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Blended Learning Adoption and Implementation in Higher Education: A Theoretical and Systematic Review

• Original research
• Open access
• Published: 07 October 2020
• Volume 27 , pages 531–578, ( 2022 )

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• Bokolo Anthony Jr.   ORCID: orcid.org/0000-0002-7276-0258 1 ,
• Adzhar Kamaludin 2 ,
• Awanis Romli 2 ,
• Anis Farihan Mat Raffei 2 ,
• Danakorn Nincarean A. L. Eh Phon 2 ,
• Aziman Abdullah 2 &
• Gan Leong Ming 2  

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Technological innovations such as blended learning (BL) are rapidly changing teaching and learning in higher education, where BL integrates face to face teaching with web based learning. Thus, as polices related to BL increases, it is required to explore the theoretical foundation of BL studies and how BL were adopted and implemented in relation to students, lecturers and administration. However, only fewer studies have focused on exploring the constructs and factors related to BL adoption by considering the students, lecturers and administration concurrently. Likewise, prior research neglects to explore what practices are involved for BL implementation. Accordingly, this study systematically reviews, synthesizes, and provides meta-analysis of 94 BL research articles published from 2004 to 2020 to present the theoretical foundation of BL adoption and implementation in higher education. The main findings of this study present the constructs and factors that influence students, lecturers and administration towards adopting BL in higher education. Moreover, findings suggest that the BL practices to be implemented comprises of face-to-face, activities, information, resources, assessment, and feedback for students and technology, pedagogy, content, and knowledge for lecturers. Besides, the review reveals that the ad hoc, technology acceptance model, information system success model, the unified theory of acceptance and use of technology, and lastly diffusion of innovations theories are the mostly employed theories employed by prior studies to explore BL adoption. Findings from this study has implications for student, lecturers and administrators by providing insights into the theoretical foundation of BL adoption and implementation in higher education.

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1 Introduction

Blended learning (BL) has increasingly been utilized in higher education as it has the advantages of both traditional and online teaching approaches (Poon 2014 ). Findings from prior studies Edward et al. ( 2018 ); Ghazal et al. ( 2018 ) indicated that BL approach enhances students’ learning engagement and experience as it creates a significant influence on students’ awareness of the teaching mode and learning background. BL moves the emphasis from teaching to learning, thus enabling students to become more involved in the learning process and more enthused and, consequently, improves their perseverance and commitment (Ismail et al. 2018a ). Poon ( 2014 ) concluded that BL is likely to be developed as the leading teaching approach for the future as one of the top ten educational trends to occur in the twentyfirst century. Poon ( 2014 ) started that the question is not whether higher education should adopt BL but rather the question should be aligned to the practice that should be included for successfully BL implementation.

The phrase blended learning was previously associated with classroom training to e-learning activities (Graham et al. 2013 ). Accordingly, BL is the integration of traditional face-to face and e-learning teaching paradigm (Wong et al. 2014 ). BL employs a combination of online-mediated and face-to-face (F2F) instruction to help lecturers attain pedagogical goals in training students to produce an algorithmic and constructive rational skill, aids to enhance teaching qualities, and achieve social order (Subramaniam and Muniandy 2019 ). BL entails the combination of different methods of delivery, styles of learning, and types of teaching (Kaur 2013 ). BL is frequently used with terms such as integrated, flexible, mixed mode, multi-mode or hybrid learning (Garrison and Kanuka 2004 ; Moskal et al. 2013 ). BL comprises integration of various initiatives, achieved by combining of 30% F2F interaction with 70% IT mediated learning (Anthony et al. 2019 ). Similarly, Owston et al. ( 2019 ) recommended that a successful BL delivery comprises of 80% high quality online learning integrated with 20% classroom teaching that is linked to online content. Respectively, BL is the combination of different didactic approaches (cooperative learning, discovery learning expository, presentations, etc.) and delivery methods (personal communication, broadcasting, publishing, etc.) (Graham 2013 ; Klentien and Wannasawade 2016 ).

Research has found that online systems possess the capability of providing platforms for competent practices in offering alternative to real-life environment, offering students a usable avenue for learning which support students to improve the quality of learning (Wong et al. 2014 ; Ifenthaler et al. 2015 ). When prudently and accurately deployed, IT can be deployed to achieve a reliable learning experience with practical relevancy to engage and motivate students (Tulaboev 2013 ). Thus, BL facilitates students to not only articulate learning but to also test on the knowledge they have attained through the semester (Aguti et al. 2013 ). Moreover, BL offers flexibility for students and lecturer, improved personalization, improved student outcomes, encourages growth of autonomy and self-directed learning, creates prospects for professional learning, reduced cost proficiencies, increases communication between students and lecturer, and among students (So and Brush 2008 ; Spring et al 2016 ). BL emboldens the reformation of pedagogic policies with the prospective to recapture the ideals of universities (Heinze and Procter 2004 ). BL seeks to produce a harmonious and coherent equilibrium between online access to knowledge and traditional human teaching by considering students' and lecturers' attitudes (Bervell and Umar 2018 ). BL therefore remains a significant pedagogical concept as its main focus is aligned with providing the most effective teaching and learning experience (Wang et al. 2004 ).

BL offers access to online resources and information that meet the students’ level of knowledge and interest. It supports teaching conditions by offering opportunities for professional collaboration, and also improve time adeptness of lecturers (Guillén-Gámez et al. 2020 ; Owston et al. 2019 ). BL proliferates students’ interest in their individual learning progression (Chang-Tik 2018 ), facilitates students to study at their own speed, and further organize students for future by providing real-world skills (Ustunel and Tokel 2018 ), that assist students to directly apply their academic skills, self-learning abilities, and of course computer know how into the working force (Güzer and Caner 2014 ; Yeou 2016 ). As pointed out by Al-shami et al. ( 2018 ) BL improves social communication in university’ communities, improves students’ aptitude and self-reliance, increased learning quality, improve critical thinking in learning setting and incorporate technology as an operative tool to convey course contents to students (Bailey et al. 2015 ; Baragash and Al-Samarraie 2018a ).

Existing studies mainly considered BL in the context of students and lecturers in improving teaching and learning. Prior studies paid attention to BL adoption towards improving the quality of student learning and lecturers teaching. But only fewer studies explored BL implementation process as well explored administrators’ who initiate policies related to BL adoption in higher education. To fill this gap in knowledge, this current study aims to systematically reviews and synthesizes prior studies that explored BL adoption and implementation related to students, lecturers and administration based on the following six research questions:

RQ1 What are the research methods, countries, contexts, and publication year of selected BL studies?

RQ2 Which BL studies proposed model related to BL adoption in higher education?

RQ3 Based on RQ2 what are the theories, location, and context of the selected BL studies?

RQ4 Based on RQ3 what are the constructs of the identified theories employed to explore BL adoption in higher education?

RQ5 What are the constructs and factors that influence students, lecturers and administration towards adopting BL?

RQ6 What are the practices involved for BL implementation in higher education?

Therefore, to address the research questions this study review and report on BL adoption model (constructs and factors), BL implementation processes, prior theories employed, and related studies that were mainly focused on BL adoption in relation to students, lecturers, and administrator’s perspective. The remainder of the article is organized as follows. Section  2 is the literature review. Section  3 is the methodology and Sect.  4 describes the findings and discussion. Section  5 is the implications and Sect.  6 is the conclusion, limitation, and future works.

2 Literature Review

Learning in higher education refers to process of acquiring new knowledge, skills, intellectual abilities which can be utilized to successfully solve problems. The deployment of technologies in teaching and learning is not a new paradigm in higher education (Poon 2012 ). Undeniably, in the twentyfirst century students are familiar with digital environments and therefore lecturers are encouraged to use Information Technology (IT) in teaching to stimulate and employ students’ learning (Ifenthaler and Widanapathirana 2014 ; Edward et al. 2018 ). Teaching and learning with the aid of BL practices have become a common teaching approach to involve students in learning (Garrison and Kanuka 2004 ). As such, BL has progressed to incorporate diverse learning strategies and is renowned as one of the foremost trends in higher education (Ramakrisnan et al. 2012 ). BL provides pedagogical productivity, knowledge access, collective collaborations, personal development, cost efficiency, simplifies corrections and further resolves problems related to attendance (Mustapa et al. 2015 ). Findings from prior studies (Wai and Seng 2015 ; Nguyen 2017 ) suggested that BL offers benefits and is also productive than traditional e-learning.

BL in higher education is a prevailing approach to create a more collaborative and welcoming learning environment to curb students' anxiety and fear of making mistakes (Wong et al. 2014 ). Adopted in universities in the late 1990s (Edward et al. 2018 ), it found wider acceptance in the 2000s with many more university courses offered in blended mode (Graham et al. 2013 ). BL employs a combination of online-mediated and face-to-face instruction to help lecturers attain pedagogical goals in training students to produce algorithmic and constructive rational skills, aids to enhance teaching qualities and achieve social order (Kaur 2013 ). Some researchers [such as Bowyer and Chambers ( 2017 )] argued that technology integration in teaching promotes learning via discovery. And adds interactivity and more motivation, leading to better feedback, social interactions, and use of course materials (Sun and Qiu 2017 ).

As seen in Fig.  1 , BL implementation usually involves F2F and other corresponding online learning delivery methods. Normally, students attend traditional lecturer-directed F2F classes with computer mediated tools to create a BL environment in gaining experiences and also promote learners’ learning success and engagement (Moskal et al. 2013 ; Baragash and Al-Samarraie 2018b ). In fact, Graham ( 2013 ); Graham et al. ( 2013 ) projected that BL will become the new course delivery model that employs different media resources to strengthen the interaction among students. BL provide motivating and meaningful learning through different asynchronous and synchronous teaching strategies such as forums, social networking, live chats, webinars, blog, etc. that provides more opportunities for reflection and feedback from students (Graham 2013 ; Moskal et al. 2013 ; Dakduk et al. 2018 ).

Key aspects of BL derived from (Graham 2013 ; Moskal et al. 2013 )

BL is facilitated with virtual learning management systems such as Blackboard WebCT, Moodle, and other Web 2.0 platforms which are employed to facilitate collaborative learning between students and lecturers (Edward et al. 2018 ; Anthony et al. 2019 ). Accordingly, Aguti et al. ( 2014 ) stated that 80 percent of institutions in developed regions dynamically employ BL approach to support teaching and learning, with 97 percent of institutions reported to be deploying one or more forms of IT mediated learning. Figure  1 indicates that BL instructional design and type of delivery includes online activities such as wordbook, reading materials, online writing tool, message board, web links, tutorials, discussion forum, reference material, simulations, quizzes, etc. (Anthony et al. 2019 ). Conversely, F2F teaching involves lectures, laboratory activities, assessment skill practices, presentation, individual/group, and discussions carried out by the lecturer to examine the learning performance of students (Sun and Qiu 2017 ).

There has been rapid development in BL adoption focused on improving teaching and learning outcome, thus prior studies assessed the effectiveness of BL by comparing the traditional teaching and online teaching (Van Laer and Elen 2020 ). However, there are limited studies that investigated the theoretical foundation of BL adoption and implementation for teaching and learning (Wai and Seng 2015 ), and very limited studies focused on investigating administrative adoption related to BL. To this end, Garrison and Kanuka ( 2004 ) mentioned that it is important to examine BL adoption from the lens of institutions administrators. Researchers such as Wong et al. ( 2014 ) argued that while there are studies in BL, research that focused on BL adoption and implementation are limited, and that this is a gap to be addressed. Given the above insights, it is felt that more BL based research is needed to guide policy makers to strategically adopt BL in higher education towards improving learning and teaching. Therefore, this study systematically reviews and synthesizes prior studies that explored students, lecturers and administration adoption and implementation of BL.

3 Methodology

It is important to carry out an extensive literature review before starting any research investigation (Anthony et al. 2017a ). Literature review finds research gaps that exists and reveals areas where prior studies has not fully explored (Anthony et al. 2017b ). Likewise, a systematic literature review is a review that is based on unambiguous research questions, defines and explores pertinent studies, and lastly assesses the quality of the studies based on specified criteria (Al-Emran et al. 2018 ). Accordingly, this study followed the recommendation postulated by Kitchenham and Charters’s ( 2007 ) in reporting a systematic review. Therefore, the research design for this study comprises of five phases which includes the specification of inclusion and exclusion criteria, presenting of search strategies and data sources, quality assessment, and data coding and analysis, and lastly findings. The research design of this review study is shown in Fig.  2 .

Research design for SLR

Figure  2 depicts the research design for this study, where each phase is presented in the subsequent sub-sections.

3.1 Inclusion and Exclusion Criteria

The inclusion and exclusion criteria (Table 1 ) and quality assessment criteria (see Table 2 ) are employed as the sampling/selection methods used to select the articles involved in this study. The inclusion and exclusion criteria are defined in Table 1 .

3.2 Search Strategies and Data Sources

The articles involved in this study were retrieved through a comprehensive search of prior studies via online databases which included Google Scholar, ScienceDirect, Emerald, IEEE, Sage, Taylor & Francis, Inderscience, Springer, and Wiley. The search was undertaken in December 2018 and March 2020. The search terms comprise the keywords ((“blended learning practices” OR “blended learning variables” OR “blended learning factors” OR “blended learning constructs”) AND (“implementation” OR “adoption” OR “approach” OR “model” OR “framework” OR “theory”)) AND (“components” OR “elements”)). The mixture of the keywords is a crucial step in any systematic review as it defines articles that will be retrieved.

Figure  3 depicts the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flowchart which was employed for searching and refining of the articles as previously utilized by Al-Emran et al. ( 2018 ). The search output presented 388 articles using the above stated keywords. 93 articles were establish as duplicates, as such were removed. Therefore, resulted to 302 articles. The authors checked the articles against the inclusion and exclusion criteria and added 12 new articles based on snowballing techniques which was used to get more articles from the references of 82 studies. Accordingly, 94 research articles meet the inclusion criteria and were included in the review process. Additionally, four studies (Kitchenham and Charters 2007 ; Anthony et al. 2017 , b ; Al-Emran et al. 2018 ) were included in the reference since they discuss SLR process.

PRISMA flowchart for the selected articles

3.3 Quality Assessment

One of the vital determinants that are required to be checked along with the inclusion and exclusion criteria is the quality assessment. To this end, a quality assessment checklist which comprises of “10” criteria was designed and employed as a means for evaluating the quality of the studies selected (n = 94) (see Fig.  3 ). The quality assessment checklist is shown in Table 2 . The checklist was adapted from recommendation from (Kitchenham and Charters 2007 ). Accordingly, the question was measured based on a 3-point scale which ranges from, 1 point being assigned for “Yes”, 0 point for “No”, and 0.5 point for “Partially”. Hence, each article score ranges from 0 to 10, where a study that attains higher total score, possess the capability to provide addresses the specified research questions. Table 11 in appendix shows the quality assessment results for all the 94 studies. Respectively, it is apparent that the selected studies have passed the quality assessment, which indicates that all the articles are eligible to be utilized for further meta-analysis.

3.4 Data Coding and Analysis

The characteristics related to the research methodology outcome were coded to include purpose of research, (BL adoption constructs and factors or BL implementation practice), research approach (e.g., literature review, conceptual, survey questionnaire, case study interviews, or experimental), country, context (e.g., student, lecturer and/or administrator), and model/framework or theory employed (e.g., Technology Acceptance Model (TAM), information system success model, the Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovations theory (DoI), Adhoc, etc.). In between the data analysis procedure, the articles that did not directly describe BL adoption model variables and implementation practices were excluded from the synthesis.

4 Findings and Discussion

Based on the selected 94 studies published in regard to BL adoption and implementation from 2004 to 2020, this review reports the findings of this systematic review in relation to the specified six research questions.

4.1 RQ1: What are the Research Methods, Countries, Contexts, and Publication Year of Selected BL Studies?

With regard to the first research question, the findings for distribution of studies related to BL adoption and implementation in higher education based on year of publication is presented in Fig.  4 . As shown, the studies are ranged from 2004 to 2020. Findings from Fig.  4 indicate that there seems to be an increase in studies on BL over the last few years as seen from 2004 to 2020, with 2018 being the highest with publications on BL adoption and implementation with 17 studies published. It is evident that the frequency of these publications in 2018 could be accredited to the fact that the intensity of BL implementation in 2018 across higher education has improved mainly in developed and developing countries across the world.

Distribution of selected BL studies in terms of years

Considering the research methodology applied in the 94 BL studies, findings from Fig.  5 show that questionnaire survey is the most employed method for data collection (N = 49, 62%), followed by studies that were conceptual by design with (N = 14, 16%). Next, is studies that adopted mixed method both survey and interview with (N = 11, 13%) and studies that are qualitative in nature as case study/interview with (N = 8, 9%). For the remaining studies (N = 5, 5%) employed experimental using LMS dataset, (N = 4, 4%) conducted literature review, and lastly only (N = 1, 1%) study deployed a mixed experimental and survey approach. These findings are analogous with the prior review studies conducted by (Holton III et al. 2006 ; Kumara and Pande 2017 ) who discussed that quantitative studies were the main approach employed in prior BL studies. Furthermore, this finding is consistent with the fact that surveys are considered as the most suitable tool to collect data in validating constructs/factors in developed BL adoption model in investigating students and lecturers’ perceptions towards BL practice in higher education (Ghazali et al. 2018 ; Ismail et al. 2018b ).

Distribution of selected BL studies in terms of research methods

With regard to the 94 BL studies country distribution, findings from Fig.  6 shows research related to BL adoption in higher education. Accordingly, most of the studies are conducted in Malaysia (N = 28), this is based on the fact that the Malaysia ministry of education initiated an educational blueprint for all higher education to adopt BL from 2015 to 2022. Therefore, there were several studies that proposed models to examine BL adoption in universities in Malaysia context. Next, research articles related to BL adoption was carried out in United States of America with (N = 11), and Australia (N = 10) and United Kingdom with (N = 7), followed by Turkey with (N = 4), Canada, Indonesia, and Spain with (N = 3) respectively. Additionally, Fig.  6 indicates that (N = 2) studies were conducted in Norway, Dubai, UAE, India, Singapore, Saudi Arabia, and Taiwan. Lastly, (N = 1) study was each conducted in Greece, Germany, Philippines, South Korea, The Netherlands, Thailand, Vietnam, Belgium, Bulgaria, China, Poland, Israel, Morocco, Colombia, Sri Lanka, and Ghana. This finding also suggest that most of the first researchers of BL adoption such as Garrison and Kanuka ( 2004 ), Graham et al. ( 2013 ), Poon ( 2014 ) and Porter and Graham ( 2016 ) are from USA, Canada, Australia and UK who are one of the most cited researchers in BL practice in higher education as compared to other regions.

Distribution of selected BL studies in terms of countries

Considering the selected studies context distribution of BL adoption in higher education findings from Fig.  7 indicate that (N = 59, 62%) studies mainly examined BL adoption by considering students perspective. This finding is consistent with results from prior studies (Wai and Seng 2013; Rahman et al. 2015 ) which advocated for the need for developing a model of measuring student satisfaction, perception (So and Brush 2008 ), commitment (Wong et al. 2014 ), effectiveness (Wai and Seng 2015 ) in the BL. In addition, findings from Fig.  7 reveal that (N = 9, 10%) studies mainly examined BL adoption by considering lecturers perspective. This finding is very consistent with results from the literature (Wong et al. 2014 ; Zhu et al. 2016 ), where the authors mentioned the need for a study to investigate the current level of adoption of BL among the academicians to identify the factors that influence BL adoption.

Distribution of selected BL studies context

Furthermore, the findings suggest that (N = 7, 8%) studies mainly examined BL adoption by considering administrative perspective. Similarly, this finding is analogous with results from qualitative studies conducted by prior researchers (Koohang, 2008 ; Graham et al. 2013 ; Porter et al. 2016 ; Bokolo Jr et al. 2020 ) which revealed that there are limited studies that explored policy and governance issues related BL adoption. Additionally, findings from Fig.  7 show that (N = 10, 10%) studies that concurrently examined BL in the context of students and lecturers, this aligns with findings presented by Brahim and Mohamad ( 2018 ); Edward et al. ( 2018 ) where the authors called for the need for empirical evidence on BL implementation to improve academic activities. Lastly, (N = 9, 10%) studies investigated BL in the context of student, lecturer, and administrators. This finding suggests that there are limited studies that examine students, lectures and administrators simultaneously as mentioned by (Machado 2007 ; Wong et al. 2014 ; Bokolo Jr et al. 2020 ). Accordingly, this review presents the constructs and factors that influence BL adoption from the perspective of students, lecturers, and administrators in higher education.

4.2 RQ2: Which BL Studies Proposed Model Related to BL Adoption in Higher Education?

Several studies have been carried out directed towards investigating the adoption of BL in higher education. Thus, Table 3 shows that out of the selected 94 studies only 51 studies developed models to examine BL where each study is compared based on the authors, contribution, purpose and identified factors/attributes and methods.

Based on the selected 51 BL studies that develop a research model to examine BL adoption in higher education, the review indicates that none of the studies is concerned with BL practices to be implemented in higher education, they are mainly concerned about BL adoption factors/attributes. As seen in Fig.  8 out of the reviewed 51 BL studies that developed models to examine BL adoption. The results suggest that survey questionnaire was most employed, whereas experimental and survey was least employed to validate the developed models. Also, Fig.  9 presents the clustered of issues addressed in the reviewed 51 BL studies. The identified factors/attributes derived from the reviewed 51 BL studies are presented in Fig.  10 and further discussed in Tables 6 , 7 and 8 .

Distribution of the reviewed 51 BL studies that developed BL adoption models

Clustering of issues addressed in the reviewed BL adoption studies

Identified factors/attributes derived in the reviewed BL adoption studies

4.3 RQ3: Based on RQ2 What are the Theories, Location, and Context of the Selected BL Studies?

Among the selected 51 BL studies, this sub-section presents prior theories that have been utilized to examine BL adoption in higher education. Moreover, the location and BL context of the 51 BL studies are presented as seen in Table 4 .

Findings from Table 4 and Fig.  11 indicate that out of the reviewed 51 BL studies, (N = 37, 72%) studies investigated BL by considering the students context similar to previous studies Tuparova and Tuparov ( 2011 ); Roszak et al. ( 2014 ), while (N = 2, 4%) studies examined BL by considering only lecturers’ context. Besides, (N = 4, 8%) studies only examined administration context analogous with prior study Mercado ( 2008 ), while another (N = 6, 12%) studies examined BL by considering the students and lecturers context similar to prior studies Maulan and Ibrahim ( 2012 ); Mohd et al. ( 2016 ). Lastly, (N = 2, 4%) studies examined BL by considering the students, lecturers and administration context analogous to research conducted by Mercado ( 2008 ); Anthony et al. ( 2019 ). Hence, it is evident that there are fewer studies that investigated BL adoption by concurrently exploring students, lecturers and administration viewpoint. Thus, this review aims to address this limitation by reviewing theoretical foundation of BL adoption and implementation in the lens of students, lecturers and administration.

Selected BL adoption studies context distribution

4.4 RQ4: Based on RQ3 What are the Constructs of the Identified Theories Employed to Explore BL Adoption in Higher Education?

This sub-section reviews the constructs of theories employed by the selected 51 BL studies in developing their model as seen in Table 5 .

Based on Tables 4 and 5 , Fig.  12 depicts the frequency of how many times each theory has been employed by prior BL studies. Findings from theories employed show that ad hoc is the most employed approach with (N = 23, 42%) studies, followed by TAM with (N = 7, 13%) studies, IS success model and UTAUT with (N = 4, 7%) studies individually, and DoI with (N = 3, 5%) studies, whereas the other theories were adopted by (N = 1, 2%) study respectively.

Distribution of BL studies in terms of adopted theories

4.5 RQ5: What are the Constructs and Factors that Influence Students, Lecturers and Administration towards Adopting BL?

The constructs and factors related to the adoption of BL by students, lecturers and administrators are shown in Fig.  13 and described in Table 6 .

Constructs and factors related to BL adoption in higher education

Tables 6 , 7 and 8 describes the derived constructs for students, lecturers, and administration related to BL adoption in higher education. BL adoption cannot be attained by only integrating online and face-to-face teaching modes (Azizan 2010 ). Thus, there is need to identify the constructs that influence students, lecturer, and administration in adopting BL practices to be implemented that play an important role in ensuring successful BL experience in higher education (Graham 2013 ; Güzer and Caner 2014 ). On this note, academicians such as Machado ( 2007 ); Wong et al. ( 2014 ); Kumara and Pande ( 2017 ); Bokolo Jr et al. ( 2020 ) highlighted that successful implementation of BL initiatives requires an alignment between administrative, lecturers, students’ educational goals. According to Dakduk et al. ( 2018 ); Anthony et al. ( 2019 ) it is importance to examine constructs related to human computer interaction to assess which constructs contributes to realizing the desired teaching and learning objectives while engaging the lecturers and students. Therefore, this study explores the BL practices to be implemented by students and lecturers in higher education as seen in Figs.  14 and 15 .

BL practice implementation for students in higher education

BL practice implementation for lecturers in higher education

4.6 RQ6: What are the Practices Involved for BL Implementation in Higher Education?

The practice to be carried out by students for implementing BL in higher education is shown in Fig.  14 .

Figure  14 depicts BL practice implementation for students in higher education. According to Kaur and Ahmed ( 2006 ); Kaur ( 2013 ) the recommended balance of BL activities for successful delivery is 80% online learning (activities, information, resources, assessment and feedback) followed by 20% classroom instruction (face to face) that is aligned to the online teaching content. Similarly, Ginns and Ellis ( 2007 ) argued that for an effective BL initiative it is required to achieve a blend of 29–30% face to face and 79–80% on-line teaching delivery. This is in line with findings from previous studies (Graham et al. 2013 ; Bokolo Jr et al. 2020 ), which states that there is need for policies showing clear decrease of face to face classroom hours and increasing online learning as a strategy to enhance BL implementation in higher education (Park et al. 2016 ). Further description of BL implementation for students is discussed in Table 9 .

Figure  15 depicts BL practice implementation for lecturers in higher education. The BL practice is based on the Technology, Pedagogy, and Content Knowledge (TPACK) framework proposed by Koehler and Mishra ( 2009 ). TPACK aimed address issues faced by how lecturers can integrate technology into their current teaching (Wang et al. 2004 ; Sahin 2011 ). Thus, TPACK offers a method that indulgences teaching as collaboration between what lecturers know and how they teach and apply what they already know uniquely through BL implementation in the contexts of physical and online classes (Graham et al. 2009 ; Koehler and Mishra 2009 ). Further description of TPACK the components in relation to BL implementation is discussed in Table 10 .

5 Implications for Theory, Methodology and Pedagogical Practice

Findings from this study offer implications for theory, methodology and pedagogical practice for higher education towards adopting BL.

5.1 Implications for Theory

Theoretically, this study identifies the factors that influence students, lecturers and administrators’ towards adopting BL. Our findings provide insight by revealing factors for higher education to better recognize how BL can be delivered towards the development of students’ learning effectiveness and also offering in-depth understanding of BL and its efficiency in order to improve students’ competence. The identified factors can be employed by institutions to assess students, lecturers and administrators’ perception towards BL and can be used to inform government policy making regarding BL development. Besides, this study also indicates that the lecturer’s attitude, teaching style, and acceptance toward BL are important in motivating the students to adopt BL. The lecturer’s attitude toward students and his/her level of responsiveness and communication are important factors that motivate students in BL environment. The findings emphasized the importance of administrative commitment towards BL adoption, showing that the purpose, advocacy and definition initiated towards BL have a strong impact on both learning and teaching effectiveness. The findings provide theoretical support to determine the relationship among the constructs and factors of BL adoption for students, lecturers and administrators (see Fig.  13 ) towards F2F and online learning.

5.2 Implications for Methodology

Based on the TPACK framework, this study provides lecturers with understanding of students' perspective on BL in helping them to reflect on their role in improving their current pedagogy, technological infusion, and syllabus design to enhance student learning and teaching outcome. Decision makers in higher education can utilize findings from this study to improve their understanding of the factors that impacts students, lecturers and administrators’ perception towards BL adoption. Respectively, given the different perspectives of students, lecturers and administrators it is mandatory for policy makers in higher education involved in the implementation of BL to deliberate on the perspectives of all stakeholders. Respectively, findings from this study significantly provide an outline for Ministry of Education across the world towards fostering BL as a teaching and learning approach for academic staffs in higher education. The BL practices for students (see Fig.  14 ) and strategies to be implemented by lecturers (see Fig.  15 ) can be integrated to the existing pedagogical polices to improve the significance of BL as one of the methods in learning and teaching. For universities and academicians, findings from this study suggest that BL serves as a substitute to learning and teaching from the traditional perspective to enhance the quality of teaching and learning of students in achieving better performance.

5.3 Implications for Pedagogical Practice

This study contributes to the acknowledgment of BL as a medium to support teaching and learning approach. The findings describing how BL practice can be implemented by students as seen in Sect.  4.6 (Fig.  14 ). Practically, findings from this study can be useful in the preparation of the best practice to support lecturers in teaching and implementing inventive approaches that promotes BL to enhance teaching and learning outcomes to be used as the reference for the arranging methodologies to embrace BL in higher education. Findings from this study indicate that BL practices derived from the literature which comprises of face-to-face, activities, information, resources, assessment, and feedback to be deployed by educators to design suitable learning policies in order to support students towards improving learning. These findings provide guidelines on the design and implementation of BL practice. This study suggests that for BL practice to be successfully implemented the decision of lecturers are determined by the ease with which online course services are managed. Thus, the availability of computer hardware and software resources, pedagogical support, financial support, and promotion consideration should be provided by institutions management. For administrators this study provides a policy roadmap to adopt BL in higher education.

6 Conclusion, Limitations, and Future Works

Review of prior studies on BL offer valuable insight regarding research related to BL practice in higher education. Nonetheless, these review studies ignored examining BL adoption and implementation in regard to students, lecturers and administrators simultaneously. Accordingly, this study conducted a systematic literature review for prior BL adoption model proposed related to theories employed in the model to investigate BL adoption in higher education. This study also identified the constructs and factors that influence students, lecturers and administration towards adopting BL studies and lastly derived the practices involved for BL implementation for students and lecturers in higher education with the aim of providing meta-analysis of the current studies and to present the implications from the review. Respectively, this paper extends the body of knowledge in BL studies by presenting 7 new findings. First, the review reveal that ad hoc approach is the most employed method by prior studies in developing research model to investigate BL adoption in higher education, followed by TAM, and then IS success model, then is UTAUT, and lastly DoI theory.

Secondly, findings show that questionnaire surveys were the most employed research methods for data collection utilized by prior BL studies in higher education. Third, the findings reveal that BL model adoption studies were carry out in Malaysia and USA, this is followed by Australia, UK, Canada, respectively among the other countries. Fourth, most of the BL studies were recurrently conducted towards examining BL in students’ context, followed by lecturers’ context, correspondingly among the other contexts. Fifth, with regard to publication year, BL studies have experienced vast attraction over the years (2016 to 2019) from many academicians who contributed to investigating BL adoption and implementation in higher educational context, where our findings observed an increase of 19 publications in 2018 (see Fig.  4 ) representing the highest frequency of the total studies. Sixth, this review also presents 51 prior studies that developed model relating to the adoption of BL in higher educational domain and further identify the constructs/factors that influence the perception of students, lecturers, and administration readiness towards BL adoption. Seventh, findings from this review present the BL practice to be implemented by students and lecturers in higher education. To that end, the identified constructs/factors that influence BL adoption and the derived BL practices implementation can be used to conceptualize and develop a model to examine student, lecturers, and administrators concurrently towards BL adoption and implementation in higher education.

Despite the aforementioned contributions, this study has a few limitations. First, the reviewed BL studies comprises of studies related to BL adoption and implementation approaches, models, and frameworks. BL readiness and effectiveness were not investigated in this current study. Secondly, this study mainly focused on popular online databases for collecting articles (i.e., ScienceDirect, Sage, Emerald, Inderscience, Wiley, Google Scholar, Springer, Taylor & Francis, and IEEE). Given that, the databases may not provide all relevant studies published on BL adoption and implementation. Thirdly, no theoretical model was proposed with hypotheses for further validation. Future studies could examine BL readiness and effectiveness from student, lecturers, and administrator’s perspective by developing a research model with hypotheses. The model will be evaluated using survey questionnaire since it’s the most widely employed methodology as seen in Fig.  5 and 8 . Further research could also extent this study by including more BL studies from other online libraries which includes Web of Science, Scopus, etc. to investigate BL in its broad sense and how it affects students, lecturers and administration in a particular country or region.

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Blended learning: the new normal and emerging technologies

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• Anders Norberg 3 &
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This study addressed several outcomes, implications, and possible future directions for blended learning (BL) in higher education in a world where information communication technologies (ICTs) increasingly communicate with each other. In considering effectiveness, the authors contend that BL coalesces around access, success, and students’ perception of their learning environments. Success and withdrawal rates for face-to-face and online courses are compared to those for BL as they interact with minority status. Investigation of student perception about course excellence revealed the existence of robust if-then decision rules for determining how students evaluate their educational experiences. Those rules were independent of course modality, perceived content relevance, and expected grade. The authors conclude that although blended learning preceded modern instructional technologies, its evolution will be inextricably bound to contemporary information communication technologies that are approximating some aspects of human thought processes.

Introduction

Blended learning and research issues.

Blended learning (BL), or the integration of face-to-face and online instruction (Graham 2013 ), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage 2006 , p. 167) or the “new normal” in course delivery (Norberg et al. 2011 , p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell 2005 ), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed hybrid ) courses (Allen and Seaman 2003 ). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction with reduced in-class seat time for students ” (Lewis and Parsad 2008 , p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.

The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al. 2017 ). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE 2017 ). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al. 2016 ; Picciano et al. 2014 ; Picciano and Dziuban 2007 ; Bonk and Graham 2007 ; Kitchenham 2011 ; Jean-François 2013 ; Garrison and Vaughan 2013 ) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.

Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams 2016 ). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al. 2004 ), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig 1974 ; Arum et al. 2016 ). Although progress has been made by initiatives, such as, Quality Matters ( 2016 ), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY n.d. ), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium n.d. ), and SERVQUAL (Alhabeeb 2015 ), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle ( 2015 ), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig ( 1974 ) in his iconic volume on the nature of quality frames the context this way,

“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).

Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil 2017 ) terms surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart 1975 ).

Information communication technologies in society and education

Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi ( 2014 ) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi 2014 , p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi 2008 ). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi ( 2014 ) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.

Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi 2014 , 25; Norberg 2017 , 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour 2013 ) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al. 2016 ). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the what? and what for? The new normal for education will be in perpetual flux. Floridi’s ( 2014 ) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.

Literature of blended learning

A number of investigators have assembled a comprehensive agenda of transformative and innovative research issues for blended learning that have the potential to enhance effectiveness (Garrison and Kanuka 2004 ; Picciano 2009 ). Generally, research has found that BL results in improvement in student success and satisfaction, (Dziuban and Moskal 2011 ; Dziuban et al. 2011 ; Means et al. 2013 ) as well as an improvement in students’ sense of community (Rovai and Jordan 2004 ) when compared with face-to-face courses. Those who have been most successful at blended learning initiatives stress the importance of institutional support for course redesign and planning (Moskal et al. 2013 ; Dringus and Seagull 2015 ; Picciano 2009 ; Tynan et al. 2015 ). The evolving research questions found in the literature are long and demanding, with varied definitions of what constitutes “blended learning,” facilitating the need for continued and in-depth research on instructional models and support needed to maximize achievement and success (Dringus and Seagull 2015 ; Bloemer and Swan 2015 ).

Educational access

The lack of access to educational technologies and innovations (sometimes termed the digital divide) continues to be a challenge with novel educational technologies (Fairlie 2004 ; Jones et al. 2009 ). One of the promises of online technologies is that they can increase access to nontraditional and underserved students by bringing a host of educational resources and experiences to those who may have limited access to on-campus-only higher education. A 2010 U.S. report shows that students with low socioeconomic status are less likely to obtain higher levels of postsecondary education (Aud et al. 2010 ). However, the increasing availability of distance education has provided educational opportunities to millions (Lewis and Parsad 2008 ; Allen et al. 2016 ). Additionally, an emphasis on open educational resources (OER) in recent years has resulted in significant cost reductions without diminishing student performance outcomes (Robinson et al. 2014 ; Fischer et al. 2015 ; Hilton et al. 2016 ).

Unfortunately, the benefits of access may not be experienced evenly across demographic groups. A 2015 study found that Hispanic and Black STEM majors were significantly less likely to take online courses even when controlling for academic preparation, socioeconomic status (SES), citizenship, and English as a second language (ESL) status (Wladis et al. 2015 ). Also, questions have been raised about whether the additional access afforded by online technologies has actually resulted in improved outcomes for underserved populations. A distance education report in California found that all ethnic minorities (except Asian/Pacific Islanders) completed distance education courses at a lower rate than the ethnic majority (California Community Colleges Chancellor’s Office 2013 ). Shea and Bidjerano ( 2014 , 2016 ) found that African American community college students who took distance education courses completed degrees at significantly lower rates than those who did not take distance education courses. On the other hand, a study of success factors in K-12 online learning found that for ethnic minorities, only 1 out of 15 courses had significant gaps in student test scores (Liu and Cavanaugh 2011 ). More research needs to be conducted, examining access and success rates for different populations, when it comes to learning in different modalities, including fully online and blended learning environments.

Framing a treatment effect

Over the last decade, there have been at least five meta-analyses that have addressed the impact of blended learning environments and its relationship to learning effectiveness (Zhao et al. 2005 ; Sitzmann et al. 2006 ; Bernard et al. 2009 ; Means et al. 2010 , 2013 ; Bernard et al. 2014 ). Each of these studies has found small to moderate positive effect sizes in favor of blended learning when compared to fully online or traditional face-to-face environments. However, there are several considerations inherent in these studies that impact our understanding the generalizability of outcomes.

Dziuban and colleagues (Dziuban et al. 2015 ) analyzed the meta-analyses conducted by Means and her colleagues (Means et al. 2013 ; Means et al. 2010 ), concluding that their methods were impressive as evidenced by exhaustive study inclusion criteria and the use of scale-free effect size indices. The conclusion, in both papers, was that there was a modest difference in multiple outcome measures for courses featuring online modalities—in particular, blended courses. However, with blended learning especially, there are some concerns with these kinds of studies. First, the effect sizes are based on the linear hypothesis testing model with the underlying assumption that the treatment and the error terms are uncorrelated, indicating that there is nothing else going on in the blending that might confound the results. Although the blended learning articles (Means et al. 2010 ) were carefully vetted, the assumption of independence is tenuous at best so that these meta-analysis studies must be interpreted with extreme caution.

There is an additional concern with blended learning as well. Blends are not equivalent because of the manner on which they are configured. For instance, a careful reading of the sources used in the Means, et al. papers will identify, at minimum, the following blending techniques: laboratory assessments, online instruction, e-mail, class web sites, computer laboratories, mapping and scaffolding tools, computer clusters, interactive presentations and e-mail, handwriting capture, evidence-based practice, electronic portfolios, learning management systems, and virtual apparatuses. These are not equivalent ways in which to configure courses, and such nonequivalence constitutes the confounding we describe. We argue here that, in actuality, blended learning is a general construct in the form of a boundary object (Star and Griesemer 1989 ) rather than a treatment effect in the statistical sense. That is, an idea or concept that can support a community of practice, but is weakly defined fostering disagreement in the general group. Conversely, it is stronger in individual constituencies. For instance, content disciplines (i.e. education, rhetoric, optics, mathematics, and philosophy) formulate a more precise definition because of commonly embraced teaching and learning principles. Quite simply, the situation is more complicated than that, as Leonard Smith ( 2007 ) says after Tolstoy,

“All linear models resemble each other, each non nonlinear system is unique in its own way” (p. 33).

This by no means invalidates these studies, but effect size associated with blended learning should be interpreted with caution where the impact is evaluated within a particular learning context.

Study objectives

This study addressed student access by examining success and withdrawal rates in the blended learning courses by comparing them to face-to-face and online modalities over an extended time period at the University of Central Florida. Further, the investigators sought to assess the differences in those success and withdrawal rates with the minority status of students. Secondly, the investigators examined the student end-of-course ratings of blended learning and other modalities by attempting to develop robust if-then decision rules about what characteristics of classes and instructors lead students to assign an “excellent” value to their educational experience. Because of the high stakes nature of these student ratings toward faculty promotion, awards, and tenure, they act as a surrogate measure for instructional quality. Next, the investigators determined the conditional probabilities for students conforming to the identified rule cross-referenced by expected grade, the degree to which they desired to take the course, and course modality.

Student grades by course modality were recoded into a binary variable with C or higher assigned a value of 1, and remaining values a 0. This was a declassification process that sacrificed some specificity but compensated for confirmation bias associated with disparate departmental policies regarding grade assignment. At the measurement level this was an “on track to graduation index” for students. Withdrawal was similarly coded by the presence or absence of its occurrence. In each case, the percentage of students succeeding or withdrawing from blended, online or face-to-face courses was calculated by minority and non-minority status for the fall 2014 through fall 2015 semesters.

Next, a classification and regression tree (CART) analysis (Brieman et al. 1984 ) was performed on the student end-of-course evaluation protocol ( Appendix 1 ). The dependent measure was a binary variable indicating whether or not a student assigned an overall rating of excellent to his or her course experience. The independent measures in the study were: the remaining eight rating items on the protocol, college membership, and course level (lower undergraduate, upper undergraduate, and graduate). Decision trees are efficient procedures for achieving effective solutions in studies such as this because with missing values imputation may be avoided with procedures such as floating methods and the surrogate formation (Brieman et al. 1984 , Olshen et al. 1995 ). For example, a logistic regression method cannot efficiently handle all variables under consideration. There are 10 independent variables involved here; one variable has three levels, another has nine, and eight have five levels each. This means the logistic regression model must incorporate more than 50 dummy variables and an excessively large number of two-way interactions. However, the decision-tree method can perform this analysis very efficiently, permitting the investigator to consider higher order interactions. Even more importantly, decision trees represent appropriate methods in this situation because many of the variables are ordinally scaled. Although numerical values can be assigned to each category, those values are not unique. However, decision trees incorporate the ordinal component of the variables to obtain a solution. The rules derived from decision trees have an if-then structure that is readily understandable. The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood. The procedure produces tree-like rule structures that predict outcomes.

The model-building procedure for predicting overall instructor rating

For this study, the investigators used the CART method (Brieman et al. 1984 ) executed with SPSS 23 (IBM Corp 2015 ). Because of its strong variance-sharing tendencies with the other variables, the dependent measure for the analysis was the rating on the item Overall Rating of the Instructor , with the previously mentioned indicator variables (college, course level, and the remaining 8 questions) on the instrument. Tree methods are recursive, and bisect data into subgroups called nodes or leaves. CART analysis bases itself on: data splitting, pruning, and homogeneous assessment.

Splitting the data into two (binary) subsets comprises the first stage of the process. CART continues to split the data until the frequencies in each subset are either very small or all observations in a subset belong to one category (e.g., all observations in a subset have the same rating). Usually the growing stage results in too many terminate nodes for the model to be useful. CART solves this problem using pruning methods that reduce the dimensionality of the system.

The final stage of the analysis involves assessing homogeneousness in growing and pruning the tree. One way to accomplish this is to compute the misclassification rates. For example, a rule that produces a .95 probability that an instructor will receive an excellent rating has an associated error of 5.0%.

Implications for using decision trees

Although decision-tree techniques are effective for analyzing datasets such as this, the reader should be aware of certain limitations. For example, since trees use ranks to analyze both ordinal and interval variables, information can be lost. However, the most serious weakness of decision tree analysis is that the results can be unstable because small initial variations can lead to substantially different solutions.

For this study model, these problems were addressed with the k-fold cross-validation process. Initially the dataset was partitioned randomly into 10 subsets with an approximately equal number of records in each subset. Each cohort is used as a test partition, and the remaining subsets are combined to complete the function. This produces 10 models that are all trained on different subsets of the original dataset and where each has been used as the test partition one time only.

Although computationally dense, CART was selected as the analysis model for a number of reasons— primarily because it provides easily interpretable rules that readers will be able evaluate in their particular contexts. Unlike many other multivariate procedures that are even more sensitive to initial estimates and require a good deal of statistical sophistication for interpretation, CART has an intuitive resonance with researcher consumers. The overriding objective of our choice of analysis methods was to facilitate readers’ concentration on our outcomes rather than having to rely on our interpretation of the results.

Institution-level evaluation: Success and withdrawal

The University of Central Florida (UCF) began a longitudinal impact study of their online and blended courses at the start of the distributed learning initiative in 1996. The collection of similar data across multiple semesters and academic years has allowed UCF to monitor trends, assess any issues that may arise, and provide continual support for both faculty and students across varying demographics. Table  1 illustrates the overall success rates in blended, online and face-to-face courses, while also reporting their variability across minority and non-minority demographics.

While success (A, B, or C grade) is not a direct reflection of learning outcomes, this overview does provide an institutional level indication of progress and possible issues of concern. BL has a slight advantage when looking at overall success and withdrawal rates. This varies by discipline and course, but generally UCF’s blended modality has evolved to be the best of both worlds, providing an opportunity for optimizing face-to-face instruction through the effective use of online components. These gains hold true across minority status. Reducing on-ground time also addresses issues that impact both students and faculty such as parking and time to reach class. In addition, UCF requires faculty to go through faculty development tailored to teaching in either blended or online modalities. This 8-week faculty development course is designed to model blended learning, encouraging faculty to redesign their course and not merely consider blended learning as a means to move face-to-face instructional modules online (Cobb et al. 2012 ; Lowe 2013 ).

Withdrawal (Table  2 ) from classes impedes students’ success and retention and can result in delayed time to degree, incurred excess credit hour fees, or lost scholarships and financial aid. Although grades are only a surrogate measure for learning, they are a strong predictor of college completion. Therefore, the impact of any new innovation on students’ grades should be a component of any evaluation. Once again, the blended modality is competitive and in some cases results in lower overall withdrawal rates than either fully online or face-to-face courses.

The students’ perceptions of their learning environments

Other potentially high-stakes indicators can be measured to determine the impact of an innovation such as blended learning on the academy. For instance, student satisfaction and attitudes can be measured through data collection protocols, including common student ratings, or student perception of instruction instruments. Given that those ratings often impact faculty evaluation, any negative reflection can derail the successful implementation and scaling of an innovation by disenfranchised instructors. In fact, early online and blended courses created a request by the UCF faculty senate to investigate their impact on faculty ratings as compared to face-to-face sections. The UCF Student Perception of Instruction form is released automatically online through the campus web portal near the end of each semester. Students receive a splash page with a link to each course’s form. Faculty receive a scripted email that they can send to students indicating the time period that the ratings form will be available. The forms close at the beginning of finals week. Faculty receive a summary of their results following the semester end.

The instrument used for this study was developed over a ten year period by the faculty senate of the University of Central Florida, recognizing the evolution of multiple course modalities including blended learning. The process involved input from several constituencies on campus (students, faculty, administrators, instructional designers, and others), in attempt to provide useful formative and summative instructional information to the university community. The final instrument was approved by resolution of the senate and, currently, is used across the university. Students’ rating of their classes and instructors comes with considerable controversy and disagreement with researchers aligning themselves on both sides of the issue. Recently, there have been a number of studies criticizing the process (Uttl et al. 2016 ; Boring et al. 2016 ; & Stark and Freishtat 2014 ). In spite of this discussion, a viable alternative has yet to emerge in higher education. So in the foreseeable future, the process is likely to continue. Therefore, with an implied faculty senate mandate this study was initiated by this team of researchers.

Prior to any analysis of the item responses collected in this campus-wide student sample, the psychometric quality (domain sampling) of the information yielded by the instrument was assessed. Initially, the reliability (internal consistency) was derived using coefficient alpha (Cronbach 1951 ). In addition, Guttman ( 1953 ) developed a theorem about item properties that leads to evidence about the quality of one’s data, demonstrating that as the domain sampling properties of items improve, the inverse of the correlation matrix among items will approach a diagonal. Subsequently, Kaiser and Rice ( 1974 ) developed the measure of sampling adequacy (MSA) that is a function of the Guttman Theorem. The index has an upper bound of one with Kaiser offering some decision rules for interpreting the value of MSA. If the value of the index is in the .80 to .99 range, the investigator has evidence of an excellent domain sample. Values in the .70s signal an acceptable result, and those in the .60s indicate data that are unacceptable. Customarily, the MSA has been used for data assessment prior to the application of any dimensionality assessments. Computation of the MSA value gave the investigators a benchmark for the construct validity of the items in this study. This procedure has been recommended by Dziuban and Shirkey ( 1974 ) prior to any latent dimension analysis and was used with the data obtained for this study. The MSA for the current instrument was .98 suggesting excellent domain sampling properties with an associated alpha reliability coefficient of .97 suggesting superior internal consistency. The psychometric properties of the instrument were excellent with both measures.

The online student ratings form presents an electronic data set each semester. These can be merged across time to create a larger data set of completed ratings for every course across each semester. In addition, captured data includes course identification variables including prefix, number, section and semester, department, college, faculty, and class size. The overall rating of effectiveness is used most heavily by departments and faculty in comparing across courses and modalities (Table  3 ).

The finally derived tree (decision rules) included only three variables—survey items that asked students to rate the instructor’s effectiveness at:

Helping students achieve course objectives,

Creating an environment that helps students learn, and

Communicating ideas and information.

None of the demographic variables associated with the courses contributed to the final model. The final rule specifies that if a student assigns an excellent rating to those three items, irrespective of their status on any other condition, the probability is .99 that an instructor will receive an overall rating of excellent. The converse is true as well. A poor rating on all three of those items will lead to a 99% chance of an instructor receiving an overall rating of poor.

Tables  4 , 5 and 6 present a demonstration of the robustness of the CART rule for variables on which it was not developed: expected course grade, desire to take the course and modality.

In each case, irrespective of the marginal probabilities, those students conforming to the rule have a virtually 100% chance of seeing the course as excellent. For instance, 27% of all students expecting to fail assigned an excellent rating to their courses, but when they conformed to the rule the percentage rose to 97%. The same finding is true when students were asked about their desire to take the course with those who strongly disagreed assigning excellent ratings to their courses 26% of the time. However, for those conforming to the rule, that category rose to 92%. When course modality is considered in the marginal sense, blended learning is rated as the preferred choice. However, from Table  6 we can observe that the rule equates student assessment of their learning experiences. If they conform to the rule, they will see excellence.

This study addressed increasingly important issues of student success, withdrawal and perception of the learning environment across multiple course modalities. Arguably these components form the crux of how we will make more effective decisions about how blended learning configures itself in the new normal. The results reported here indicate that blending maintains or increases access for most student cohorts and produces improved success rates for minority and non-minority students alike. In addition, when students express their beliefs about the effectiveness of their learning environments, blended learning enjoys the number one rank. However, upon more thorough analysis of key elements students view as important in their learning, external and demographic variables have minimal impact on those decisions. For example college (i.e. discipline) membership, course level or modality, expected grade or desire to take a particular course have little to do with their course ratings. The characteristics they view as important relate to clear establishment and progress toward course objectives, creating an effective learning environment and the instructors’ effective communication. If in their view those three elements of a course are satisfied they are virtually guaranteed to evaluate their educational experience as excellent irrespective of most other considerations. While end of course rating protocols are summative the three components have clear formative characteristics in that each one is directly related to effective pedagogy and is responsive to faculty development through units such as the faculty center for teaching and learning. We view these results as encouraging because they offer potential for improving the teaching and learning process in an educational environment that increases the pressure to become more responsive to contemporary student lifestyles.

Clearly, in this study we are dealing with complex adaptive systems that feature the emergent property. That is, their primary agents and their interactions comprise an environment that is more than the linear combination of their individual elements. Blending learning, by interacting with almost every aspect of higher education, provides opportunities and challenges that we are not able to fully anticipate.

This pedagogy alters many assumptions about the most effective way to support the educational environment. For instance, blending, like its counterpart active learning, is a personal and individual phenomenon experienced by students. Therefore, it should not be surprising that much of what we have called blended learning is, in reality, blended teaching that reflects pedagogical arrangements. Actually, the best we can do for assessing impact is to use surrogate measures such as success, grades, results of assessment protocols, and student testimony about their learning experiences. Whether or not such devices are valid indicators remains to be determined. We may be well served, however, by changing our mode of inquiry to blended teaching.

Additionally, as Norberg ( 2017 ) points out, blended learning is not new. The modality dates back, at least, to the medieval period when the technology of textbooks was introduced into the classroom where, traditionally, the professor read to the students from the only existing manuscript. Certainly, like modern technologies, books were disruptive because they altered the teaching and learning paradigm. Blended learning might be considered what Johnson describes as a slow hunch (2010). That is, an idea that evolved over a long period of time, achieving what Kaufmann ( 2000 ) describes as the adjacent possible – a realistic next step occurring in many iterations.

The search for a definition for blended learning has been productive, challenging, and, at times, daunting. The definitional continuum is constrained by Oliver and Trigwell ( 2005 ) castigation of the concept for its imprecise vagueness to Sharpe et al.’s ( 2006 ) notion that its definitional latitude enhances contextual relevance. Both extremes alter boundaries such as time, place, presence, learning hierarchies, and space. The disagreement leads us to conclude that Lakoff’s ( 2012 ) idealized cognitive models i.e. arbitrarily derived concepts (of which blended learning might be one) are necessary if we are to function effectively. However, the strong possibility exists that blended learning, like quality, is observer dependent and may not exist outside of our perceptions of the concept. This, of course, circles back to the problem of assuming that blending is a treatment effect for point hypothesis testing and meta-analysis.

Ultimately, in this article, we have tried to consider theoretical concepts and empirical findings about blended learning and their relationship to the new normal as it evolves. Unfortunately, like unresolved chaotic solutions, we cannot be sure that there is an attractor or that it will be the new normal. That being said, it seems clear that blended learning is the harbinger of substantial change in higher education and will become equally impactful in K-12 schooling and industrial training. Blended learning, because of its flexibility, allows us to maximize many positive education functions. If Floridi ( 2014 ) is correct and we are about to live in an environment where we are on the communication loop rather than in it, our educational future is about to change. However, if our results are correct and not over fit to the University of Central Florida and our theoretical speculations have some validity, the future of blended learning should encourage us about the coming changes.

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The authors acknowledge the contributions of several investigators and course developers from the Center for Distributed Learning at the University of Central Florida, the McKay School of Education at Brigham Young University, and Scholars at Umea University, Sweden. These professionals contributed theoretical and practical ideas to this research project and carefully reviewed earlier versions of this manuscript. The Authors gratefully acknowledge their support and assistance.

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TEACHER'S CAPABILITIES, LEARNING EXPERIENCES, AND CHALLENGES IN BLENDED LEARNING IN THE NEW NORMAL EDUCATION

2022, IOER International Multidisciplinary Research Journal

The educational services worldwide are affected by COVID 19 pandemic. It brings numerous challenges, particularly to all. Thus, the Philippine public educational sectors decided to shift traditional teachinglearning into a blended learning modality. There are options such as online classes and modular distance learning as an alternative way for schools and teachers to render educational services to the students. Moreover, even teachers are still in the process of adjusting and improving their teaching practices and capabilities to deal with the current trend of teaching-learning modality. So, the researcher explored the teachers' capabilities, learning experience, and its challenges in blended learning in this new normal education. This descriptive research used a quantitative method wherein the teachers of Diplahan National High School served as respondents. They were chosen randomly through stratified random sampling to come up with 68 sample sizes. It utilized Pearson Product Moment Correlation, T-Test, and ANOVA as statistical tools. Results revealed that teachers in Diplahan National High School possess the necessary capabilities needed for the implementation of blended learning. The learning experiences of the teachers in blended learning are the following: the students are improving their ICT literacy skills, the students are responsive to queries, respectful, participative, and are paying attention. The respondents experienced these challenges: students with internet connectivity, poor internet connectivity, distance from home, too much auxiliary work, stress, and lack of sufficient time. More so, there is a significant effect of teachers' capabilities on the teacher's learning experiences in blended learning, particularly in the length of service. Consequently, regardless of the respondent's differences in terms of their age, years in service, and position, they have acquired some learning experiences in this blended learning.

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What is Blended Teaching?

Main navigation.

At Stanford, blended teaching describes in-person, classroom-based, synchronous instruction that incorporates elements of online learning, and technology-enhanced pedagogies. It is likely the most common and varied style of instruction.

Blended teaching can take many forms

The “blend” of blended teaching often includes different technology tools, pedagogic strategies, and learning experiences.

Some examples of blended courses include:

• A large lecture course where the lecture is recorded for students to rewatch later.
• A “flipped classroom" course where students read and watch assigned material  such as pre-recorded lecture videos, or assigned readings, as homework before in-person class sessions where they work together on group activities.
• A small seminar course where guest lecturers use Zoom to join the class.

Blended teaching is distinct from hybrid instruction

A blended course is distinct from a hybrid course. At Stanford, hybrid  specifically describes a course where some sessions take place in person and some sessions take place fully online . The in-person meetings may often include blended teaching elements.

Hybrid courses at Stanford have different requirements and policies associated with them, compared to blended teaching. If you are considering teaching a hybrid course, see  What is a Hybrid Course?  for more information.

A course where some sessions are regularly held in person, and other sessions are regularly held online is a hybrid course.

Stay centered on the learning experience

Because blended teaching covers such a wide range of activities, instructors using this framework can benefit from taking a broad view of their course and first considering the central questions of teaching and learning: 

• What are the most important things students should learn to do? 
• How will students demonstrate what they've learned?
• Which activities will help them master and demonstrate what they've learned? 
• What strategies and tools would best facilitate these activities?
• What will be the best use of classroom time? 
• What will be the best use of students’ focus time for homework? 

Benefits of blended teaching

A key benefit of blended teaching is that it foregrounds these pedagogic questions and provides a wide array of solutions. Indeed, we might think of these options as simply "technology-enhanced" versions of our own teaching.

A few specific benefits of such teaching are:

• Better access for all students (e.g., students can potentially access online materials at any time and from any place)
• More engaging learning (e.g., students can use class time for group activities or to ask questions)
• Saving time as an instructor (e.g., avoiding photocopying, using online grading options)

Finding the best blend for your course

Blended teaching is a combination of techniques and formats for different situations. The central question of blended teaching is: “What is the best strategy for your unique teaching situation?”

The answers depend on your preferred approach to teaching, your students, and the particular skills and subject matter that you teach. 

For example, learning activities that demand individual focus (e.g., listening to lectures) are generally good fits for online course environments. Activities that thrive on personal interaction (e.g., group work, Q&A, community building) are often most effective in face-to-face interactions. Courses with large numbers of students often benefit from technologies that increase access for students or streamline course management tasks. 

For this reason, blended teaching requires experimentation and adaptation. As you get started with blended teaching, and as you describe your teaching process to your students, it will be helpful to have a mindset of trying things and adjusting to what works best.

• Open access
• Published: 08 May 2024

Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

• Mariyamma Philip 1  

BMC Medical Education volume  24 , Article number:  512 ( 2024 ) Cite this article

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Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.

One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.

Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.

The study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

Peer Review reports

A PhD degree is a research degree, and research scholars submit a thesis based on original research in their chosen field. Doctor of Philosophy (PhD) degrees are awarded in a wide range of academic disciplines, and the PhD students are usually referred as research scholars. A comprehensive understanding of statistics allows research scholars to add rigour to their research. This approach helps them evaluate the current practices and draw informed conclusions from studies that were undertaken to generate their own hypotheses and to design, analyse and interpret complex clinical decisions. Therefore, intensive training at the beginning of the PhD journey is essential, as intensive training in research methodology and statistics in the early stages of research helps scholars design and plan their studies efficiently.

The University Grants Commission of India has taken various initiatives to introduce academic reforms to higher education institutions in India and mandated in 2009 that coursework be treated as a prerequisite for PhD preparation and that a minimum of four credits be assigned to one or more courses on research methodology, which could cover areas such as quantitative methods, computer applications, and research ethics. UGC also clearly states that all candidates admitted to PhD programmes shall be required to complete the prescribed coursework during the initial two semesters [ 1 ]. National Institute of Mental Health and Neurosciences (NIMHANS) at Bangalore, a tertiary care hospital and medical higher education institute in South India, that trains students in higher education in clinical fields, also introduced coursework in the PhD program for research scholars from various backgrounds, such as basic, behavioral and neurosciences, as per the UGC mandate. Research scholars undertake coursework programs soon after admission, which consist of several modules that include research methodology and statistical software training, among others.

Most scholars approach a course in statistics with the prejudice that statistics is uninteresting, demanding, complex or involve much mathematics and, most importantly, it is not relevant to their career goals. They approach statistics with considerable apprehension and negative attitudes, probably because of their inability to grasp the relevance of the application of the methods in their fields of study. This could be resolved by providing sufficient and relevant examples of the application of statistical techniques from various fields of medical research and by providing hands-on experience to learn how these techniques are applied and interpreted on real data. Hence, research methodology and statistical methods and the application of statistical methods using software have been given much importance and are taught as two modules, named Research Methodology and Statistics and Statistical Software Training, at this institute of medical higher education that trains research scholars in fields as diverse as basic, behavioural and neurosciences. Approximately 50% of the coursework curriculum focused on these two modules. Research scholars were thus given an opportunity to understand the theoretical aspects of the research methodology and statistical methods. They were also given hands-on training on statistical software to analyse the data using these methods and to interpret the findings. The coursework program was designed in this specific manner, as this intensive training would enable the research scholars to design their research studies more effectively and analyse their data in a better manner.

It is important to study attitudes toward statistics because attitudes are known to impact the learning process. Also, most importantly, these scholars are expected to utilize the skills in statistics and research methods to design research projects or guide postgraduate students and research scholars in the near future. Several authors have assessed attitudes toward statistics among various students and examined how attitudes affect academic achievement, how attitudes are correlated with knowledge in statistics and how attitudes change after a training program. There are studies on attitudes toward statistics among graduate [ 2 , 3 , 4 ] and postgraduate [ 5 ] medical students, politics, sociology, ( 6 – 7 ) psychology [ 8 , 9 , 10 ], social work [ 11 ], and management students [ 12 ]. However, there is a dearth of related literature on research scholars, and there are only two studies on the attitudes of research scholars. In their study of doctoral students in education-related fields, Cook & Catanzaro (2022) investigated the factors that contribute to statistics anxiety and attitudes toward statistics and how anxiety, attitudes and plans for future research use are connected among doctoral students [ 13 ]. Another study by Sohrabi et al. (2018) on research scholars assessed the change in knowledge and attitude towards teaching and educational design of basic science PhD students at a Medical University after a two-day workshop on empowerment and familiarity with the teaching and learning principles [ 14 ]. There were no studies that assessed changes in the attitudes or knowledge of research scholars across the PhD training period or after intensive training programmes such as PhD coursework. Even though PhD coursework has been established in institutes of higher education in India for more than a decade, there are no published research on the effectiveness of coursework from Indian universities or institutes of higher education.

This study aimed to determine the effectiveness of PhD coursework and whether intensive training programs such as PhD coursework can influence the knowledge and attitudes toward statistics of research scholars. Additionally, it would be interesting to know if the acquired knowledge could be retained longer, especially 2–3 years after the coursework, the crucial time of PhD data analysis. Hence, this study compares the scores of knowledge in statistics and attitude toward statistics of the research scholars at different time points of their PhD training, i.e., before, soon after and 2–3 years after the coursework.

Participants

This is an observational study of single group with repeated assessments. The institute offers a three-month coursework program consisting of seven modules, the first module is ethics; the fifth is research methodology and statistics; and the last is neurosciences. The study was conducted in January 2020. All research scholars of the institute who had completed PhD coursework in the last three years were considered for this study ( n  = 130). Knowledge and attitudes toward statistics before and soon after the coursework module were assessed as part of the coursework program. They were collected on the first and last day of the program respectively. The author who was also the coordinator of the research methodology and statistics module of the coursework have obtained the necessary permission to use the data for this study. The scholars invited to be part of the study by e-mail. Knowledge and attitude towards statistics 2–3 years after the coursework were assessed online using Google forms. They were also administered a semi structured questionnaire to elicit details about the usefulness of coursework. Participation was voluntary, and consent was also sought online. The confidentiality of the data was assured. Data were not collected from research scholars of Biostatistics or from research scholars who had more than a decade of experience or who had been working in the institute as faculty, assuming that their scores could be higher and could bias the findings. This non funded study was reviewed and approved by the Institute Ethics Committee.

Instruments

Knowledge in Statistics was assessed by a questionnaire prepared by the author and was used as part of the coursework evaluation. The survey included 25 questions that assessed the knowledge of statistics on areas such as descriptive statistics, sampling methods, study design, parametric and nonparametric tests and multivariate analyses. Right answers were assigned a score of 1, and wrong answers were assigned a score of 0. Total scores ranged from 0 to 25. Statistics attitudes were assessed by the Survey of Attitudes toward Statistics (SATS) scale. The SATS is a 36-item scale that measures 6 domains of attitudes towards statistics. The possible range of scores for each item is between 1 and 7. The total score was calculated by dividing the summed score by the number of items. Higher scores indicate more positive attitudes. The SAT-36 is a copyrighted scale, and researchers are allowed to use it only with prior permission. ( 15 – 16 ) The author obtained permission for use in the coursework evaluation and this study. A semi structured questionnaire was also used to elicit details about the usefulness of coursework.

Statistical analysis

Descriptive statistics such as mean, standard deviation, number and percentages were used to describe the socio-demographic data. General Linear Model Repeated Measures of Analysis of variance was used to compare knowledge and attitude scores across assessments. Categorical data from the semi structured questionnaire are presented as percentages. All the statistical tests were two-tailed, and a p value < 0.05 was set a priori as the threshold for statistical significance. IBM SPSS (28.0) was used to analyse the data.

One hundred and thirty research scholars who had completed coursework (CW) in the last 2–3 years were considered for the study. These scholars were sent Google forms to assess their knowledge and attitudes 2–3 years after coursework. 81 scholars responded (62%), and 4 scholars did not consent to participate in the study. The data of 77 scholars were merged with the data obtained during the coursework program (before and soon after CW). Socio-demographic characteristics of the scholars are presented in Table  1 .

The age of the respondents ranged from 23 to 36 years, with an average of 28.7 years (3.01), and the majority of the respondents were females (65%). Years of experience (i.e., after masters) before joining a PhD programme ranged from 0.5 to 9 years, and half of them had less than three years of experience before joining the PhD programme (median-3). More than half of those who responded were research scholars from the behavioural sciences (55%), while approximately 30% were from the basic sciences (29%).

General Linear Model Repeated Measures of Analysis of variance was used to compare the knowledge and attitude scores of scholars before, soon after and 2–3 after the coursework (will now be referred as “later the CW”), and the results are presented below (Table  2 ; Fig.  1 ).

Comparison of knowledge and attitude scores across the assessments. Later the CW – 2–3 years after the coursework

The scores for knowledge and attitude differed significantly across time. Scores of knowledge and attitude increased soon after the coursework; the percentage of change was 77% and 43% respectively. However, significant reductions in knowledge and attitude scores were observed 2–3 years after the coursework compared to scores soon after the coursework. The reduction was higher for attitude scores; knowledge and attitude scores have decreased by 10% and 37% respectively. The change in scores across assessments is evident from the graph, and clearly the effect size is higher for attitude than knowledge.

The scores of knowledge or attitude before the coursework did not significantly differ with respect to gender or age or were not correlated with years of experience. Hence, they were not considered as covariates in the above analysis.

A semi structured questionnaire with open ended questions was also administered to elicit in-depth information about the usefulness of the coursework programme, in which they were also asked to self- rate their knowledge. The data were mostly categorical or narratives. Research scholars’ self-rated knowledge scores (on a scale of 0–10) also showed similar changes; knowledge improved significantly and was retained even after the training (Fig.  2 ).

Self-rated knowledge scores of research scholars over time. Later the CW – 2–3 years after the coursework

The response to the question “ How has coursework changed your attitude toward statistics?”, is presented in Fig.  3 . The responses were Yes, positively, Yes - Negatively, No change – still apprehensive, No change – still appreciate, No change – still hate statistics. The majority of the scholars (70%) reported a positive change in their attitude toward statistics. Moreover, none of the scholars reported negative changes. Approximately 9% of the scholars reported that they were still apprehensive about statistics or hate statistics after the coursework.

How has coursework changed your attitude toward statistics?

Those scholars who reported that they were apprehensive about statistics or hate statistics noted the complexity of the subject, lack of clarity, improper instructions and fear of mathematics as major reasons for their attitude. Some responses are listed below.

“The statistical concepts were not taught in an understandable manner from the UG level” , “I am weak in mathematical concepts. The equations and formulae in statistics scare me”. “Lack of knowledge about the importance of statistics and fear of mathematical equations”. “The preconceived notion that Statistics is difficult to learn” . “In most of the places, it is not taught properly and conceptual clarity is not focused on, and because of this an avoidance builds up, which might be a reason for the negative attitude”.

Majority of the scholars (92%) felt that coursework has helped them in their PhD, and they were happy to recommend it for other research scholars (97%). The responses of the scholars to the question “ How was coursework helpful in your PhD journey ?”, are listed below.

“Course work gave a fair idea on various things related to research as well as statistics” . “Creating the best design while planning methodology, which is learnt form course work, will increase efficiency in completing the thesis, thereby making it faster”. “Course work give better idea of how to proceed in many areas like literature search, referencing, choosing statistical methods, and learning about research procedures”. “Course work gave a good idea of research methodology, biostatistics and ethics. This would help in writing a better protocol and a better thesis”. “It helps us to plan our research well and to formulate, collect and plan for analysis”. “It makes people to plan their statistical analysis well in advance” .

This study evaluated the effectiveness of the existing coursework programme in an institution of higher medical education, and investigated whether the coursework programme benefits research scholars by improving their knowledge of statistics and attitudes towards statistics. The study concluded that the coursework program was beneficial for improving scholars’ knowledge about statistics and attitudes toward statistics.

Unlike other studies that have assessed attitudes toward statistics, the study participants in this study were research scholars. Research scholars need extensive training in statistics, as they need to apply statistical tests and use statistical reasoning in their research thesis, and in their profession to design research projects or their future student dissertations. Notably, no studies have assessed the attitudes or knowledge of research scholars in statistics either across the PhD training period or after intensive statistics training programs. However, the findings of this study are consistent with the findings of a study that compared the knowledge and attitudes toward teaching and education design of PhD students after a two-day educational course and instructional design workshop [ 14 ].

Statistics educators need not only impart knowledge but they should also motivate the learners to appreciate the role of statistics and to continue to learn the quantitative skills that is needed in their professional lives. Therefore, the role of learners’ attitudes toward statistics requires special attention. Since PhD coursework is possibly a major contributor to creating a statistically literate research community, scholars’ attitudes toward statistics need to be considered important and given special attention. Passionate and engaging statistics educators who have adequate experience in illustrating relatable examples could help scholars feel less anxious and build competence and better attitudes toward statistics. Statistics educators should be aware of scholars’ anxiety, fears and attitudes toward statistics and about its influence on learning outcomes and further interest in the subject.

Strengths and limitations

Analysis of changes in knowledge and attitudes scores across various time points of PhD training is the major strength of the study. Additionally, this study evaluates the effectiveness of intensive statistical courses for research scholars in terms of changes in knowledge and attitudes. This study has its own limitations: the data were collected through online platforms, and the nonresponse rate was about 38%. Ability in mathematics or prior learning experience in statistics, interest in the subject, statistics anxiety or performance in coursework were not assessed; hence, their influence could not be studied. The reliability and validity of the knowledge questionnaire have not been established at the time of this study. However, author who had prepared the questionnaire had ensured questions from different areas of statistics that were covered during the coursework, it has also been used as part of the coursework evaluation. Despite these limitations, this study highlights the changes in attitudes and knowledge following an intensive training program. Future research could investigate the roles of age, sex, mathematical ability, achievement or performance outcomes and statistics anxiety.

The study concluded that a rigorous and intensive training program such as PhD coursework was beneficial for improving knowledge about statistics and attitudes toward statistics. However, the significant reduction in attitude and knowledge scores after 2–3 years of coursework indicates that a refresher program might be helpful for research scholars as they approach the analysis stage of their thesis. Statistics educators must develop innovative methods to teach research scholars from nonstatistical backgrounds. They also must be empathetic to understanding scholars’ anxiety, fears and attitudes toward statistics and to understand its influence on learning outcomes and further interest in the subject.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The author would like to thank the participants of the study and peers and experts who examined the content of the questionnaire for their time and effort.

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This study used data already collected data (before and soon after coursework). The data pertaining to knowledge and attitude towards statistics 2–3 years after coursework were collected from research scholars through the online survey platform Google forms. The participants were invited to participate in the survey through e-mail. The study was explained in detail, and participation in the study was completely voluntary. Informed consent was obtained online in the form of a statement of consent. The confidentiality of the data was assured, even though identifiable personal information was not collected. This non-funded study was reviewed and approved by NIMHANS Institute Ethics Committee (No. NIMHANS/21st IEC (BS&NS Div.)

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Philip, M. Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework. BMC Med Educ 24 , 512 (2024). https://doi.org/10.1186/s12909-024-05487-y

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Mom delivers baby in car hours before defending her Rutgers doctoral thesis

• Updated: May. 08, 2024, 3:05 p.m. |
• Published: May. 08, 2024, 11:30 a.m.

Tamiah Brevard-Rodriguez delivered her son, Enzo, hours before defending her dissertation at the Rutgers-New Brunswick Graduate School of Education. Nick Romanenko/Rutgers University

• Tina Kelley | NJ Advance Media for NJ.com

Giving birth and defending a doctoral dissertation could easily be considered among the most stressful items on a bucket list. For Tamiah Brevard-Rodriguez, it was all in a day’s work. One day’s work.

She even grabbed a shower in between.

On March 24, Brevard-Rodriguez, director of Aresty Research Center at Rutgers University, was finishing up preparations for her doctoral defense the next day. Eight months pregnant with her second child, she didn’t feel terrific, but she persisted.

She was trying to hone down to 20 minutes her remarks on “The Beauty Performances of Black College Women: A Narrative Inquiry Study Exploring the Realities of Race, Respectability, and Beauty Standards on a Historically White Campus.” The Zoom link had gone out to family, friends, and colleagues for the defense, scheduled for 1 p.m. the next day.

“Operation Dissertation before Baby,” as she called it, was a go.

But at 2:15 a.m. on March 25 her water broke, a month and a day early.

As the contractions came closer and closer, her wife drove her down the Garden State Parkway, trying to get to Hackensack Meridian Mountainside Medical Center in Montclair before Baby Enzo showed up.

But the baby was faster than a speeding Maserati and arrived in the front seat at 5:55 a.m., after just three pushes. He weighed in at 5-pounds 12-ounces, 19 inches long, and in perfect health for a baby four weeks early.

“I did have to detail her car afterward,” the new mom said of her wife.

Brevard-Rodriguez was feeling so good after the birth that she decided against asking to reschedule her thesis defense.

“I had more than enough time to regroup, shower, eat and proceed with the dissertation,” she said. She had a quick nap, too. The doctors and nurses supported her decision and made sure she had access to reliable wifi at the hospital.

She gave her defense with a Rutgers background screen. When she learned she had passed, she dropped the fake background, and people could see Brevard-Rodriguez in her maternity bed, and Enzo in her wife’s arms.

“I said, ‘You guys missed the big news,’ and they just fell out,” said Brevard-Rodriguez, who waited for the reveal because she didn’t want extra sympathy from her dissertation committee.

Melina Mangin, chair of the Educational Theory, Policy & Administration Department at the Graduate School of Education, was astounded.

“Tamiah had delivered a flawless defense with zero indication that she had just given birth,” she said. “She really took the idea of productivity to the next level!”

Finishing her doctorate in education and having her last child were fitting 40th birthday presents to herself, Brevard-Rodriguez said. She turned 40 in November and returns to work in late August.

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