or, of course, both). The first option creates a structure called a transitive triad , and the latter a cyclical triad . For many types of relationships (i.e., those involving giving of goods or esteem), a preponderance of transitive triads is considered an indicator of hierarchy (with A always giving and C always receiving), while a preponderance of cyclical triads is an indicator of egalitarianism (with everyone giving and everyone receiving). If asking students about their ideal study partners, the presence of transitive triads would reflect a system wherein students agree on an implicit ranking of best partners, presumably based on levels of knowledge and/or helpfulness. Cyclic triads (as well as other longer cycles) would be more likely to appear if students believed that other factors mattered instead or as well; for instance, that it is most useful to study with someone from a different lab group or with a different learning style so as to maximize the breadth of knowledge.

Actor-Level Variables.

Nodes within a network also have their own set of measurements. These include the exogenously defined attributes with which we are generally familiar (e.g., age, race, major), but they also include measures of position of nodes in the network. Within the latter, a widely considered cluster of interrelated metrics revolves around the concept of centrality . Several ways of measuring centrality have been proposed, including degree ( Nieminen, 1974 ), closeness ( Sabidussi, 1966 ), betweenness ( Freeman, 1977 ), and eigenvector centrality ( Bonacich, 1987 ). Degree centrality represents the total number of connections a node has. In networks in which relations are directional, this includes measures of indegree and outdegree , or the number of edges pointing to or away from an actor, respectively. Degree centrality is often useful for examining the equity or inequity in the number of ties between individuals and can be done by looking at the degree distribution, which shows the distribution of degrees over an entire network. Betweenness centrality focuses on whether actors serve as bridges in the shortest paths between two actors. Actors with high betweenness centrality have a high probability of existing as a link on the shortest path ( geodesic ) between any two actors in a network. If one were to look at an airport network (airports connected by flights), airports serving as main hubs, such as Chicago O’Hare and London Heathrow, would have high betweenness, as they connect many cities with no direct flights between them. Closeness centrality focuses on how close one actor is to other actors on average, measured along geodesics. It is important to keep in mind that closeness centrality is poorly suited for disconnected networks (networks in which many actors have zero ties or groups of actors have no connection to other groups). Eigenvector centrality places importance on being connected to other well-connected individuals; having well-connected neighbors gives a higher eigenvector centrality than having the same number of neighbors who are less well connected. Easily the most famous metric based upon eigenvector centrality is the PageRank algorithm used by Google ( Page et al. , 1999 ). Because the interpretation of what centrality is actually measuring depends on the metric selected and the type of network at hand, careful consideration is advised before selecting one or more types of centrality for one's study.

Network Methods: Data Collection

In this section, we provide guidance for collecting network data from classrooms. Our discussion is based on existing literature as well as personal experience from our previously described network study.

Both relational and nodal attribute data can be collected using surveys. Designing an effective survey is a more challenging task than often anticipated. There are excellent resources available for writing and facilitating survey questions ( Fink, 2003 ; Denzin and Lincoln, 2005 ). This section highlights some of the issues unique to surveys for educational network data.

Survey fatigue, and its resulting problems with data quality ( Porter et al. , 2004 ), can be an issue for any form of survey research; however, for network studies, it can be especially challenging, given that students are reporting not only on themselves but also on each of their relational partners. For our project, we avoided overuse of surveys in several ways. Routine administrative information such as lab section, lecture section, student major, course grades, and exam grades was easily collected from instructor databases. Data about student demographics, educational background, and standardized testing were obtained through a request to our university's registrar's office (with accompanying human subjects approval).

We strongly suggest pilot studies with your survey, as scheduling a single high-value data collection as the first use of a survey instrument can be risky. The delay in waiting for the next term or the next class for a more vetted collection is worthwhile. Data processing time and effort can be greatly reduced by streamlined data collection, and analysis will be strengthened by iterative improvement of survey questions. With adequate design preparation, brief surveys can easily collect relational data. It is important to keep questions clear and compact. Guidance into the form of the data can make data collected from both closed- and open-ended questions much simpler to clear and process ( Wasserman et al. , 1990 ; Scott and Carrington, 2011 ).

Question 11: We are interested in learning how in-class study networks form in large undergraduate classes. Over the next few pages is a class roster with two checkboxes next to each student—one which says “Pre-class friend” and one which says “Strong student”. For each student, evaluate whether they fit the description for each box (immediately below this paragraph), and check the box if they do.

Pre-class friend : A student that you would consider a friend from BEFORE the term of this class. If you have met someone in this class that you would consider a friend now but not before this class, do not list them as a pre-class friend.

Strong student : A student you believe is good at understanding class material.

If you are not exactly sure of a name, mark your best guess. The next question in this survey will allow you to write in a name if you don't see one or aren't sure.

***Please know that your response is completely confidential. All names will be immediately re-coded so we will have no idea who studied with whom. This information will never be used for any class purpose, grading purpose, or anything else before the end of the class. Also, please note that students that you list will not know that you listed them in this survey, and you will not know if anyone listed you.***

The number of possible choices given to subjects is an area of intense interest to survey writers in other fields ( Couper et al. , 2004 ). Limiting respondents to a given number of answers has a variety of purposes; e.g., in egocentric studies in which a respondent will be asked many questions about each partner, it can help to limit respondent fatigue. For census network data, this is not an issue because we will not need to ask students a long list of questions about the attributes of their alters; we will have that information from the alters themselves, who are also students in the class. It can also help avoid a subject with a broad definition of friendship or collaboration from dominating the data set. We chose to avoid limits on numbers of student nominations, which have the potential to induce subjects to enter data to fill up their perceived quota. In our experience, individual student responses are typically few; no student listed so many friends or study partners that it drowned out other signals significantly.

We are interested in how networks form in classes. Please list first and last names if possible. If this is not possible, last initials or any description of that person would be appreciated (ie: “they are in the same lab as me”, “really tall” or “sits in the second row”).

If no one fits one of these descriptions, simply write “none.”

***Your response is completely confidential. All names will be re-coded so we will have no idea who listed whom. This information will never be used for any class purpose, grading purpose, or anything else before the end of the class. Also, please note that students that you list will not know you listed them in this survey, and you will not know if anyone listed you.***

There are no right or wrong answers for this. We will ask you similar questions a few times this term. These data are incredibly valuable, so we truly appreciate your answers!

Please list any people in the class that you know are strong with class material. If you do not list anybody, please type either “No one fits description” OR “I prefer not to answer”. (separate multiple students with a comma, like “Jane Doe, John Doe”) .

Finally, it may be appropriate in smaller classes, communities with less online capability, or in particularly well-funded studies to collect relational data by interviews. This brings along greater privacy concerns but may be necessary for some hypotheses. Open-ended questions allow for greater breadth of data collection but come with intrinsic complexity in processing. For example, a valued network describing the amount of respect that students have for various faculty might be best collected in a private interview. In this format, the interviewer could more thoroughly describe “respect” by using repeated and individualized questioning to ascertain the amount of respect a student has for each faculty member.

Timing of Survey Administration

Timing of survey questions throughout a class is important. For classroom descriptions consisting of a single network, data should be collected at the earliest possible time that all students have had the experiences desired in the research study. This limits the loss of data due to students forgetting particular ties, dropping or switching classes, or failing to complete the assignment as submission rates inevitably drop toward the end of the term. For longitudinal studies involving several collections, relational data can be collected either at regular intervals or around important classroom events. In either case, we strongly suggest implanting relational survey questions in already existing assignments, if permitted, to maximize data collection rates.

For our project, we collected data throughout the 10-wk term of an introductory biology course. We surveyed for student study partnerships after each exam, spread at semiregular intervals throughout the term (weeks 3, 5, 8, and 10). It will come as no surprise to instructors that attempts to administer an additional, nongraded survey gave lower response rates from already overworked and overscheduled undergraduates. Instead, we appended ungraded survey questions to existing graded online assignments. Depending on your research question, it may be appropriate to repeat some collections to allow for redundancy or for longitudinal analyses. Friendships, for example, are subjectively defined and temporal ( Galaskiewicz and Wasserman, 1993 ). In some of our projects, we ask students for friendship relational data at both the beginning and end of the term as an internal measure of this natural volatility.

Given high response rates, anecdotal accounts of student study groupings that corroborated with the relational data, and limited extra work placed on students to provide data, we have a high level of confidence in the efficacy of our data collection methods, and others interested in network research with similar populations may also find these methods effective.

IRB and Consent

Data used solely for curricular improvement and not for generalizable research often do not require consent, but any use of the data for generalizable research does ( Martin and Inwood, 2012 ). Social network data include the unique issue of one individual reporting on others in some form or other, even if it is only on the presence of a shared relationship. They also often describe vulnerable populations; this can be especially true for educational network research, when researchers are often also acting as instructors or supervisors to the student subjects and are thus in a position of authority. This may create the impression in students’ minds that research participation is linked to student assessment. Because of this, early and frequent conversations with your local human subjects division are useful, illuminating, and should take priority ( Oakes, 2002 ).

The nature of network data not only allows subjects to report information on other subjects but may allow recognizability of even anonymized data (called deductive disclosure ), especially in small networks. This makes larger data sets typically safer for subjects. It also means that some network data fields must be stripped of information ( Martin and Inwood, 2012 ). A relatively common example is in networks of mixed ethnicity in which one ethnic group is extremely small. In these cases, ethnicities may need to be identified by random identifiers rather than specific names. In many scenarios, researchers must plan on anonymizing or removing identifiers on data ( Johnson, 2008 ). Your IRB will determine the best fit of plan for any given population of subjects.

Obtaining consent makes networks exciting and problematic at the same time. Complete inclusion of all subjects gives fascinating power to network statistics. Incomplete networks are far less compelling. More so than simpler unstructured data, networks may hinge on a small group of centralized actors in a community. The twin goals of subject protection and data set completion may compete ( Johnson, 2008 ).

In our experience, conversations with IRB advisors led to an understanding of opt-in and opt-out procedures. For example, a standard opt-in procedure would use an individual not involved with the course to talk students through a consent script, answer questions, and retrieve signed consent forms from consenting subjects. An opt-out procedure would provide the same opportunities for student information and questions but ask subjects to opt out by signing a centrally located and easily accessible form kept confidential from researchers until after the research is completed. While the opt-in procedures are more common and foreground subject protection, they tend to omit data with a bias toward underserved and less successful populations. For this reason, we used an opt-out procedure, which commonly leads to higher rates of data return. Balancing research goals and appropriate protection of subject rights and privacy is critical ( Johnson, 2008 ). By minimizing the risk to our subjects via confidential network collection, the use of an opt-out procedure was justified.

Data Management

Matrices are a powerful way to store and represent social network data. Common practice is to use a combination of matrices, one (or more) containing nodal attributes (see Table 1 ) and one (or more) containing relational data. A common form for the latter is called a sociomatrix or adjacency matrix (see Table 2 ); another is as an edgelist , a two-column matrix with each row identifying a pair of nodes in a relationship. For our study, we compiled several sociomatrices taken longitudinally at key points in the class, as well as one matrix with data of interest about our students.

A unipartite sociomatrix will always be square, with as many rows and columns as there are respondents. For undirected networks, the sociomatrix will be symmetric along the main diagonal; for undirected, the upper and lower triangles will instead store different information. Matrices for binary networks will be filled with 1s and 0s, indicating the existence of a tie or not, respectively. In cases of nonbinary ties (e.g., how many hours each student studied together) the numbers within the matrix may exceed one. The matrix storing nodal attribute information need not be square; it will have a row for each respondent and a column for each attribute measured.

It is important to understand the value of keeping rows of attribute data linkable to, and in the same order as, sociomatrices—this will ensure the relational data of a student are paired properly to his or her other data. The linkage can be done through unique names; more typically it will be done using unique study IDs.

The amount of effort and time spent cleaning the data will depend on how the data were collected and the classroom population. For this reason, it is advisable to plan the amount and means of collecting data around your ability to process them. Recently, we collected a large relational data set via open-ended survey online. To process these data into sociomatrices we created a program capable of doing more than 50% of the processing ( Butler, 2013 ), leaving the rest to simple data entry. For data collected using a prepopulated computerized list, it may even be possible for all data processing to be automated.

Data Analysis

Many different questions can be addressed with SNA, and there are nearly as many different SNA tools as there are questions. As an example, we will look at the change in student study networks over the span of two exams from our previously described study. Our main interest in these analyses will be how study networks form in a classroom and the impacts these networks have on students. To generate testable hypotheses, we will first perform exploratory data analysis, taking advantage of sociographs. These informative network visualizations offer an abundance of qualitative information and are a distinguishing feature of SNA. It is important to note that, while SNA lends itself well to exploratory analyses, it is often judicious to have a priori hypotheses before beginning data collection. The exploratory data analysis embedded below is used to provide a more complete tutorial rather than to model how research incorporating relational data must be performed.

Starting Analyses

Most familiar statistical methods require observations to be independent. In SNA, not only are the data dependent among observations, but we are fundamentally interested in that dependence as our core question. For these reasons, the methods must deal with dependence. As a result, analyses may occasionally seem different from familiar methods, while at other times they can seem familiar but have subtle differences with important implications. This point should be kept in mind while reading about or performing any analysis with dependent data.

There are a number of proprietary software packages available for performing SNA, and interested investigators should weigh the pros and cons of each for their own purposes before choosing which to use. We use the statnet suite of packages ( Handcock et al. , 2008 ; Hunter et al. , 2008 ) in R, primarily its constituent packages network and sna ( Butts, 2008 ). R is an open-source statistical and graphical programming language in which many tools for SNA have been, and continue to be, developed. The learning curve is steeper than for most other software packages, but it comes with arguably the most complex statistical capabilities for SNA. Other network analysis packages available in R are RSiena ( Ripley et al. , 2011 ), and igraph ( Csardi and Nepusz, 2006 ). Other software packages commonly used for analysis for academic purposes include UCINet ( Borgatti et al. , 1999 ), Pajek ( Batagelj and Mrvar, 1998 ), NodeXL ( Smith et al. , 2009 ; Hansen et al. , 2010 ), and Gephi ( Bastian et al. , 2009 ).

We include R code for step-by-step instructions for our analysis in the Supplemental Material for those interested in using statnet for analyses. The Supplemental Material also includes instructions for accessing a mock data set to use with the included code, as confidentiality needs and corresponding IRB agreements do not allow us to share the original data.

Exploratory Data Analysis

In performing SNA, visualizing the network is often the first step taken. Using sociographs, with nodal attributes represented by different colors, shapes, and sizes, we will be able to begin qualitatively assessing a priori hypotheses and deriving new hypotheses. We hypothesize that students who are in the same lab are more likely to study together, due to their increased interaction. We also think students with fewer study partners, and thus less group support in the class, are less likely to perform well in the class.

Figure 2 contains two sociographs visualizing the study networks for the first and second exam. Each shape represents a student, and a line between two shapes represents a study relationship. In these graphs, each color represents a different lab section, shape represents gender, and the size of each shape corresponds to how well the student performed in the class.

Figure 2. Sociographs representing study networks for the first and second exam. Male students are represented as triangles and females as diamonds. The color of each node corresponds to the lab section each student was in. Edges (lines) between nodes in the networks represent a study partnership for the first and second exam, respectively.

Figure 3. A parallel coordinate plot tracking changes in number of study partners from the first and second exam. The number of students whose number of study partners changes from exam 1 to exam 2 is denoted by the line widths.

While no statistical significance can be drawn from sociographs, we can qualitatively assess our hypotheses. Judging by the clustering of colors, it seems as though same-lab study partnerships were rarer in the first exam than the second exam, for which several same-color clusters exist. This provides valuable visual evidence, but more rigorous statistical methods are important, particularly if policy depends on results.

There does not seem to be any strong visual evidence for an association between classroom performance and number of study partners. If this were true, we would see isolated nodes (those with zero ties) and nodes with few connections to be smaller on average than well-connected nodes. Visually, it is hard discern whether this is the case, and more rigorous tests can help us test this hypothesis. We first explore structural changes in study networks between the first two exams before statistically testing for an association between test scores and social studying.

Network Changes over Time

We can compare the study networks from the first and second exams using network measures such as density, triad censuses, and transitivity. These measurements allow us to assess whether the number of study partnerships are increasing or decreasing and whether any changes affect larger network structures such as triads.

Examining Table 3 , a few things become clear. First, 34 more study partnerships exist in the second exam compared with the first, a 22.5% increase in network density. This increase in study partnerships does not distinguish between students moving from studying alone to studying with other students and students who have study partners adopting more study partners. One way to gain a better understanding of the increase in overall study partnerships is to look at the degree distribution for the first two exams, seen in Table 4 .

There are fewer students without study partners on the second exam, several students exhibiting extreme sociality in their study habits, and an overall trend toward more students with upwards of five study partners. Unfortunately, the degree distribution does not completely illuminate the social mobility of students between the first and second exam. One way to view general trends is to use a parallel coordinate plot using the degree data from the first and second study networks.

The plot in Figure 3 seems to indicate that the overall increase in study partnerships is not dominated by a few individuals and is instead an outcome of an overall class increase in social study habits. While we see many isolated students studying alone on the first and second exam, we also find many branching off and studying socially in the second exam. At the same rate, many students studied with partners in the first exam and become isolated on the second.

Not only are there more overall connections, but we see higher transitivity and a trend toward complete triads. This increase in both measures indicates how students find their new study partners; they become more likely to study with their study partner's study partner, resulting in more group studying.

Ties as Predictors of Performance

Understanding study group formation and evolution is both interesting and important, but we are not limited to questions focused on network formation. As educators, we are inherently interested in what drives student learning and the kinds of environments that maximize the process. We can start addressing this broad question by integrating student performance data with network data.

As an example, we will test for an association between exam scores and both degree centrality and betweenness centrality. Studying with more students (indicated by degree centrality) and being embedded centrally in the larger classroom study network (indicated by betweenness centrality) may be a better strategy than studying alone or only with socially disconnected students. If we think of each edge in the study network as representing class material being discussed in a bidirectional manner, then more social students may have a leg up on those who are not grappling with class material with peers.

Owing to the dependent nature of centrality measures, testing for an association between network position and exam performance is not completely straightforward. One way around the dependence assumption is to use a permutation correlation test. The general idea is to create a distribution of correlations from our data by randomly sampling values from one variable and matching them to another. In effect, we will assign each student in the study network a randomly selected exam score from the scores in the class 100,000 times. This creates a null distribution of correlation coefficients (ρ) for the correlation between exam score and centrality measure for the set of exam scores found in our data, as seen in Table 5 . We can then test the null hypothesis that ρ = 0 using this created distribution.

a Significance is seen between both types of centrality for the second exam, but not the first.

With a one-tailed test, we see no significant correlation for either centrality measure for the first exam but find a significant correlation between both betweenness centrality and degree centrality and exam performance on the second exam. With our understanding of how students changed their studying patterns between the first and second exam, this finding is rather interesting. Given the opportunity to revise their network positions after some experience in the course, we find a social influence on exam performance.

Because we are unable to control for student effort (a measure notoriously hard to capture), we are unable to discern whether study effort confounds our finding and makes causality vague. Regardless, the association is interesting and exemplifies the sort of direction researchers can take with SNA.

More Complex Models of Network Formation

The methods we present here only scratch the surface of those available and largely focus on fairly descriptive techniques. A variety of approaches exists to explore the structure of networks, to infer the processes generating those structures, and to quantify the relationships among those structures and the flow of entities on them, with a recent trend away from description and toward more inferential models. For instance, past decades saw great interest in specific models for network structure (e.g., the “small-world” model) and their implications ( Watts and Strogatz, 1998 ). A host of methods exist for identifying endogenous clusters in networks (e.g., study groups) that are not reducible to exogenous attributes like major or lab group; these have evolved over the decades from more descriptive approaches to those involving an underlying statistical model ( Hoff et al. , 2002 ). Recently, more general approaches for specifying competing models of network structure within the framework and performing model selection based on maximum likelihood have become feasible. These include actor-oriented models, implemented in the RSiena package ( Snijders, 1996 ), and exponential-family random graph models, implemented in statnet ( Wasserman and Pattison, 1996 ; Hunter et al. , 2008 ). One recent text that covers all of these and more, using examples from both biology and social science and with a statistical orientation, is Statistical Analysis of Network Data by Kolaczyk (2009) .

FUTURE DIRECTIONS

Within education research, we are just beginning to explore the kinds of questions that can benefit from these methods. Correlating student performance (on any number of measures) to network position is one clear area of research possibility. Specific experiments in pedagogical strategies or tactics, beyond having effects on student learning, may be assessable by differential effects on student network formation. For example, three groups of students could be required to perform a classroom task either by working alone, by working in pairs, or by working in larger groups. Differential outcomes might include grade results, future self-efficacy, or understanding of scientific complexity. The outcomes could be correlated with significant differences in the emergent network structures, strength of ties, and number of ties that emerge in a network of studying partnerships. Controlled experimentation with social constraints and network data would provide insight on advantages or disadvantages of intentional social structuring of class work.

Educational networks are not exclusive to students; relational data between teachers, teacher educators, and school administrators may reveal how best teaching practices spread and explain institutional discrepancies in advancing science education.

Beyond correlational studies, major questions of equity and student peer perceptions will be a good fit for directed network analysis. Conceivably, network analysis can be used to describe the structure of seemingly ethereal concepts such as reputation, charisma, and teaching ability through the social assessment of peers and stakeholders. With a better understanding of the formation and importance of classroom networks, instructors may wish to understand how their teaching fosters or hinders these networks, potentially as part of formative assessment. Reducing the achievement gaps along many demographic lines is likely to involve social engineering at some granular level, and the success or failure of interventions represents rich opportunities for network assessment.

In this primer, we have analyzed two study networks from a single classroom. We have discussed collection of both nodal and relational data, and we specifically focused on keeping surveys brief and simple to process. We transitioned these data to a sociomatrix form for use with SNA software in a statistical package. We analyzed and interpreted these data by visualizing network data with sociographs, looking at some basic network measurements, and testing for associations between network position and a nodal attribute. Data were interpreted both as a description of a single network and as a longitudinal time lapse of community change. For this project, data collection required a single field of data from the institution registrar and a single survey question asked longitudinally on just two occasions. With a relatively small investment in data collection we can rigorously assess hypotheses about interactions within our educational environments.

It bears repeating: this primer is intended as a first introduction to the power and complexity of educational research aims that might benefit from SNA. Your specific research question will determine which parts of these methods are most useful, and deeper resources in SNA are widely available.

In short, networks are a relatively simple but powerful way of looking at the small and vital communities in every school and college. Empirical research of undergraduate learning communities is sparse, and instructors are thus limited to anecdotal evidence to inform decisions that may impact student relations. We hope this primer helps to guide educational researchers into a growing field that can help investigate classroom-scale hypotheses, and ultimately inform for better instruction.

FURTHER RESOURCES

For readers whose interest in SNA has been piqued, there are numerous resources to use in learning more. We provide some of our favorites here:

Carolan, Brian V. Social Network Analysis and Education: Theory, Methods & Applications . Los Angeles: Sage, 2013.

Kolaczyk, Eric D. Statistical Analysis of Network Data: Methods and Models . Springer Series in Statistics. New York: Springer, 2009.

Lusher, Dean, Johan Koskinen, and Garry Robbins. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications . Structural Analysis in the Social Sciences 35. Cambridge, UK: Cambridge University Press, 2012.

Prell, Christina. Social Network Analysis: History, Theory & Methodology . Los Angeles: Sage, 2012.

Scott, John, and Peter J. Carrington. The Sage Handbook of Social Network Analysis . London: Sage, 2011.

Wasserman, Stanley, and Katherine Faust. Social Network Analysis: Methods And Applications. Structural Analysis in the Social Sciences 8. Cambridge, UK: Cambridge University Press, 1994.

Other resources include the journals Social Network Analysis and Connections , both published by the International Network for Social Network Analysis; the SOCNET listserv; and the annual Sunbelt social networks conference.

ACKNOWLEDGMENTS

We thank Katherine Cook, Sarah Davis, Arielle DeSure, and Carrie Sjogren for fast and fastidious data-cleaning work. We thank Carter Butts for allowing us to use code originally written by him in our analyses. We also thank our funders at the National Science Foundation (NSF), IGERT Grant BCS-0314284 and NSF-DUE #1244847, for supporting this line of research. Finally, we greatly appreciate the discussions and moral support of the University of Washington Biology Education Research Group.

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Submitted: 20 August 2013 Revised: 22 January 2014 Accepted: 23 January 2014

© 2014 D. Z. Grunspan et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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• v.13(2); Summer 2014

Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research

Daniel z. grunspan.

*Department of Anthropology, University of Washington, Seattle, WA 98185

Benjamin L. Wiggins

† Department of Biology, University of Washington, Seattle, WA 98185

Steven M. Goodreau

Associated data.

The authors introduce basic concepts in SNA, along with methods for data collection, data processing, data analysis, and conduct analyses of a study relationship network. Also covered are generative processes that create observed study networks and practical issues, such as the unique aspects of human subjects review for network studies.

Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.

INTRODUCTION

Social relationships are a major aspect of the undergraduate experience. While groups on campus exist to facilitate social interactions, the classroom is a principle domain wherein working relationships form between students. These relationships, and the larger networks they create, have significant effects on student behavior. Network analysis can inform our understanding of student network formation in classrooms and the types of impacts these networks have on students. This set of theoretical and methodological approaches can help to answer questions about pedagogy, equity, learning, and educational policy and organization.

Social networks have been successfully used to test and create paradigms in diverse fields. These include, broadly, the social sciences ( Borgatti et al. , 2009 ), human disease ( Morris, 2004 ; Barabási et al. , 2011 ), scientific collaboration ( Newman, 2001 ; West et al. , 2010 ), social contagion ( Christakis and Fowler, 2013 ), and many others. Network analysis entails two broad classes of hypotheses: those that seek to understand what influences the formation of relational ties in a given population (e.g., having the same major, having relational partners in common), and those that consider the influence that the structure of ties has on shaping outcomes, at either the individual level (e.g., grade point average [GPA] or socioeconomic status) or the population level (e.g., graduation rates or retention in science, technology, engineering, and mathematics [STEM] disciplines). A growing volume of research on social influences at the postsecondary level exists, examining outcomes such as overall GPA and academic performance ( Sacerdote, 2001 ; Zimmerman, 2003 ; Hoel et al. , 2005 ; Foster, 2006 ; Stinebrickner and Stinebrickner, 2006 ; Lyle, 2007 ; Carrell et al. , 2008 ; Fletcher and Tienda, 2008 ; Brunello et al. , 2010 ), cheating ( Carrell et al. , 2008 ), drug and alcohol use ( Duncan et al. , 2005 ; DeSimone, 2007 ; Wilson, 2007 ), and job choice ( Marmaros and Sacerdote, 2002 ; De Giorgi et al. , 2009 ). The impacts are often significant, perhaps not surprisingly; this research has many implications, including the importance that randomly determined relationships such as roommate or lab partner can have on undergraduates’ behavioral choices and, consequently, their college experiences.

One key direction for education researchers is to study network formation within classrooms, in order to elucidate how the realized networks affect learning outcomes. Network analysis can give a baseline understanding of classroom network norms and illuminate major aspects of undergraduate learning. Educators interested in changing curriculum, introducing new teaching methods, promoting social equity in student interactions, or fostering connections between classrooms and communities can obtain a more nuanced understanding of the social impacts different pedagogical strategies may have. For example, we know active learning is effective in college classrooms ( Hake, 1998 ; O’Sullivan and Copper, 2003 ; Freeman et al. , 2007 ; Haak et al. , 2011 ), but the full set of causal pathways is unclear. Perhaps one important change introduced by active learning is the facilitation of student networks to be stronger, less centralized, or structured in some other new way to maximize student learning. Social network analysis (SNA) can help us assess these types of hypotheses.

Recent research in physics education has found that a student's position within communication and interaction networks is correlated with his or her performance ( Bruun and Brewe, 2013 ). An informal learning environment was found to be facilitative in mixing physics students of diverse backgrounds ( Fenichel and Schweingruber, 2010 ; Brewe et al. , 2012 ). However, these exciting initial steps into network analysis in STEM education still leave many hypotheses to explore, and SNA provides a diverse array of tools to explore them.

The goal of this paper is to enable and encourage researchers interested in biology education, and education research more generally, to perform analyses that use relational data and consider the importance of learning relationships to undergraduate education. In doing so, we first introduce some of the many basic concepts and terms in SNA. We outline methods and concerns for data collection, including the importance of gaining approval from your local institutional review board (IRB). We briefly discuss a straightforward way to organize data for analysis, before performing a brief analysis of a classroom network along three avenues: descriptive analysis of the network, exploration of network evolution, and analysis of network position as a predictor of individual outcomes. This paper is aimed at serving as an initial primer for education researchers rather than as a research paper or a comprehensive guide. For the latter, see Further Resources , where we provide a list of additional resources.

INTRODUCTION TO THE CASE STUDY

In introducing network analysis, we draw our example from a subset of a 10-wk introductory biology course with 187 students who saw the course to completion as an example. Each student in this course attended either a morning or afternoon 1-h lecture of ∼90 students four times a week and attended one of eight student labs of ∼24 students each, which met once a week for 3 h and 20 min. This course used a heavy regimen of active learning, including a significant amount of guided student–student interaction in both lecture and lab. The total percentage of active-learning activities used in this lecture course was greater than 65% of classroom time, including audience response–device questions. The data we collected included who students studied with for the first three exams, all of their class grades, the lecture and lab sections to which they belonged, and general demographic information from the registrar.

Network Concepts

In this section, we lay out some of the foundations of SNA and introduce concepts and measurements commonly seen in network studies.

Social Network Basics.

SNA aims to understand the determinants, structure, and consequences of relationships between actors. In other words, SNA helps us to understand how relationships form, what kinds of relational structures emerge from the building blocks of individual relationships between pairs of actors, and what, if any, the impacts are of these relationships on actors. Actors , also called nodes , can be individuals, organizations, websites, or any entity that can be connected to other entities. A group of actors and the connections between them make up a network.

The importance of relationships and emergent structures formed by relationships makes SNA different from other research paradigms, which often focus solely on the attributes of actors. For example, traditional analyses may separate students into groups based on their attributes and search for disproportional outcomes based on those attributes. A social network perspective would focus instead on how individuals may have similar network positions due to shared attributes. These similar network positions may present the same social influences on both individuals, and these social influences may be an important part of the causal chain to the shared outcome. In situations in which a presence or absence of social support is suspected to be important to outcomes of interest, such as formal learning within a classroom, the SNA paradigm is appealing.

Network Types.

One way to categorize networks is by the number of types of actors they contain. Networks that consist of only one type of actor (e.g., students) are referred to as unipartite (or sometimes monopartite or one-mode ). While not discussed in detail here, bipartite (or sometimes two-mode ) networks are also possible, linking actors with the groups to which they belong. For example, a bipartite network could link scholars to papers they authored or students to classes they took, differing from a unipartite network, which would link author to author or student to student.

Networks can also be categorized by the nature of the ties they contain. For example, if ties between actors are inherently bidirectional, the network would be referred to as undirected . A network of students studying with one another is an example of an undirected network; if student A studies with student B, then we can be certain that student B also studied with student A, creating an undirected tie. If the relational interest of a network has an associated direction, such as student perceptions of one another, then it is referred to as a directed network; if student A perceives student B as smart, it does not imply that student B perceives student A as smart; without the latter, we would have one directed tie from A to B.

Ties can also be binary or valued . Binary ties represent whether or not a relation exists, while valued ties include additional quantitative information about the relation. For example, a binary network of student study relations would indicate whether or not student A studied with student B, while a valued network would include the number of hours they studied together. Binary networks are simpler to collect and analyze. Valued networks include a trade-off of more information in the data versus increased analytical and methodological complexity. Using the example of a study network, the added complexity of valued networks would allow an investigation regarding a threshold number of study hours necessary for a peer impact on learning gains, while a binary network would treat any amount of study time with a peer equally.

Network Data Collection.

Collecting network data requires deciding on a time frame for the relationships of interest. Real-world networks are rarely static; ties form, break, strengthen and weaken over time. At any given time, however, a network takes on a given cross-sectional realization. Network data collection (and subsequent analyses) can be categorized, then, by whether it considers a static network, a cross-sectional realization of an implicitly dynamic network, or an explicitly dynamic network. The last of these may take the form of multiple cross-sectional snapshots or of some form of continuous data collection. Measuring and analyzing dynamic networks introduces a host of new challenges. Because the set of actors in a classroom population is mostly static for a definite period of time (i.e., a semester or quarter), while the relational ties among them may change over that period, all three options are feasible in this setting. The type of collection should, of course, be driven by the research question at hand. For example, our interest in the evolution of study networks inspired a longitudinal network collection design. Examining the impact of network ties on subsequent classroom performance, on the other hand, could be done with a single network collection.

Beyond considering the time frame of collection, it is also important to consider how to sample from a population. Egocentric studies focus on a sample of individuals (called “egos”) and the local social environment surrounding them without explicitly attempting to “connect the dots” in the network further. Typically, respondents are asked about the number and nature of their relationships and the attributes of their relational partners (called “alters”). In some fields, the term “egocentric data collection” implies that individual identifiers for relational partners are not collected, while in other fields this is not part of the definition. By either definition, egocentric studies tend to be easier to implement than other methods, both in terms of data collection and ethics and human subjects review. Egocentric data are excellent first descriptors of a sample and, in many situations, may be the only form of data available. A wide range of important hypotheses can be tested using egocentric data, although questions about larger network structure cannot. Asking a sample of college freshmen to list friends and provide demographic information about each friend listed would represent egocentric network collection.

At the other end of the spectrum, census networks, sometimes referred to as whole networks, collect data from an entire bounded population of actors, including identifiable information about the respondents’ relational partners. These alters are then identified among the set of respondents, yielding a complete picture of the network. This results in more potential hypotheses to be tested, due to the added ability to look at network structures. In our classroom study, we asked students to list other students in that same classroom with whom they studied; this is an example of a census network whose population is bounded within a single classroom.

High-quality census networks are rare, due to the exhaustive nature of the data collection, as well as the need for bounding a population in a reasonable way. It is worth noting that census networks may lack information on potentially influential relations with actors who are not a part of the population of interest; for example, important interactions between students and teaching assistants will be absent in a census network interested in student–student interactions, as would any students outside the class with whom students in the class studied. In the case of longitudinal studies, an added challenge arises—handling students who withdraw from the class or who join after the first round of data collection has been conducted. Census data collection also presents a nonresponse risk, which may result in a partial network. Nonresponse is more acute in complete network studies than other kinds of data collection because many of the commonly used analytical methods for complete networks consider the entire network structure as an interactive system and assume that it has been completely observed. Educational environments such as classrooms are fairly well bounded and have unique and important cultures between relatively few actors; they are thus prime candidates for census data collection, although the above issues must still be attended to.

Network Level Concepts and Measures.

Network analysis entails numerous concepts and measurements absent in more standard types of data analyses. Perhaps the most basic measurement in network analysis is network density . The density of a network is a measurement of how many links are observed in a whole network divided by the total number of links that could exist if every actor were connected to every other actor. These measurements are frequently small but vary by the type and size of the network. Density measurements are often hard to interpret without comparable data from other similar networks.

Density is a global metric that simply indicates how many ties are present. A long list of network concepts are further concerned with the patterns of who is connected with whom . One pervasive concept in the latter realm is homophily ( McPherson et al. , 2001 ), a propensity for similar actors to be disproportionately connected in a relation of interest. If we are interested in who studies with whom, and males disproportionately studied with other males and females with other females, this would exemplify some level of homophily by gender. Likewise, we could see homophily by ethnicity, GPA, office-hours attendance, or any other characteristic that can be the same or similar between two students. Understanding and researching homophily in classroom and educational networks may be central for several reasons. For example, two reasonable hypotheses are that relationships of social support in classrooms are more likely to be seen between students with similar backgrounds and that having sufficient social support is important for STEM retention. Testing these hypotheses by looking for homophily in networks with relation to STEM retention would provide valuable information regarding the lower STEM retention rates of underrepresented groups. Confirming these hypotheses, then, would inform improved classroom behavioral strategies for educators to emphasize.

Finding a pattern of homophily for certain research questions is interesting on its own. Note, however, that a pattern of homophily can emerge from multiple processes. Two examples of these are social selection and social influence . Social selection occurs when a relationship is more likely to occur due to two actors having the same attributes, while social influence occurs when individuals change their attributes to match those of their relational partners, due to influence from those partners. As an example, we can imagine a hypothetical college class in which a network of study partners reveals that students who received “A's” disproportionately studied with other students receiving “A’s.” If “A”-level students seek out other “A”-level students to study with, this would be social selection; if studying with an A-level student helps raise other students’ grades, this would be social influence. Depending on the goals of a study, disentangling between these two possibilities may or may not be of interest. Doing so is most straightforward when one has longitudinal data, so that event sequences can be determined (e.g., whether student X became an “A” student before or after studying with student Y).

Analyzing ties between two individuals independently, such as in studies of homophily, falls into the category of dyad-level analysis. When one has a census network, however, analysis at higher levels such as triads is possible. Triads have received considerable interest in network theory ( Granovetter, 1973 ; Krackhardt, 1999 ) due to their operational significance. Triads are any set of three nodes and offer interesting structural dynamics, such as one node brokering the formation of a tie between two other nodes, or one node acting as a conduit of information from one node to the other. One version of classifying triads in an undirected network (commonly called the undirected Davis-Leinhardt triad census) is shown in Figure 1 .

Davis and Leinhardt triad classifications for undirected networks.

In a study network, a class exhibiting many complete triads may indicate a strong culture of group study compared with a class that exhibits comparatively few complete triads. One way to examine this would be a triad census —a simple count of how many different triad types exist in a network. Another way to measure this would be to look at transitivity , a value representing the likelihood of student A being tied to C, given that A is tied to B and B is tied to C. Transitivity is a simple, local measure of a more general set of concepts related to clustering or cohesion, which may extend to much larger groups beyond size three.

In directed networks, transitivity can take on a different meaning, pointing to a distinct pair of theoretical concepts. When three actors are linked by a directed chain of the form A→B→C, then there are two types of relationships that can close the triad: either A→C or C→A (or, of course, both). The first option creates a structure called a transitive triad , and the latter a cyclical triad . For many types of relationships (i.e., those involving giving of goods or esteem), a preponderance of transitive triads is considered an indicator of hierarchy (with A always giving and C always receiving), while a preponderance of cyclical triads is an indicator of egalitarianism (with everyone giving and everyone receiving). If asking students about their ideal study partners, the presence of transitive triads would reflect a system wherein students agree on an implicit ranking of best partners, presumably based on levels of knowledge and/or helpfulness. Cyclic triads (as well as other longer cycles) would be more likely to appear if students believed that other factors mattered instead or as well; for instance, that it is most useful to study with someone from a different lab group or with a different learning style so as to maximize the breadth of knowledge.

Actor-Level Variables.

Nodes within a network also have their own set of measurements. These include the exogenously defined attributes with which we are generally familiar (e.g., age, race, major), but they also include measures of position of nodes in the network. Within the latter, a widely considered cluster of interrelated metrics revolves around the concept of centrality . Several ways of measuring centrality have been proposed, including degree ( Nieminen, 1974 ), closeness ( Sabidussi, 1966 ), betweenness ( Freeman, 1977 ), and eigenvector centrality ( Bonacich, 1987 ). Degree centrality represents the total number of connections a node has. In networks in which relations are directional, this includes measures of indegree and outdegree , or the number of edges pointing to or away from an actor, respectively. Degree centrality is often useful for examining the equity or inequity in the number of ties between individuals and can be done by looking at the degree distribution, which shows the distribution of degrees over an entire network. Betweenness centrality focuses on whether actors serve as bridges in the shortest paths between two actors. Actors with high betweenness centrality have a high probability of existing as a link on the shortest path ( geodesic ) between any two actors in a network. If one were to look at an airport network (airports connected by flights), airports serving as main hubs, such as Chicago O’Hare and London Heathrow, would have high betweenness, as they connect many cities with no direct flights between them. Closeness centrality focuses on how close one actor is to other actors on average, measured along geodesics. It is important to keep in mind that closeness centrality is poorly suited for disconnected networks (networks in which many actors have zero ties or groups of actors have no connection to other groups). Eigenvector centrality places importance on being connected to other well-connected individuals; having well-connected neighbors gives a higher eigenvector centrality than having the same number of neighbors who are less well connected. Easily the most famous metric based upon eigenvector centrality is the PageRank algorithm used by Google ( Page et al. , 1999 ). Because the interpretation of what centrality is actually measuring depends on the metric selected and the type of network at hand, careful consideration is advised before selecting one or more types of centrality for one's study.

Network Methods: Data Collection

In this section, we provide guidance for collecting network data from classrooms. Our discussion is based on existing literature as well as personal experience from our previously described network study.

Both relational and nodal attribute data can be collected using surveys. Designing an effective survey is a more challenging task than often anticipated. There are excellent resources available for writing and facilitating survey questions ( Fink, 2003 ; Denzin and Lincoln, 2005 ). This section highlights some of the issues unique to surveys for educational network data.

Survey fatigue, and its resulting problems with data quality ( Porter et al. , 2004 ), can be an issue for any form of survey research; however, for network studies, it can be especially challenging, given that students are reporting not only on themselves but also on each of their relational partners. For our project, we avoided overuse of surveys in several ways. Routine administrative information such as lab section, lecture section, student major, course grades, and exam grades was easily collected from instructor databases. Data about student demographics, educational background, and standardized testing were obtained through a request to our university's registrar's office (with accompanying human subjects approval).

We strongly suggest pilot studies with your survey, as scheduling a single high-value data collection as the first use of a survey instrument can be risky. The delay in waiting for the next term or the next class for a more vetted collection is worthwhile. Data processing time and effort can be greatly reduced by streamlined data collection, and analysis will be strengthened by iterative improvement of survey questions. With adequate design preparation, brief surveys can easily collect relational data. It is important to keep questions clear and compact. Guidance into the form of the data can make data collected from both closed- and open-ended questions much simpler to clear and process ( Wasserman et al. , 1990 ; Scott and Carrington, 2011 ).

Relational data collected in a closed-ended format such as lists, drop-down menus, or autocomplete forms can limit errors that come with open-ended data collection and are often easier to process. While these streamline student choices, they also come with a downside: they can introduce name confusions (e.g., in our class, nine students share the same first name) and are most problematic when students use nicknames. List data should always allow for both a “Nobody” answer choice and a default “I prefer not to answer” answer choice. An example of data collection with a closed list is shown below:

Question 11: We are interested in learning how in-class study networks form in large undergraduate classes. Over the next few pages is a class roster with two checkboxes next to each student—one which says “Pre-class friend” and one which says “Strong student”. For each student, evaluate whether they fit the description for each box (immediately below this paragraph), and check the box if they do.

Pre-class friend : A student that you would consider a friend from BEFORE the term of this class. If you have met someone in this class that you would consider a friend now but not before this class, do not list them as a pre-class friend.

Strong student : A student you believe is good at understanding class material.

If you are not exactly sure of a name, mark your best guess. The next question in this survey will allow you to write in a name if you don't see one or aren't sure.

***Please know that your response is completely confidential. All names will be immediately re-coded so we will have no idea who studied with whom. This information will never be used for any class purpose, grading purpose, or anything else before the end of the class. Also, please note that students that you list will not know that you listed them in this survey, and you will not know if anyone listed you.***

The number of possible choices given to subjects is an area of intense interest to survey writers in other fields ( Couper et al. , 2004 ). Limiting respondents to a given number of answers has a variety of purposes; e.g., in egocentric studies in which a respondent will be asked many questions about each partner, it can help to limit respondent fatigue. For census network data, this is not an issue because we will not need to ask students a long list of questions about the attributes of their alters; we will have that information from the alters themselves, who are also students in the class. It can also help avoid a subject with a broad definition of friendship or collaboration from dominating the data set. We chose to avoid limits on numbers of student nominations, which have the potential to induce subjects to enter data to fill up their perceived quota. In our experience, individual student responses are typically few; no student listed so many friends or study partners that it drowned out other signals significantly.

Open-ended data collection should also include a means for students to indicate that no choices fit the question, to differentiate between nonrespondents and null answers. The largest source of respondent error in open-ended data is again name confusion between students. However, errors can be minimized by providing concise instructions for student-answer formatting. For one of our projects, one example of an open-ended relational survey question was:

We are interested in how networks form in classes. Please list first and last names if possible. If this is not possible, last initials or any description of that person would be appreciated (ie: “they are in the same lab as me”, “really tall” or “sits in the second row”).

If no one fits one of these descriptions, simply write “none.”

***Your response is completely confidential. All names will be re-coded so we will have no idea who listed whom. This information will never be used for any class purpose, grading purpose, or anything else before the end of the class. Also, please note that students that you list will not know you listed them in this survey, and you will not know if anyone listed you.***

There are no right or wrong answers for this. We will ask you similar questions a few times this term. These data are incredibly valuable, so we truly appreciate your answers!

Please list any people in the class that you know are strong with class material. If you do not list anybody, please type either “No one fits description” OR “I prefer not to answer”. (separate multiple students with a comma, like “Jane Doe, John Doe”) .

Finally, it may be appropriate in smaller classes, communities with less online capability, or in particularly well-funded studies to collect relational data by interviews. This brings along greater privacy concerns but may be necessary for some hypotheses. Open-ended questions allow for greater breadth of data collection but come with intrinsic complexity in processing. For example, a valued network describing the amount of respect that students have for various faculty might be best collected in a private interview. In this format, the interviewer could more thoroughly describe “respect” by using repeated and individualized questioning to ascertain the amount of respect a student has for each faculty member.

Timing of Survey Administration

Timing of survey questions throughout a class is important. For classroom descriptions consisting of a single network, data should be collected at the earliest possible time that all students have had the experiences desired in the research study. This limits the loss of data due to students forgetting particular ties, dropping or switching classes, or failing to complete the assignment as submission rates inevitably drop toward the end of the term. For longitudinal studies involving several collections, relational data can be collected either at regular intervals or around important classroom events. In either case, we strongly suggest implanting relational survey questions in already existing assignments, if permitted, to maximize data collection rates.

For our project, we collected data throughout the 10-wk term of an introductory biology course. We surveyed for student study partnerships after each exam, spread at semiregular intervals throughout the term (weeks 3, 5, 8, and 10). It will come as no surprise to instructors that attempts to administer an additional, nongraded survey gave lower response rates from already overworked and overscheduled undergraduates. Instead, we appended ungraded survey questions to existing graded online assignments. Depending on your research question, it may be appropriate to repeat some collections to allow for redundancy or for longitudinal analyses. Friendships, for example, are subjectively defined and temporal ( Galaskiewicz and Wasserman, 1993 ). In some of our projects, we ask students for friendship relational data at both the beginning and end of the term as an internal measure of this natural volatility.

Given high response rates, anecdotal accounts of student study groupings that corroborated with the relational data, and limited extra work placed on students to provide data, we have a high level of confidence in the efficacy of our data collection methods, and others interested in network research with similar populations may also find these methods effective.

IRB and Consent

Data used solely for curricular improvement and not for generalizable research often do not require consent, but any use of the data for generalizable research does ( Martin and Inwood, 2012 ). Social network data include the unique issue of one individual reporting on others in some form or other, even if it is only on the presence of a shared relationship. They also often describe vulnerable populations; this can be especially true for educational network research, when researchers are often also acting as instructors or supervisors to the student subjects and are thus in a position of authority. This may create the impression in students’ minds that research participation is linked to student assessment. Because of this, early and frequent conversations with your local human subjects division are useful, illuminating, and should take priority ( Oakes, 2002 ).

The nature of network data not only allows subjects to report information on other subjects but may allow recognizability of even anonymized data (called deductive disclosure ), especially in small networks. This makes larger data sets typically safer for subjects. It also means that some network data fields must be stripped of information ( Martin and Inwood, 2012 ). A relatively common example is in networks of mixed ethnicity in which one ethnic group is extremely small. In these cases, ethnicities may need to be identified by random identifiers rather than specific names. In many scenarios, researchers must plan on anonymizing or removing identifiers on data ( Johnson, 2008 ). Your IRB will determine the best fit of plan for any given population of subjects.

Obtaining consent makes networks exciting and problematic at the same time. Complete inclusion of all subjects gives fascinating power to network statistics. Incomplete networks are far less compelling. More so than simpler unstructured data, networks may hinge on a small group of centralized actors in a community. The twin goals of subject protection and data set completion may compete ( Johnson, 2008 ).

In our experience, conversations with IRB advisors led to an understanding of opt-in and opt-out procedures. For example, a standard opt-in procedure would use an individual not involved with the course to talk students through a consent script, answer questions, and retrieve signed consent forms from consenting subjects. An opt-out procedure would provide the same opportunities for student information and questions but ask subjects to opt out by signing a centrally located and easily accessible form kept confidential from researchers until after the research is completed. While the opt-in procedures are more common and foreground subject protection, they tend to omit data with a bias toward underserved and less successful populations. For this reason, we used an opt-out procedure, which commonly leads to higher rates of data return. Balancing research goals and appropriate protection of subject rights and privacy is critical ( Johnson, 2008 ). By minimizing the risk to our subjects via confidential network collection, the use of an opt-out procedure was justified.

Data Management

Matrices are a powerful way to store and represent social network data. Common practice is to use a combination of matrices, one (or more) containing nodal attributes (see Table 1 ) and one (or more) containing relational data. A common form for the latter is called a sociomatrix or adjacency matrix (see Table 2 ); another is as an edgelist , a two-column matrix with each row identifying a pair of nodes in a relationship. For our study, we compiled several sociomatrices taken longitudinally at key points in the class, as well as one matrix with data of interest about our students.

Example of nodal attributes held in a matrix

Example of a small sociomatrix

A unipartite sociomatrix will always be square, with as many rows and columns as there are respondents. For undirected networks, the sociomatrix will be symmetric along the main diagonal; for undirected, the upper and lower triangles will instead store different information. Matrices for binary networks will be filled with 1s and 0s, indicating the existence of a tie or not, respectively. In cases of nonbinary ties (e.g., how many hours each student studied together) the numbers within the matrix may exceed one. The matrix storing nodal attribute information need not be square; it will have a row for each respondent and a column for each attribute measured.

It is important to understand the value of keeping rows of attribute data linkable to, and in the same order as, sociomatrices—this will ensure the relational data of a student are paired properly to his or her other data. The linkage can be done through unique names; more typically it will be done using unique study IDs.

The amount of effort and time spent cleaning the data will depend on how the data were collected and the classroom population. For this reason, it is advisable to plan the amount and means of collecting data around your ability to process them. Recently, we collected a large relational data set via open-ended survey online. To process these data into sociomatrices we created a program capable of doing more than 50% of the processing ( Butler, 2013 ), leaving the rest to simple data entry. For data collected using a prepopulated computerized list, it may even be possible for all data processing to be automated.

Data Analysis

Many different questions can be addressed with SNA, and there are nearly as many different SNA tools as there are questions. As an example, we will look at the change in student study networks over the span of two exams from our previously described study. Our main interest in these analyses will be how study networks form in a classroom and the impacts these networks have on students. To generate testable hypotheses, we will first perform exploratory data analysis, taking advantage of sociographs. These informative network visualizations offer an abundance of qualitative information and are a distinguishing feature of SNA. It is important to note that, while SNA lends itself well to exploratory analyses, it is often judicious to have a priori hypotheses before beginning data collection. The exploratory data analysis embedded below is used to provide a more complete tutorial rather than to model how research incorporating relational data must be performed.

Starting Analyses

Most familiar statistical methods require observations to be independent. In SNA, not only are the data dependent among observations, but we are fundamentally interested in that dependence as our core question. For these reasons, the methods must deal with dependence. As a result, analyses may occasionally seem different from familiar methods, while at other times they can seem familiar but have subtle differences with important implications. This point should be kept in mind while reading about or performing any analysis with dependent data.

There are a number of proprietary software packages available for performing SNA, and interested investigators should weigh the pros and cons of each for their own purposes before choosing which to use. We use the statnet suite of packages ( Handcock et al. , 2008 ; Hunter et al. , 2008 ) in R, primarily its constituent packages network and sna ( Butts, 2008 ). R is an open-source statistical and graphical programming language in which many tools for SNA have been, and continue to be, developed. The learning curve is steeper than for most other software packages, but it comes with arguably the most complex statistical capabilities for SNA. Other network analysis packages available in R are RSiena ( Ripley et al. , 2011 ), and igraph ( Csardi and Nepusz, 2006 ). Other software packages commonly used for analysis for academic purposes include UCINet ( Borgatti et al. , 1999 ), Pajek ( Batagelj and Mrvar, 1998 ), NodeXL ( Smith et al. , 2009 ; Hansen et al. , 2010 ), and Gephi ( Bastian et al. , 2009 ).

We include R code for step-by-step instructions for our analysis in the Supplemental Material for those interested in using statnet for analyses. The Supplemental Material also includes instructions for accessing a mock data set to use with the included code, as confidentiality needs and corresponding IRB agreements do not allow us to share the original data.

Exploratory Data Analysis

In performing SNA, visualizing the network is often the first step taken. Using sociographs, with nodal attributes represented by different colors, shapes, and sizes, we will be able to begin qualitatively assessing a priori hypotheses and deriving new hypotheses. We hypothesize that students who are in the same lab are more likely to study together, due to their increased interaction. We also think students with fewer study partners, and thus less group support in the class, are less likely to perform well in the class.

Figure 2 contains two sociographs visualizing the study networks for the first and second exam. Each shape represents a student, and a line between two shapes represents a study relationship. In these graphs, each color represents a different lab section, shape represents gender, and the size of each shape corresponds to how well the student performed in the class.

Sociographs representing study networks for the first and second exam. Male students are represented as triangles and females as diamonds. The color of each node corresponds to the lab section each student was in. Edges (lines) between nodes in the networks represent a study partnership for the first and second exam, respectively.

While no statistical significance can be drawn from sociographs, we can qualitatively assess our hypotheses. Judging by the clustering of colors, it seems as though same-lab study partnerships were rarer in the first exam than the second exam, for which several same-color clusters exist. This provides valuable visual evidence, but more rigorous statistical methods are important, particularly if policy depends on results.

There does not seem to be any strong visual evidence for an association between classroom performance and number of study partners. If this were true, we would see isolated nodes (those with zero ties) and nodes with few connections to be smaller on average than well-connected nodes. Visually, it is hard discern whether this is the case, and more rigorous tests can help us test this hypothesis. We first explore structural changes in study networks between the first two exams before statistically testing for an association between test scores and social studying.

Network Changes over Time

We can compare the study networks from the first and second exams using network measures such as density, triad censuses, and transitivity. These measurements allow us to assess whether the number of study partnerships are increasing or decreasing and whether any changes affect larger network structures such as triads.

Examining Table 3 , a few things become clear. First, 34 more study partnerships exist in the second exam compared with the first, a 22.5% increase in network density. This increase in study partnerships does not distinguish between students moving from studying alone to studying with other students and students who have study partners adopting more study partners. One way to gain a better understanding of the increase in overall study partnerships is to look at the degree distribution for the first two exams, seen in Table 4 .

General measurements taken from study networks of the first two exams

Degree distribution from the study networks of the first two exams

There are fewer students without study partners on the second exam, several students exhibiting extreme sociality in their study habits, and an overall trend toward more students with upwards of five study partners. Unfortunately, the degree distribution does not completely illuminate the social mobility of students between the first and second exam. One way to view general trends is to use a parallel coordinate plot using the degree data from the first and second study networks.

The plot in Figure 3 seems to indicate that the overall increase in study partnerships is not dominated by a few individuals and is instead an outcome of an overall class increase in social study habits. While we see many isolated students studying alone on the first and second exam, we also find many branching off and studying socially in the second exam. At the same rate, many students studied with partners in the first exam and become isolated on the second.

A parallel coordinate plot tracking changes in number of study partners from the first and second exam. The number of students whose number of study partners changes from exam 1 to exam 2 is denoted by the line widths.

Not only are there more overall connections, but we see higher transitivity and a trend toward complete triads. This increase in both measures indicates how students find their new study partners; they become more likely to study with their study partner's study partner, resulting in more group studying.

Ties as Predictors of Performance

Understanding study group formation and evolution is both interesting and important, but we are not limited to questions focused on network formation. As educators, we are inherently interested in what drives student learning and the kinds of environments that maximize the process. We can start addressing this broad question by integrating student performance data with network data.

As an example, we will test for an association between exam scores and both degree centrality and betweenness centrality. Studying with more students (indicated by degree centrality) and being embedded centrally in the larger classroom study network (indicated by betweenness centrality) may be a better strategy than studying alone or only with socially disconnected students. If we think of each edge in the study network as representing class material being discussed in a bidirectional manner, then more social students may have a leg up on those who are not grappling with class material with peers.

Owing to the dependent nature of centrality measures, testing for an association between network position and exam performance is not completely straightforward. One way around the dependence assumption is to use a permutation correlation test. The general idea is to create a distribution of correlations from our data by randomly sampling values from one variable and matching them to another. In effect, we will assign each student in the study network a randomly selected exam score from the scores in the class 100,000 times. This creates a null distribution of correlation coefficients (ρ) for the correlation between exam score and centrality measure for the set of exam scores found in our data, as seen in Table 5 . We can then test the null hypothesis that ρ = 0 using this created distribution.

Results from a permutation correlation test between degree and betweenness centrality and student exam performance

a Significance is seen between both types of centrality for the second exam, but not the first.

With a one-tailed test, we see no significant correlation for either centrality measure for the first exam but find a significant correlation between both betweenness centrality and degree centrality and exam performance on the second exam. With our understanding of how students changed their studying patterns between the first and second exam, this finding is rather interesting. Given the opportunity to revise their network positions after some experience in the course, we find a social influence on exam performance.

Because we are unable to control for student effort (a measure notoriously hard to capture), we are unable to discern whether study effort confounds our finding and makes causality vague. Regardless, the association is interesting and exemplifies the sort of direction researchers can take with SNA.

More Complex Models of Network Formation

The methods we present here only scratch the surface of those available and largely focus on fairly descriptive techniques. A variety of approaches exists to explore the structure of networks, to infer the processes generating those structures, and to quantify the relationships among those structures and the flow of entities on them, with a recent trend away from description and toward more inferential models. For instance, past decades saw great interest in specific models for network structure (e.g., the “small-world” model) and their implications ( Watts and Strogatz, 1998 ). A host of methods exist for identifying endogenous clusters in networks (e.g., study groups) that are not reducible to exogenous attributes like major or lab group; these have evolved over the decades from more descriptive approaches to those involving an underlying statistical model ( Hoff et al. , 2002 ). Recently, more general approaches for specifying competing models of network structure within the framework and performing model selection based on maximum likelihood have become feasible. These include actor-oriented models, implemented in the RSiena package ( Snijders, 1996 ), and exponential-family random graph models, implemented in statnet ( Wasserman and Pattison, 1996 ; Hunter et al. , 2008 ). One recent text that covers all of these and more, using examples from both biology and social science and with a statistical orientation, is Statistical Analysis of Network Data by Kolaczyk (2009) .

FUTURE DIRECTIONS

Within education research, we are just beginning to explore the kinds of questions that can benefit from these methods. Correlating student performance (on any number of measures) to network position is one clear area of research possibility. Specific experiments in pedagogical strategies or tactics, beyond having effects on student learning, may be assessable by differential effects on student network formation. For example, three groups of students could be required to perform a classroom task either by working alone, by working in pairs, or by working in larger groups. Differential outcomes might include grade results, future self-efficacy, or understanding of scientific complexity. The outcomes could be correlated with significant differences in the emergent network structures, strength of ties, and number of ties that emerge in a network of studying partnerships. Controlled experimentation with social constraints and network data would provide insight on advantages or disadvantages of intentional social structuring of class work.

Educational networks are not exclusive to students; relational data between teachers, teacher educators, and school administrators may reveal how best teaching practices spread and explain institutional discrepancies in advancing science education.

Beyond correlational studies, major questions of equity and student peer perceptions will be a good fit for directed network analysis. Conceivably, network analysis can be used to describe the structure of seemingly ethereal concepts such as reputation, charisma, and teaching ability through the social assessment of peers and stakeholders. With a better understanding of the formation and importance of classroom networks, instructors may wish to understand how their teaching fosters or hinders these networks, potentially as part of formative assessment. Reducing the achievement gaps along many demographic lines is likely to involve social engineering at some granular level, and the success or failure of interventions represents rich opportunities for network assessment.

In this primer, we have analyzed two study networks from a single classroom. We have discussed collection of both nodal and relational data, and we specifically focused on keeping surveys brief and simple to process. We transitioned these data to a sociomatrix form for use with SNA software in a statistical package. We analyzed and interpreted these data by visualizing network data with sociographs, looking at some basic network measurements, and testing for associations between network position and a nodal attribute. Data were interpreted both as a description of a single network and as a longitudinal time lapse of community change. For this project, data collection required a single field of data from the institution registrar and a single survey question asked longitudinally on just two occasions. With a relatively small investment in data collection we can rigorously assess hypotheses about interactions within our educational environments.

It bears repeating: this primer is intended as a first introduction to the power and complexity of educational research aims that might benefit from SNA. Your specific research question will determine which parts of these methods are most useful, and deeper resources in SNA are widely available.

In short, networks are a relatively simple but powerful way of looking at the small and vital communities in every school and college. Empirical research of undergraduate learning communities is sparse, and instructors are thus limited to anecdotal evidence to inform decisions that may impact student relations. We hope this primer helps to guide educational researchers into a growing field that can help investigate classroom-scale hypotheses, and ultimately inform for better instruction.

FURTHER RESOURCES

For readers whose interest in SNA has been piqued, there are numerous resources to use in learning more. We provide some of our favorites here:

Carolan, Brian V. Social Network Analysis and Education: Theory, Methods & Applications . Los Angeles: Sage, 2013.

Kolaczyk, Eric D. Statistical Analysis of Network Data: Methods and Models . Springer Series in Statistics. New York: Springer, 2009.

Lusher, Dean, Johan Koskinen, and Garry Robbins. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications . Structural Analysis in the Social Sciences 35. Cambridge, UK: Cambridge University Press, 2012.

Prell, Christina. Social Network Analysis: History, Theory & Methodology . Los Angeles: Sage, 2012.

Scott, John, and Peter J. Carrington. The Sage Handbook of Social Network Analysis . London: Sage, 2011.

Wasserman, Stanley, and Katherine Faust. Social Network Analysis: Methods And Applications. Structural Analysis in the Social Sciences 8. Cambridge, UK: Cambridge University Press, 1994.

Other resources include the journals Social Network Analysis and Connections , both published by the International Network for Social Network Analysis; the SOCNET listserv; and the annual Sunbelt social networks conference.

Supplementary Material

Acknowledgments.

We thank Katherine Cook, Sarah Davis, Arielle DeSure, and Carrie Sjogren for fast and fastidious data-cleaning work. We thank Carter Butts for allowing us to use code originally written by him in our analyses. We also thank our funders at the National Science Foundation (NSF), IGERT Grant BCS-0314284 and NSF-DUE #1244847, for supporting this line of research. Finally, we greatly appreciate the discussions and moral support of the University of Washington Biology Education Research Group.

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• Instructional Design
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10 Best Online Course Social Media: Paid & Free

Think Orion

OCTOBER 13, 2023

Practical Case Studies An online course with case studies is designed to challenge critical thinking and problem-solving skills by understanding the solutions to problems found in real-world situations. Google : Master managing your business’s online presence and reputation on Google Maps and user reviews.

LearnTrends: The Immernet Singularity

• Experiencing eLearning

NOVEMBER 18, 2009

We say “we’re instructional designers, we don’t deal with autonomous learners&# but they go out and use Google anyway. 1+2 = People want networked virtual spaces that include 2D synch + knowledge sharing (1+2). 3+4 = 3D Social Networking . Networks can give us more of that. Virtual Worlds.

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Trending Sources

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Conversation Learning

Clark Quinn

OCTOBER 2, 2008

for Learning Professionals is how social networks impact personal learning. Sure, the amount of existing content out there grows exponentially (a trillion web pages indexed by Google in 2008). I do some searching on Google and find some overviews of the solution and some other good information.

Breaking Down Big Data

AUGUST 19, 2021

With technological advancements and the presence of social networks , more and more data is created on a daily basis, and these data can be used by businesses in different aspects, e.g, Search engines like Google and tools like Google Trends can be used to increase the volume of Big Data as well. .

Innovative Work Environments

SEPTEMBER 24, 2013

Google is known for creativity, innovation, and fun– the Mountain View headquarters epitomize this. Google heavily emphasizes green practices and this can be seen by Google’s on-campus green initiatives and competitions, the bike-to-work program, and carbon neutral products.

Converting Live Workshops to an Online Course (Part I)

Web Courseworks

FEBRUARY 10, 2010

This is a great comeback to the usual “loss of social networking ” argument from people who resistant to online learning. Ways for online instructors and designers to build community: Use a Wiki or Google application and assign groups to work on a project together. Bill Winfield teaches an online course on online course design.

Surprising Course Failure Case Study Transforms into Monetized Content Expert Business with WordPress Ecommerce and WooCommerce Pro BobWP

AUGUST 18, 2019

Surprising course failure case study transforms into monetized content expert business with WordPress ecommerce and WooCommerce pro BobWP (Bob Dunn) in this episode of LMScast hosted by Chris Badgett of LifterLMS. We had a local social community. I go into my Google Analytics dashboard. Those are just free workshops.

Top 31 Online Tools You Need For Your Facebook Ads Campaigns

learnWorlds

AUGUST 31, 2021

Discover how Facebook Ads Manager can be used to its full potential through the case studies that show examples of businesses already using it. Apart from Facebook Ads, Revealbot also has Google Ads and Snapchat Ads automation tools. #10 If you are on the go, use its mobile app which is available on iOS and android. #2

Microlearning under the Microscope: How to Incorporate Microlearning into Online Training

JULY 23, 2014

Or divide learners into subgroups and challenge them to create collaborative social quizzes to test each other. Business simulations and case studies : Create real-life stories related to business issues. Micro-blogging through social networks like Yammer can extend the conversation and make learning real.

July bonanza at Working Smarter Daily

JULY 30, 2011

Working Smarter Daily draws upon ideas from design thinking, network optimization, brain science, user experience design, learning theory, organizational development, social business, technology, collaboration, web 2.0 patterns, social psychology, value network analysis, anthropology, complexity theory, and more.

2018 eLearning Predictions: Updated Hype Curve

DECEMBER 29, 2017

We especially like the idea of curation, which is providing value by letting recognized experts and social networks recommend online learning created by other authors: this is like the community playlists and celebrity playlists on Spotify. Think of those Google Alerts you excitedly set up in 2008 and haven’t read since 2009.

10 Social Media Tools For Learning

The eLearning Coach

NOVEMBER 16, 2009

In terms of promoting learning, employees could use blogs to write first-hand accounts of case studies , lessons learned, project debriefings, travel tips and cultural customs. Category: Social Networking Platform. Elgg is an aggregate of many social media technologies in one platform. Google Collaboration Tools.

100 eLearning Articles and White Papers

Tony Karrer

APRIL 28, 2008

eLearn: Case Studies - The Reluctant Online Professor 12. Managing the Complexity of Forming an Online Networking Community: 14. Podcast on Social Networking in Project-Based Learning | Beyond School 44. Learning and Networking with a Blog (Deleted Scenes) : eLearning Technology 46. Mental Model Musings 40.

CellCast App for BlackBerry 10

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JANUARY 30, 2013

What transpired next will become fodder for many future business case studies as the venerable RIM/BlackBerry began to lose market share to more sexy iOS and Android-based alternatives due to what the market perceived as a lack of innovation coming from the folks in Waterloo.

Innovations and trends in performance management

DECEMBER 8, 2015

This trend became increasingly obvious in 2015, with the publication of numerous papers and case studies all highlighting the benefits APM offers. In October, Google gave us a lesson on how to tackle this sticking point. Assist new hire to build a social network . Match new hire with peer buddy.

Cammy Beans Learning Visions: Is Facebook the next Microsoft?

Learning Visions

JULY 26, 2007

No, Google is. David, In the article they do compare Google vs. Facebook: "The difference with Facebook will be how the various applications are glued together, and this is where Facebook already has the advantage: Facebook’s origins as a social networking site means that everything they launch is linked in to that central core.

Networks and Communities

JANUARY 14, 2009

Limits of Search In Value from Social Media , I looked at a scenario where I'm evaluating a particular solution for my company / organization. Through Google , I find a lot of information. But in many cases , I will still be left feeling uncomfortable. If they would have done it, they would have come up with the same thing."

Event Marketing: Your complete Guide for 2023

JANUARY 3, 2023

Put your Event on Google ’s first page. Many people search for Events on Google and a huge proportion of them look no further to decide. Also, a good social network to use hashtags to your benefit. Also, Instagram is a great social network for hashtags. This is exactly what we’ll discuss.

LIVE from the Lectora User Conference: Keynote Sandy Carter

MAY 24, 2012

In a recent study , Sandy explained how over 80 percent of learners trust CEOs and top company executives if they are fully engaged on their social networks . “It It is too risky not to engage in social ,” Carter said. Case Studies . Product User Guides. Lectora Online - USA. Lectora Online - EU. ReviewLink.

Do not take this sitting down!

MARCH 17, 2013

Gore or Google or the agile companies you read about in Fast Company. For the first time in memory, lots of workers are singing from the same hymnal (it’s accessible on the corporate social network .) That’s the case study . Pockets of your organization are beginning to resemble W.L.

Semantic technologies and learning

Learning with e's

FEBRUARY 5, 2011

Spector ( 2009 ) of Google Inc. collaborative tagging, social networking , mash-ups, and wikis), lightweight representation of semantics and metadata is used in the form of folksonomies, user comments, and ratings. Semantic social networking in multimedia-based learning environments. techniques (e.g.

Coming for your job.or at least the way you do it now.

Mark Oehlert

MAY 6, 2008

0 thing" (I thought I had that original idea this morning, Googled it and found like 520,000 results with the oldest one seeming to date from 2005 by Joi Ito - 2005 in Web Time being something akin to the Paleoproterozoic ) really reminds of the introduction of e-learning. " Here is one for you: Web 2.0: Companies Will Spend $4.6

Connected Cultures

JUNE 26, 2008

In the final section of this volume, Part 4, we present four case studies drawn from contrasting training and learning cultures, examining the cultures of corporate e-learning, cybercriminality, language evolution and social networking . The Foreword is by Howard Rheingold.

Get the 2012 Lectora User Conference Mobile App

MAY 16, 2012

Now available for download in the iTunes store and Android Market, the “LUC 2012” mobile application provides all of the e-Learning essentials and more for the conference and downtown Chicago including conference information, social networking , local entertainment and much more. Case Studies . Product User Guides.

So many thoughts, so little time

JULY 6, 2009

Google Bets Big on HTML 5: News from Google I/O - OReilly Radar , May 27, 2009. Google Wave: What Might Email Look Like If It Were Invented Today? Twitter Drives Traffic, Sales: A Case Study - OReilly Radar , February 19, 2009. Google Announces Support for Microformats and RDFa - OReilly Radar , May 12, 2009.

Bob Little – Crystal Balling with Learnnovators

Learnnovators

JULY 20, 2015

Case studies set out a (business) challenge, how that challenge was overcome and the results that have accrued. Social networking and social learning has grown immensely since 2012. From being a term used exclusively in academic circles (around 2010), ‘ social learning’ has made steady progress.

Cammy Beans Learning Visions: The Gin System

JULY 2, 2007

It seems to me that a system like this could provide some of the networking that FaceBook provides -- within an organization -- and provide a lot of content. Have you heard of it? Online Chat with Charles Reigeluth (Instructional.

Cammy Beans Learning Visions: Using FaceBook

JUNE 19, 2007

Posted by Cammy Bean at 10:45 AM Labels: facebook , social networking 2comments: clarence said. Some Buzz on Buzzword Using FaceBook Come to Dinner -- Spreading the Word Charlie Goes to Digital Mountain Google Docs Visual and Auditory Multi-Tasking ADDIE: It's a Process Defining an e-Learning Strategy: Planning the Kick.

Storyboard Templates iPhone - Best of eLearning Learning

eLearning Learning Posts

JANUARY 5, 2010

Telstra’s Social Media Training Guide - Social Media in Learning , December 17, 2009. Google Goggles will rock m-learning. Online social networks , learning and viral expansion loops - Sticky Learning , December 14, 2009. A Social Learning Case Study Review - Learning Putty , December 8, 2009.

I need a (training) hero: How to make a career in training

JANUARY 5, 2023

The go-to tool for research, Google is a limitless pool of knowledge. Tip: Copy “link:[link] into Google , replacing “[WEBSITE]” with the domain of the site you’re researching to see what websites it’s linking to. Design online courses in just a few clicks with TalentLMS. But it’s not exclusively so.

Cammy Beans Learning Visions: Emerging Technologies in e-Learning

JUNE 28, 2007

Animation Tools Avatars Blogs Clickers Gaming Tools E-Portfolios Mobil Learning Personal Learning Environments Personalization Rapid e-Learning Semantic Web Simulation Tools Social Bookmarking Social Networking Tools VoIP and Telephony Wikis Wearable Peer to Peer Authoring Tools Haptics (the ability to touch things and get a sense of force feedback.

e-Clippings (Learning As Art): Where Do You Begin (Part II)

MARCH 26, 2007

e-Clippings (Learning As Art) Home Archives Subscribe About My Social Networks « Where do you begin when you havent posted in like 2 weeks? » March 26, 2007 Where Do You Begin (Part II) Google Desktop 5 is out. but first something important.

SEO for Course Creators with Ken Marshall from RevenueZen

NOVEMBER 5, 2022

This should include stories, case studies , and testimonials to build trust. However, Google advises against building links artificially. There is this grandma test without which, Google AdWords will not approve your account. In the beginning, you need to install Google Analytics and Google Search Console.

LearnDash Program with 10,000+ Users

APRIL 5, 2018

The answer is a resounding “Yes” In this case study you will be introduced to Anthony Balduzzi, founder of the Fit Father project. BuddyPress is a popular WordPress plugin (free) that allows you to create a social network component to your WordPress site. Video Transcript.

Using Social Media for Learning: Tools & Practices #BHsocialmedia

APRIL 27, 2011

3 key take-aways from these case studies : Don’t rely on “if they build it they will come” – still use some ‘push’ technolgoy – like email – to draw learners in so they start to see the value They don’t obsess about measurement. an entire social network surrounding your content” – it’s still in beta.

5 Creative Ways to Market Your eLearning on a Budget

Association eLearning

JUNE 20, 2013

Instead of spending thousands of dollars on expensive web banners and ads in industry newsletters, spend an hour each day sharing the word about your new eLearning offering on social networks . Hashtags are now available on Vine and Facebook too, so be sure to utilize your hashtags across social networks to maximize exposure.

Top Social Learning LMS Features

Talented Learning

JANUARY 27, 2016

Allow users to create accounts, log in, port contacts, access feeds using their existing Facebook, LinkedIn, Google +, SharePoint, Twitter or other existing social account credentials. Advanced Social Learning LMS Features. Integrated social interface – not a separate tab in the LMS. Case Studies from Dozens of Vendors.

Cammy Beans Learning Visions: Gadgets, Games and Gizmos for Learning

SEPTEMBER 12, 2007

IMing, wikis, Google , cell phones -- these gadgets are changing the workforce, regardless of the games one has played. Theres definitely trends based on age, and someone who is younger is much more likely to be an active gamer and blogger and social networker than someone who is older. Labels are just labels.

10 Best Facebook Ads Examples From Successful Course Creators

NOVEMBER 30, 2021

billion monthly active users , the world’s most popular social network has an enormous customer base and offers unparalleled advertising potential. With over 15 years of experience in the industry, and after having written five books on social media, Krista knows what she is doing. Considering that Facebook has about 2.8

Enterprise mLearning Predictions for 2011

JANUARY 2, 2011

I think case studies from enterprise organizations on the leading edge will abound by year’s end for successful mobile learning initiatives by thus providing the “I want to do what they did!” Case studies featuring accelerated ROI with tens of thousands of learners (or more!) Private Social Networks Win Over Public.

Enterprise Mobile Learning 2011 - Year in Review

DECEMBER 28, 2011

50 suggestions for implementing 70-20-10 (3)

FEBRUARY 17, 2013

Effective managers encourage their team members to buddy up on projects, to shadow others and to participate in professional social networks . A Community of Practice (CoP) is a social network of people who identify with one another professionally (e.g. What worked at Google . Encourage them to talk.

2019 Talented Learning Awards: Continuing Education Systems

FEBRUARY 13, 2019

For example, you can deliver daily or weekly questions or case studies that promote ongoing interaction and lead to certification credit. It includes full white-label site design and Google -quality search, along with easy-to-use tools for content creation, web conferencing, video capture, publishing and ecommerce.

2010 in Review Part 3: My year in reflective blogging

DECEMBER 28, 2010

.  I tried to make the case for the role of a performance consultant by comparing their work with that of a wildlife conservationist, ie someone who works on projects in the field, rather than in "caged" environments – perhaps not that successfully!   In How do you learn from #lrnchat?

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This is not me

International Conference on Technology Trends

CITT 2018: Technology Trends pp 245–258 Cite as

Integration and Evaluation of Social Networks in Virtual Learning Environments: A Case Study

• Alexandra Juma 13 ,
• José Rodríguez 13 ,
• Jorge Caraguay 14 ,
• Miguel Naranjo 15 ,
• Antonio Quiña-Mera 14 &
• Iván García-Santillán 14  
• Conference paper
• First Online: 30 December 2018

1083 Accesses

6 Citations

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 895))

Higher education institutions objective is to establish a quality learning process in which faculty members and students may use the most suitable digital communication channel. In this context, this study’s intention is to integrate and evaluate the social networks Twitter and Facebook in the Virtual Learning Environment (VLE). The study was conducted in a Technological Institute (ITSI) to determine the level of impact virtual communication has on faculty and students. In order to accomplish this goal, a Moodle platform was implemented with the following services: (i) user authentication (ii) cloud storage and file sharing and (iii) Twitter extension for message replication processes from VLE-ITSI to social networks. The proposal evaluation was performed using (i) usability and satisfaction metrics set up by the ISO/IEC 9126 standard and (ii) through a statistical analysis. The results from the standard application and the Wilcoxon statistical testing proved that social networks integration with the VLE-ITSI significantly contribute to faculty-student digital interaction during educational processes.

• Virtual Learning Environment
• Social networks
• Satisfaction
• ISO/IEC 9126

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Vialart, N.M., Medina, I.: Programa educativo para el empleo de los entornos virtuales de enseñanza aprendizaje dirigido a los docentes de enfermería (Educational program for the use of virtual teaching-learning environments aimed at nursing teachers). Rev. Cubana Tecnol. Salud 9 (1), 79–89 (2018). https://www.revtecnologia.sld.cu/index.php/tec/article/view/1025

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Jorge Caraguay, Antonio Quiña-Mera & Iván García-Santillán

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Juma, A., Rodríguez, J., Caraguay, J., Naranjo, M., Quiña-Mera, A., García-Santillán, I. (2019). Integration and Evaluation of Social Networks in Virtual Learning Environments: A Case Study. In: Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., Zúñiga Sánchez, M. (eds) Technology Trends. CITT 2018. Communications in Computer and Information Science, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-05532-5_18

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7.6: Networking Case Studies

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LEARNING OBJECTIVES

• Practice networking case studies to better understand how to build a sustainable network.
• Appreciate the nuances that are involved when you build relationships during your career search.

Case studies are a great way to “practice” your networking skills, which is always a wise thing to do. They teach you how to network better in a variety of situations.

Case Study 1

Your mentor introduces you to her colleague who introduces you to a business lead (say Jane Smith), who consents to an informational interview. You send your mentor’s colleague a nice thank-you and schedule the interview. The interview is substantive, and you send Jane Smith a nice thank-you. Two weeks later you get a formal interview, which you schedule for later. Are you done for now?

Case Study 2

You get an informational interview with a managing director, Jeff Roberts, in the boutique firm that specializes in exactly what you want to do. He asks you to coordinate with his assistant to get on his calendar. You call her to schedule the meeting. After the interview, you send Jeff Roberts a nice thank-you. Have you completed the interview etiquette?

Case Study 3

You are late for a 1:30 interview at a company’s headquarters and by the time you get there, it’s about 1:25. You go to the security desk, but bypass the X-ray area, so they redirect you there. You get a bit huffy. You rush to the elevator and fail to keep it open for a woman who is trying to get in. When you finally make it upstairs, you are escorted to the office, and asked to wait for a moment or two. When the person with whom you are meeting finally arrives, you recognize each other: you didn’t save the elevator for her. What do you do?

Case Study 4

You are scheduled for a second interview on a Friday, at 5 p.m. You are invited to attend the company’s weekly happy hour and afterward meet with some of the team privately for one-on-one interviews. You wear an interview suit and discover everyone else is wearing jeans. At your first interview, they had all worn business casual. “Jeans are allowed on Friday,” someone calls out. Are you appropriately dressed? What if you get called in the next Friday—what do you wear?

Case Study 5

You are very interested in working for two companies, and fortunately, you are in final rounds with both. You receive the first offer, and feel strongly that you will accept—in fact, you know you will if you get the second offer. The deadline for the first offer is a week away. The second company calls to schedule a final round. What do you tell them?

Case Study 6

You are in a two-on-one interview. One person is a line business manager and is taking the lead in the interview; the other person is an HR representative and does not say much. How do you conduct yourself during the interview and how do you interact with each person?

Case Study 7

You are attending a school-sponsored networking event with your classmates and representatives from a top marketing firm. You strike up a conversation with a company person and realize that several of your classmates have gathered to either contribute to your discussion or ask their own questions of the company representative with whom you are speaking. You first finish with the conversation before turning to your classmates and acknowledging their presence. Is this good or bad networking behavior? Why?

Case Study 8

You have accepted an invitation to attend training with the office of career services because a representative from a top company will be giving an overview of their business. At the last minute, you need to cram for an exam. In addition, you also do not feel well, so you decide not to attend. Is this is good or bad networking behavior? Why?

Case Study: Things to Consider

Here are key points to consider for each of these case studies, which will help you build upon your networking skills.

The topic is “Mentor Introductions and Follow-Up”:

• Always keep your mentor in the loop. They want to know you are taking their advice and reaping the fruits of your efforts. Your mentor is there to help you succeed.
• Maintain good relationships with everyone with whom you come into contact, and you will benefit in the long run. Sending thank-you notes shows good manners and an appreciative attitude, and it’s a good way to stay connected.
• Be aware of the matrix relationships all around you. When you land a position in a corporation, you can often have three or four different managers. Navigating these individuals with ease separates you from those who have difficulty doing so.

The topic is “Informational Interview Follow-Up”:

• It’s always wise to thank everyone who has helped you to land interviews and coordinate schedules. This includes administrative staff.
• Administrative assistants often carry influence with their manager, so the extra step to extend thanks for their efforts is good manners and good career management.

The topic is “Late for an Interview”:

• You only get one chance to make a first impression!
• You have to apologize, give a short explanation, and move on quickly.
• You next redirect your focus to the interview at hand and do your very best.

The topic is “Business or Business Casual Dress”:

• When in doubt, always dress in business attire. You had no idea it was dress-down Friday, so it was wise for you to wear a suit.
• When you get called back the following Friday, you remember that jeans are allowed on Friday. Jeans are allowed, but that doesn’t mean everyone wears them. The more senior people may wear khakis, and if you wore jeans, you could be dressed inappropriately (i.e., more casually than the senior managers). Remember that you are not yet an employee; you are still a candidate, so dress more conservatively.
• When interviewing in different industries, keep in mind that different dress protocols apply, for example, nuances in media are dramatically different from financial services.

The topic is “Multiple Offers”:

• The most impressive candidates communicate well and let recruiters and hiring managers know that they have options. It’s especially impressive when they communicate deadlines so appropriate actions can be taken.
• If you know you want a position with a company and you know you will accept its offer, take yourself out of the running for the second opportunity. It shows (a) confidence, (b) goodwill, and (c) your thoughtfulness in giving other candidates a chance to interview. The positive qualities and effects of this decision just go on and on!
• On the other hand, it’s always good to explore all options. Definitely let the second company know that you have received an offer from another company. Exploring this second company may help you decide which company you prefer. Perhaps they will expedite the interview process because they really want you, and then you can make a more informed decision.

The topic is “Live Interviewing with Multiple Interviewers”:

• Acknowledge the business manager and the HR representative and treat both with utmost respect. When answering the business manager’s questions, direct your answer to both parties and maintain eye contact with both.
• Remember, at all stages of the interview and job search process, you are constantly marketing yourself and selling your abilities.
• You have no idea which person is the real decision maker.
• Ask each interviewer questions and tailor your questions to the interviewer.
• You may be in other situations when you are in a group, yet talking mostly to one person. Be polite and address and acknowledge all members in the group.
• Remember that the HR representative has the ability to direct you to another business, should your interview with this particular business not go as well as you would like.

The topic is “Being Inclusive at a Networking Event”:

• Always include others in your conversation; this is both thoughtful and polite.
• Encourage others to ask questions. You can always learn from their questions and the responses they receive.

The topic is “Office of Career Services Training Session”:

• If you have accepted an invitation to attend an event, barring a medical emergency, you should make every effort to attend. Your attendance is a reflection of your commitment.
• If you absolutely cannot attend, make sure you contact someone in career services and let them know why you cannot attend. Simply not showing up is in poor taste and disrespectful of other people’s efforts on your behalf.
• If you don’t show and there is a poor turnout, the company sponsoring the business overview may decide to do fewer events with the school or pull out altogether.

KEY TAKEAWAYS

• Case studies are an excellent way to learn about proper networking etiquette.
• If you are in a position where you are not sure what to do, consult someone with the relevant expertise, including someone in career services if at all possible.
• Good networking behavior can influence your success in the job search.
• If in doubt, always dress professionally for networking activities with firms, even if it’s well known that their dress code is casual.
• Pair up with a team of five classmates to create your own networking etiquette case studies, preferably using something that truly happened. Share your case studies in class to continue the learning.

Networking Success Stories: Case Studies

Learning from the Best: Insights and Inspiration from Networking Success Stories

You might be wondering how networking can amplify your business reach and success. Given the digital age, leveraging connections—both online and offline—is crucial for professional growth.

This article delves into six case studies exploring how companies have utilized networking to acquire innovation, manage customer data, gain competitive advantages and more. Ready? Let’s dive into success stories that will inspire you to bring your networking game to new levels!

Key Takeaways

• Successful networking can open doors to opportunities and resources that accelerate professional growth.
• Social media platforms have revolutionized networking, allowing professionals to connect with colleagues, clients, and partners across the globe.
• Networking has played a crucial role in the success of companies like Epic Games, Salesforce, Scuderia Ferrari, and NatWest Bank.
• Leveraging networking can drive innovation, create competitive advantages, personalize customer experiences, optimize operational costs, enhance security measures, and improve service delivery.

Importance of Networking

Networking plays a crucial role in professional growth, connecting individuals to opportunities and resources that can accelerate their careers.

The role of connections in professional growth

Networking holds the key to professional growth. Strong connections can open doors to opportunities that would otherwise remain closed. Extensive networks allow professionals to exchange ideas, discuss challenges, and share expertise with peers across industries or around the globe.

The result is an increase in knowledge and competencies that drive career progression. Many successful companies such as Epic Games have leveraged their business networks for innovation, demonstrating the significant role of connections in driving professional growth and success.

The power of social media in networking

Social media platforms have revolutionized the networking landscape. With just a click, professionals can now connect with colleagues, clients, or potential partners across the globe.

Companies like 99Bridges and COVID-19 have effectively used social media to expand their network and collaborate without geographical barriers.

Twitter allows influencers in every industry to share insights on trending topics. LinkedIn facilitates business relationships development while Instagram gives brands an opportunity to visually engage with customers.

Facebook not only offers advertising options but also enables businesses to create groups where they can interact directly with their consumers. Indeed, these dynamic interactions fostered by social media play a crucial role in digital transformation, ultimately leading toward greater success for companies wide and far-reaching into diverse regions and industries.

Case Study 1: Epic Games

Epic Games demonstrates how leveraging networking has led to innovation, with a key role played by AWS in their success.

How leveraging networking led to innovation

Epic Games utilized their extensive network to drive innovation, creating industry trends instead of just following them. Through collaborations with strategic partners like AWS, they explored uncharted territories in gaming technology.

This specific approach unveiled new opportunities for growth and diversification, allowing the company to maintain a leading position within its sector. Leveraging networking also fostered a culture of idea sharing and teamwork that propelled further advancement within Epic Games.

The role of AWS in Epic Games’ success

Epic Games, the company behind the popular Fortnite game, dramatically increased their networking capabilities due to a strategic partnership with AWS. Utilizing high-performance and low-latency connections provided by AWS, Epic Games successfully cultivated an online gaming environment capable of supporting millions of players simultaneously.

This robust infrastructure not only improved user experience but also allowed the gaming giant to concentrate on innovation and game development. The secure and scalable solutions offered by AWS played a vital role in propelling Epic Games towards monumental success in the competitive gaming market.

Case Study 2: Salesforce

Salesforce utilized networking as a tool for customer data management, leveraging the power of connections to create a single source of truth and drive their success.

The power of a single source of truth

A single source of truth can be incredibly powerful for organizations. It ensures that everyone has access to accurate and consistent data, leading to improved decision-making and streamlined processes.

Companies like Salesforce have implemented a single source of truth through their platform, allowing businesses to eliminate data discrepancies and redundancies. This not only improves efficiency but also enhances data integrity, which is crucial for making informed business decisions.

By centralizing information in one place, organizations can harness the power of a single source of truth to drive success and achieve their goals more effectively.

Networking as a tool for customer data management

Networking plays a crucial role in customer data management, as highlighted in Case Study 2: Salesforce. By leveraging networking strategies, companies can effectively collect, store, and analyze customer data to gain valuable insights and enhance their overall business operations.

Implementing IoT solutions, monitoring and analytics tools, and cloud computing solutions are just some of the ways organizations can optimize their network infrastructure for efficient customer data management.

This enables businesses to provide personalized experiences, improve decision-making processes, and ultimately drive customer satisfaction and loyalty.

Case Study 3: Scuderia Ferrari

Scuderia Ferrari leveraged networking for competitive advantage and utilized AI and ML to enhance their networking strategies.

Networking for competitive advantage

Scuderia Ferrari understands the power of networking to gain a competitive advantage in the fast-paced world of racing. By collaborating with AWS, they are able to analyze race data in real-time and make strategic decisions that optimize their performance on the track.

This partnership allows Scuderia Ferrari to leverage advanced technologies like AI and ML, which enable them to extract valuable insights from their race data. Through networking and utilizing cutting-edge tools, Scuderia Ferrari proves that staying ahead in the competitive sports industry requires continuous innovation and collaboration with technology experts.

The role of AI and ML in networking

AI and ML technologies have a pivotal role in networking, including the operations of Scuderia Ferrari. These advanced technologies enable the team to analyze and optimize network traffic and data flow, improving overall network performance and efficiency.

By leveraging AI and ML, real-time adjustments can be made to ensure better race performance. Additionally, these technologies play a crucial role in facilitating reliable and secure communication between team members and drivers.

The use of AI and ML in networking not only enhances connectivity but also enables teams like Scuderia Ferrari to stay ahead in their competitive arena.

Case Study 4: NatWest Bank

NatWest Bank personalized customer experience through networking and reduced operational costs.

Personalizing customer experience through networking

NatWest Bank is committed to providing a personalized customer experience through networking. By implementing networking solutions, they have tailored their services to meet the individual needs of each customer.

Through this approach, NatWest Bank has been able to offer a more seamless and customized banking experience. Networking has played a pivotal role in helping them better understand and anticipate customer preferences, resulting in stronger relationships and increased customer loyalty.

The impact on operational costs

NatWest Bank’s implementation of networking solutions has had a significant impact on their operational costs. By leveraging these solutions, the bank has been able to improve operational efficiency and streamline processes, resulting in cost savings.

While the specific networking solutions used by NatWest Bank are not mentioned in the article, it is clear that their adoption has yielded positive outcomes for the bank’s bottom line.

This case study underscores the importance of implementing effective networking solutions to optimize operational costs and drive financial success.

Networking Challenges and Solutions

Staying connected: strategies for maintaining networks, the value of meaningful interactions in networking, and asking the right questions to ensure effective networking.

Staying connected: strategies for maintaining networks

Staying connected is crucial for maintaining networks. Here are some strategies to help you stay connected:

• Regularly reach out to your network: Make it a habit to connect with your contacts on a regular basis. This can be through email, phone calls, or even meeting up in person.
• Attend networking events: Take advantage of industry conferences, seminars, and other events to meet new people and expand your network. These events provide opportunities for meaningful interactions and potential collaborations.
• Utilize social media: Social media platforms like LinkedIn, Twitter, and Facebook can be powerful tools for staying connected with your network. Stay active by sharing relevant content, engaging with others’ posts, and joining relevant groups or communities.
• Nurture relationships: Building strong relationships requires effort and time. Take the time to follow up with contacts, show genuine interest in their work, and offer support whenever possible.
• Provide value to your network: Offer assistance or share valuable resources with your contacts whenever you can. This helps build trust and establishes you as a valuable connection.
• Seek out mentorship opportunities: Mentors can provide guidance, advice, and support throughout your career journey. Actively seek out mentors within your industry who can help you navigate challenges and provide insight.
• Stay updated on industry trends: Keeping yourself informed about the latest developments in your field allows you to engage in meaningful conversations with others within your network.

The value of meaningful interactions in networking

Meaningful interactions play a crucial role in networking. It is not just about collecting contacts or making surface-level connections; it’s about building relationships that have a lasting impact.

When professionals engage in meaningful conversations, they create opportunities for collaboration, mentorship, and growth. Meaningful interactions allow individuals to exchange ideas, gain insights from others’ experiences, and even find new career opportunities.

By fostering genuine connections with others in their industry or field, professionals can expand their knowledge base and access valuable resources. These interactions can lead to partnerships, referrals, and ultimately contribute to long-term success in networking endeavors.

Asking the right questions: a key to effective networking

Asking the right questions is a crucial skill for successful networking. By understanding what to ask and how to ask it, you can gather valuable information, establish meaningful connections, and open up opportunities for collaboration.

Effective questioning helps you delve deeper into conversations, uncover common interests or goals, and demonstrate genuine interest in others. By asking thoughtful and relevant questions, you show that you value the perspectives of others and are actively engaged in building relationships.

This not only strengthens your network but also allows you to gather insights and knowledge that can contribute to your professional growth.

In order to make the most out of your networking interactions, it is important to approach conversations with curiosity and an open mind. By focusing on asking insightful questions rather than simply talking about yourself or making small talk, you can foster meaningful connections that go beyond surface-level interactions.

Case Study 5: Alpha Omega

Alpha Omega, a tech company focused on meeting public demands, utilized networking to upskill its workforce in AWS and drive professional development.

Upskilling in AWS to meet public demands

Alpha Omega Integration recognized the importance of staying updated with the latest technologies in the networking industry. To meet the increasing demands of the public sector, they upskilled their employees using AWS Partner Training and Certification.

This commitment to continuous improvement allowed Alpha Omega to showcase their ability to address unique challenges and requirements in networking, ultimately leading to client satisfaction and business success.

By implementing AWS solutions, Alpha Omega demonstrated their dedication to meeting public demands effectively in a rapidly evolving industry.

The role of networking in professional development

Networking plays a crucial role in professional development, both for individuals and organizations. Building and maintaining a strong network of connections can open doors to new opportunities, collaborations, and resources.

Networking allows professionals to exchange knowledge, skills, and experiences with others in their field, helping them stay updated on industry trends and best practices. By connecting with experts and influencers, professionals can gain valuable insights and guidance that can accelerate their growth.

Additionally, networking provides a platform for showcasing one’s expertise and accomplishments, which can enhance career prospects and visibility within the industry. Ultimately, networking serves as a catalyst for continuous learning, personal growth, and advancement in the professional world.

Case Study 6: DeepThink Health

DeepThink Health strengthened their security through networking, resulting in improved healthcare service delivery.

Strengthening security through networking

DeepThink Health, a case study focused on strengthening security through networking, showcases the implementation of cloud and computing solutions, security solutions, and networking solutions.

By leveraging Cisco’s products and services, DeepThink Health has successfully enhanced their security measures. They have implemented cloud and computing solutions as well as monitoring and analytics solutions to strengthen their overall network security.

Collaboration, customer experience improvement, and digital transformation play crucial roles in achieving this goal. The case study demonstrates the effectiveness of Cisco’s offerings in the healthcare industry by highlighting successful implementations of networking solutions to enhance security measures at DeepThink Health.

The impact on healthcare service delivery

DeepThink Health has had a remarkable impact on healthcare service delivery. This case study showcases how networking solutions have transformed the way healthcare providers operate and deliver care.

While specific details are not provided, it is clear that DeepThink Health addresses challenges faced by healthcare providers in terms of networking, ultimately leading to improved efficiency, communication, and patient outcomes.

The positive influence of these networking solutions highlights the immense potential for technology to revolutionize the healthcare industry and enhance overall service delivery.

The Future of Networking

The future of networking is shaped by emerging trends and the role of technology in connecting individuals and businesses.

Emerging trends in networking

• Networking is evolving with emerging trends that are shaping the future of connectivity.
• Cloud computing is playing a vital role in networking, providing scalable and flexible solutions for businesses.
• Software – defined networking (SDN) is gaining popularity, allowing organizations to manage and control their networks through software rather than hardware.
• The Internet of Things (IoT) is revolutionizing networking by connecting devices and enabling real – time data exchange.
• Network automation is streamlining operations, reducing manual tasks, and improving efficiency.
• Artificial intelligence (AI) and machine learning (ML) are being integrated into networks to enhance security, detect anomalies, and optimize performance.
• Edge computing is becoming essential as more data is generated at the edge of networks, requiring processing closer to the source.
• 5G technology is set to revolutionize networking by providing faster speeds, lower latency, and increased capacity for connected devices.
• Network virtualization allows for the creation of virtual networks within physical infrastructures, offering flexibility and cost savings.
• Cybersecurity remains a top concern in networking, leading to advancements in network security solutions and practices.
• Emerging trends in networking are showcased through the “This Is My Architecture” program by AWS
• The architectural solutions showcased in “This Is My Architecture” demonstrate the technical capabilities of AWS

The role of technology in shaping networking

Technology plays a pivotal role in shaping networking strategies and success. With advancements in IoT, monitoring and analytics, cloud computing, collaboration tools, and security solutions, organizations have the power to transform their networking environments.

Technologies like Meraki Firewalls and Wireless Access Points cater specifically to the needs of small businesses, offering reliable and scalable networking solutions. Additionally, solutions such as Duo for secure authentication and Umbrella for enhanced network security contribute to creating robust and safe networking environments.

By leveraging technology effectively, businesses can optimize their networks for efficiency, productivity, and growth while staying ahead in today’s digital landscape.

Discovering the power of networking through real-life case studies can be inspiring and eye-opening. These success stories from companies like Epic Games, Salesforce, Scuderia Ferrari, NatWest Bank, Alpha Omega, and DeepThink Health demonstrate how strategic networking can lead to innovation, competitive advantage, personalized customer experiences, professional development, enhanced security, and improved service delivery.

By leveraging connections and staying connected in meaningful ways, organizations can unlock new opportunities for growth and success in the ever-evolving business landscape.