technology education and early childhood

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Using Technology to Enhance Early Childhood Education

Edtech can advance learning goals and remove barriers to applying concepts, enhancing kids’ hands-on explorations.

The pandemic accelerated the movement toward using technology at all levels of teaching and learning. While schools and homes were inundated with options of what educational technology to use, often the questions of why and when to use it were glossed over. Especially in early childhood, we must examine the questions of what technology trades off with in the learning environment and what nontechnological pedagogies and tools we must protect.

High-quality instruction takes place within a specific context, so how can we weigh the affordances of learning with and without edtech to balance the needs of early childhood teachers, their students, and their families? 

Not To Tech

Using technology and media in learning, especially in early childhood, should never replace opportunities to build the critical relationships that form the foundation of a learning environment. For a child to learn, they must feel connected and valued within a community that recognizes strengths and challenges of all types, and that makes experimentation and mistakes normal and safe. Technology and media do not replace a caring interaction that demonstrates these values. Digital resources should therefore be used sparingly and for specified purposes that would be hard to accomplish without them. 

Unfortunately, much of edtech is often purchased and delivered to educators without such considerations. In a recent study, we found that delivering even high-quality technology and media without support actually interfered with instruction and led to less time learning than if no tech had been used at all.

However, when teachers were provided with a curriculum that utilized video clips, online games, and apps to introduce and practice specific skills, and then used these resources in whole group and small group interactions to spark discussion and collaborative problem-solving, students learned more, teachers spent more instructional time focused on the learning goals, and teachers felt more confident in supporting learning of the content. 

At the Center for Children and Technology at the Education Development Center , we focus on engaging students, teachers, and families in learning through high-quality, play-based learning initiatives that utilize and integrate digital resources . Research demonstrates that edtech should be used only when it utilizes research-backed pedagogy, such as asking participants open-ended questions, providing multiple ways of engaging with a task, offering opportunities for collaboration between participants, and specifying tailored and in-the-moment feedback that drives new thinking or strategies.

All edtech resources should ensure that students are engaged in learning as it unfolds most naturally, by driving exploration and discovery through their own questions, at their own pace, and in their own ways. Therefore, we design and integrate edtech in service of the learners and the learning environment.

For example, the Preschool Data Toolbox App (free download in the App Store or on Google Play ) helps young children learn about data collection and analysis. In a classroom setting, the time spent collecting and organizing data, and creating accurate and clear representations, is a significant barrier to teaching students about data. Simplifying and speeding up this process frees up valuable time for understanding and “reading” graphs. 

At left, a quick question about a class’s favorite fruits is made into a graph, which could be used as part of a lesson about healthy eating or what children bring in their lunches. However, the app is not meant to be used exclusively to teach about data. Children still explore their environments with hands-on materials and physical movement, ask questions, and consider when collecting data can help answer questions. They create object and picture graphs and compare them with bar graphs in and out of the app. In this way, the app can facilitate and scaffold learning about data and analysis, and connect with other areas of the curriculum. 

Similarly, in  Early Science with Nico & Nor , developed in partnership with Digital Promise, SRI Education, and WGBH,  Coconut Star  is used to experiment with variables like force, incline, and surface material as a coconut is pushed and rolled to a target. Preschoolers can adjust inclines when the coconut rolls onto grass versus dirt versus metal, as well as the power of the kick that launches the coconut, and then get feedback about whether the coconut rolled too far, didn’t roll far enough, or reached the target. While the app does not replace experimenting with conditions of a ramp setup in real life, it does offer a controlled environment for more precise representations of scientific phenomena. 

Each of these edtech examples uses technology intentionally to advance learning goals and remove barriers to seeing and applying concepts. But these resources never replace the hands-on explorations and child-driven questions and discoveries that unfold in a vibrant classroom. 

Thus, when edtech is used in isolation, without consideration of learning goals and without integration into the larger curriculum or classroom structure, it is better not to tech. But when edtech is used in ways that enhance nontech materials and methods, they can be used productively to address specific learning goals in ways that align with typical classroom routines. 

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Incorporating Technology into Instruction in Early Childhood Classrooms: a Systematic Review

Claire donehower paul.

College of Education and Human Development, Department of Learning Sciences, Georgia State University, 30 Pryor St SW, Atlanta, GA 30303 USA

Sarah G. Hansen

Chelsea marelle, melinda wright.

The purpose of this review is to describe the variety and effectiveness of instructional technologies used in the early childhood setting.

A systematic review of three databases was completed, and studies were reviewed by two independent coders to determine if they met inclusion criteria. Studies were excluded from this review if (a) the technology was used to train teachers and was not directly used with early childhood students, (b) participants were all enrolled in 2nd grade or higher, (c) the setting was not an early childhood education setting, or (d) studies were descriptive in nature or utilized a survey methodology. Data were extracted from each article related to participant characteristics, setting characteristics, research design, technology type, and dependent variables.

Thirty-five studies met criteria were included in this review. A wide range of technologies were used to provide or facilitate instruction on (a) academics, (b) social and communication skills, and (c) cognitive skills. Academic outcomes targeted in Head Start preschools were the most common across studies. The results ranged from no effect to highly effective.

Conclusions

The findings from the included studies varied widely in their outcomes from reporting no difference between traditional instruction and technology-aided instruction to reporting significant difference between groups or reporting a functional relation between the technology-based intervention and the target behavior or skill. Studies that included students identified with neurodevelopmental disorders demonstrated a positive impact in the outcomes of students who experience an intervention that included technology-aided instruction. Future research is needed to identify critical components of effective technology-based interventions in early childhood educational settings.

Decades of research indicate that access to high-quality early learning environments is predictive of later success (Guralnick, 1991 ; Ramey & Ramey, 1994 ; White, 1985 ). High-quality early childhood education (i.e., education for children ages 3–8) is associated with superior academic achievement throughout the lifespan. In fact, a meta-analysis of studies that examined the longitudinal effects of early childhood education reported moderate effect sizes of preschool programs on academic skills all the way through eighth grade (i.e., d = 0.30), with similar effects in social communication ( d = 0.27) that persisted into high school ( d = 0.33; Nelson et al., 2003 ). A more recent study found that participants of a rigorous preschool program following the Montessori model showed minimized differences between children at program exit who had been behind at program entry, indicating that high-quality preschool may be sufficient for children at risk to catch up to peers (Lillard et al., 2017 ). On a range of measures, the benefit of high-quality early childhood education environments has remained evident.

Recently, high-tech elements have become part of the preschool learning environment (Northrop & Killeen, 2013 ; Reeves et al., 2017 ; Rodgers et al., 2016 ). In the past, technology has been a controversial addition to early childhood settings, with parent and educator concerns about long-term screen time effects governing policy on the presence of technology in classrooms (Blackwell et al., 2013 ; Jeong & Kim, 2017 ; Parette et al., 2010 ). A major change came in 2012, when the National Association for the Education of young Children (NAEYC) and the Fred Rodgers Center published a joint position statement on the use of technology in early childhood classrooms and instruction (NAEYC & Fred Rodgers Center, 2012 ). This statement signaled a shift in thinking about how technology could be incorporated into high-quality early childhood programs and paired with an increase in available technology, which allowed for a rapid increase of technology in quality-rated classrooms. In a 2018 study of early childhood educators, 89% of respondents reported having Internet access in their classroom, 81% had a desktop computer, 71% tablets, and 30% an interactive whiteboard (Pila et al., 2019 ). These results indicate as much as a threefold increase in student access to technology in the last 6 years. The results of the survey further indicated that early childhood educators had noticed an increase in access to technology in their classroom in the last 3 years and that their rating of the acceptability of the technology remained consistently neutral to high (Pila et al., 2019 ).

A diverse array of technologies can be found in today’s early childhood programs. Over 90% of respondents to a survey on technology in preschool reported using some form of technology in the preschool classroom (Pila et al., 2019 ). The technology used in early childhood classrooms ranges all the way from technology specific to classroom settings, like digital whiteboards (30%), to more ubiquitous technology like Internet-enabled computers (89%) and televisions (63%) (Pila et al., 2019 ). Despite the broadening role of technology in early childhood, there are limited resources available to ensure the technology is being used to its optimal benefit. In a qualitative analysis of teacher perceptions of ease of use and observations uptake of instructional technology in preschool classrooms in Shanghai, China, Dong ( 2016 ) found that while teachers are observed to be using more technology in their classrooms, they do not always feel they can keep pace with ever-changing technology, a finding supported by more than one survey of teachers about technology use at school (e.g., Mertala, 2019 ; Yildiz Durak, 2021 ).

In educational settings serving individuals with neurodevelopmental disabilities across the lifespan, technology-based interventions are more established. For instance, video modeling is considered an evidence-based practice for individuals with autism spectrum disorders (ASD), and computer-based instruction has shown good effect for addressing learning disabilities (Park et al., 2019 ; Zavaraki et al., 2019 ). Evidence-based practices for improving communication, functional life skills, and addressing challenging behavior have all been modified to contain technology and continued to be effective (e.g., LeJeune et al., 2021 ; Light et al., 2019 ; Schmidt & Glaser, 2021 ). Despite their evident effectiveness, technology-based interventions have been differentially applied to individuals with disabilities outside of the classroom setting, especially in the younger ages (Lancioni et al., 2019 ; Lynch et al., 2022 ). The utility of technology in early childhood settings to improve outcomes for individuals with and without neurodevelopmental disabilities needs to be assessed.

There is increased capacity to deliver evidenced-based intervention and teaching strategies by capitalizing on technology, as evidenced by consistent innovations in both technology and education. These innovations are promising, but so far documented by mostly exploratory or descriptive research (see Su et al., 2022 , for examples). An analysis of experimental research to measure the effectiveness of technological innovations in the early childhood education setting is warranted. Of particular interest is the adaptation of typical early childhood educational practices (i.e., developmentally appropriate practice; Copple et al., 2013 ) to include technology. For example, circle-times or morning meetings transformed by a movement video on the whiteboard, or an investigation or space enriched by the NASA website. There is some evidence to suggest that technological advances in the classroom can have a positive effect in academic or pre-academic gains, such as early literacy skills (Meadan et al., 2008 ; Parette et al., 2009 ). Despite the potential promise of adaptations of current practices to include technology, there is little guidance on how to effectively implement.

Technology in early childhood, as in the world at large, is rapidly changing. Previous reviews have examined specific pieces of technology such as tablets (Couse & Chen, 2010 ; Neumann, 2018 ; Neumann & Neumann, 2014 ) and literacy aides (Jamshidifarsani et al., 2019 ) and found mixed results of these interventions. Previous reviews have also aimed to describe the effects of technology on instruction more generally, as well as perceptions of technology in early childhood curricula (Wang et al., 2010 ; Sarker et al., 2019 ). Neither of these previous reviews partitioned out the efficacy for individuals with developmental disabilities. Finally, technology as a support for individuals with developmental disabilities is robust in the literature (e.g., Sun et al., 2022 ; Ramdoss et al., 2011 ). While the results of these reviews demonstrate potential for these technologies, they report results for a range of settings and ages. To our knowledge, previous reviews of the literature have not addressed classroom technology in early childhood related to children with and without disabilities. The current review aims to extend the previously available reviews and address the following research questions: (a) How is technology used in instruction in early childhood educational settings? and (b) What is the effect of technologies used in instruction in early childhood settings for students with and without disabilities?

Systematic searches were completed using three electronic databases: Education Resources Information Center (ERIC), Academic Search Premier, and PsycINFO. In all databases, the following search term combinations were entered (“technology” or “smartboard” or “whiteboard” or “tablet” or “digital”) and (“early childhood” or “preschool” or “early learning”) and (“intervention” or “curriculum” or “instruction”) in the keywords field. Only peer-reviewed articles from 2013 to 2021 were included in this systematic review.

Across all three databases, 45,455 results were returned. With duplicates removed, there were 1468 abstracts for review. The abstracts of the returned studies were reviewed, and those studies using an experimental or quasi-experimental design were retained. All searches by the second author took place in May 2020. Reliability searches completed by the fourth author took place in June 2020. Reliability coding and discussion among coauthors took place in August 2020. Following reviewer feedback, the search was extended in April of 2022 to include the years 2020–2021. A total of 140 abstracts were selected for potential inclusion before examination with the inclusion criterion. Initial search procedures were conducted by the fourth author for years 2013–2020 and by the third author for years 2020–2021. For a visual of the search procedures, please see Fig. ​ Fig.1 1 .

Article search and selection procedure

Reliability

Reliability procedures on inclusion/exclusion were conducted by the second author. The written search procedures were replicated by the fourth author. A 100% agreement was reached between the second and fourth author on inclusion and exclusion decisions following a retraining session to remediate a misunderstanding. This procedure was replicated between the second and third authors for the additional year with 98% agreement. The article with a disagreement was removed. For the years 2020–2021, there was one disagreement, which was discussed, and an agreement was reached. Reliability on data extraction was completed for 30% of included studies. Reliability on data extraction was 100%.

In order to be included in this review, each study needed to meet a set of inclusion criteria. Requirements for inclusion were (a) publication in an English language peer-reviewed journal between the years 2013 and 2021; (b) utilized an experimental or quasi-experimental design; (c) the intervention took place in a pre-K, kindergarten, or 1st grade educational setting; and (d) included technology as a part of the independent variable. Studies were excluded from this review if (a) the technology was used to train teachers and was not directly used with early childhood students (e.g., Aldimir, 2016 ); (b) participants were all enrolled in 2nd grade or higher (e.g., Gardner et al., 2015 ); (c) the setting was not an early childhood education setting (e.g., clinical setting; Gilliver et al., 2016 ); and (d) studies were descriptive or exploratory in nature or utilized a survey methodology (e.g., Danby et al., 2016 ).

Data Analyses

Data were initially extracted by the first and third authors using a spreadsheet created for this review. Data were reported on the following variables: (a) participants (number, age, gender, race/ethnicity, disability); (b) setting; (c) research design; (d) independent variable (technology used); (e) dependent variables; and (f) results. The dependent variables for the included studies were categorized as (a) academic and cognitive skills (e.g., general knowledge, early literacy skills, early mathematics skills, vocabulary skills, cognitive skills); (b) social communication skills(e.g., requesting, commenting, social exchanges); (c) social-emotional skills (e.g., social-emotional learning, behavior); (d) engagement; (e) programming skills; (f) motor skills; and (g) attitudes and perceptions towards the technology and/or intervention.

Thirty-five studies met criteria and were included in this review of technology use in early childhood settings. Data were extracted from each article related to participant characteristics, setting characteristics, research design, technology type, and dependent variables (see Tables ​ Tables1 1 and ​ and2 2 ).

Participant and setting characteristics

Research design, independent variables and dependent variables

Participant Characteristics

Across the 35 included studies, there were 4720 participants with sample sizes ranging from 3 to 766 participants. Ages of participants ranged from 2 to 7 years old. In several studies, only a mean age was reported (Desoete & Praet, 2013 ; Hsiao & Chen, 2016 ; Lee & Tu, 2016 ; Maureen et al., 2018 ) while one study reported a grade range (i.e., K-2nd grade; Kim et al., 2019 ). Participant gender was reported in 29 of the included studies (83%). For the 29 studies that reported participant gender, 2183 participants were male, and 2176 were female. Race and/or ethnicity were reported in only 10 of the 35 studies (28%). For the ten studies that included this data, 475 participants identified as Caucasian, 790 identified as Hispanic, 230 identified as African American, 36 identified as Asian, 133 identified as multi-racial, and 127 as Other. Fourteen of the included studies (40%) included information on the disability status of their participants. Four studies reported participants with autism spectrum disorder (Cardon et al., 2019 ; Dueñas et al., 2021 ; Jung & Sainato, 2015 ; Pellegrino et al., 2020 ); three studies reported participants with intellectual disability or developmental disabilities (Boyle et al., 2021 ; Chai, 2017 ; Taylor, 2018 ); two studies reported participants with specific learning disability and speech language impairment or learning disabilities in general (Amorim et al., 2020 ; Musti-Rao et al., 2015 ); and four studies reported participants as having an IEP or an unspecified qualification for special education (Kim et al., 2019 ; Lee & Tu, 2016 ; Sullivan & Bers, 2018 ; Wilkes et al., 2020 ). One study reported including students “at risk” for mathematics difficulty (Desoete & Praet, 2013 ). Finally, five studies reported that they did not include students with disabilities (Dennis et al., 2016 ; Maureen et al., 2018 ; McCoy et al., 2017 ; Oades-Sese et al., 2021 ; Simsek & Isikoglu Erdogan, 2021 ).

Setting Characteristics

In order to be included in this review, the studies needed to take place in an early childhood education setting (see Table ​ Table1). 1 ). Thirty-one studies (89%) took place in a Head Start, preschool, or early elementary school classroom. Three studies (9%) took place in a 1:1 setting within a school (e.g., table between classrooms or empty room; Chai, 2017 ; Dennis et al., 2016 ; Taylor, 2018 ), and one study (3%) took place as part of a summer reading program (Kim et al., 2019 ).

Research Design

The included studies utilized a variety of experimental and quasi-experimental designs to evaluate the impact of the technology on targeted skills in early childhood education settings (see Table ​ Table2). 2 ). Eleven (31%) of the included studies were randomized control trials, ten (29%) utilized a single-case design, nine (26%) utilized quasi-experimental designs, three utilized a mixed methods design, one utilized a SMART design, and one utilized a pre-post (no control group) design.

Technology Type

A variety of technologies were used in the studies including tablets (40%), computers/computer games (17%), robots (9%), projection devices (6%), video models (6%), whiteboards (3%), and motion sensor 3D camera scanner (ASUS Xtion Pro; 3%). Several studies utilized more than one type of technology (Martín et al., 2017 ; Papadakis et al., 2018 ). The intervention protocols varied widely across the included studies including teacher-directed or independent student interaction with iPad apps or computer games (Aunio & Mononen, 2018 ; Chai, 2017 ; Dennis et al., 2016 ; Desoete & Praet, 2013 ; Hsiao & Chen, 2016 ; Lee & Tu, 2016 ; Musti-Rao et al., 2015 ; Papadakis et al., 2018 ; Schacter & Jo,  2017 ; Vatalaro et al., 2017 ), explicit instruction protocols (Taylor, 2018 ), video modeling protocols (Cardon et al., 2019 ; Jung & Sainato, 2015 ), e-books with embedded vocabulary supports (Korat et al., 2017 ), and digital storytelling (Maureen et al., 2018 ; Maureen et al., 2020 ).

Independent Variables

A majority (21, 60%) of the included studies had independent variables that were the same as the technology used, for example, an instructional video game or a tablet app. The technology used was only coded as the independent variable if no other information was given about intervention components. The remaining 14 studies (40%) included the following: social communication intervention (2, 14%), vocabulary instruction (2, 14%), math instruction (2, 14%), peer mediated instruction (2, 14%), inquiry-based science learning (2, 14%), leveled literacy intervention (1, 7%), project-based learning (1, 7%), story-based learning (2, 14%), and flashcards (2, 14%).

Dependent Variables

Several skill areas were addressed in the 35 included studies: academic and cognitive skills (e.g., general knowledge, early literacy skills, early mathematics skills, vocabulary skills, auditory processing, logic, and reasoning), social communication skills, social-emotional skills, engagement, programming skills, and motor skills. Additionally, several studies measured students’ attitudes and perceptions towards the technology and/or intervention (Martín et al., 2017 ; Sullivan & Bers, 2018 ). Academic and cognitive skills were targeted in 28 studies (80%), while social communication skills were only addressed in three studies (9%). Academic engagement and programming skills were each addressed in three studies (9%), while motor skill and social-emotional skills were addressed in only one study (3%).

The included studies reported results that ranged from no effect to significant effects of the technology-based intervention. For studies that compared a technology intervention to a traditional intervention, seven reported improvements across both groups from pre-test to posttest with no significant difference between the group who had access to the technology-based intervention and the control group (Aunio & Mononen, 2018 ; Furman et al. 2019 ; Oades-Sese et al., 2021 ; Outhwaite et al., 2020 ; Pan et al., 2021 ; Redondo et al., 2020 ; Simsek & Isikoglu Erdogan, 2021 ), while thirteen studies reported significant differences between technology and control groups with the group who had access to the technology-based intervention outperforming the control group on outcome measure (Amorim et al., 2020 ; Desoete & Praet, 2013 ; Hsiao & Chen, 2016 ; Korat et al., 2017 ; Martín et al., 2017 ; Maureen et al., 2018 ; Maureen et al., 2020 ; Muñoz-Repiso & Caballero-González, 2019 ; Papadakis et al., 2018 ; Schacter & Jo,  2017 ; Sullivan & Bers, 2018 ; Tang, 2020 ; Vatalaro et al., 2017 ; Wilkes et al., 2020 ). One study showed varying results across control and intervention groups, meaning that one group performed higher on certain skills than the other and vice-versa (Elimelech & Aram, 2020 ). For the included studies that employed a single-case research design, the results were mixed as well with most studies reporting an increase in target academic skills (Boyle et al., 2021 ; Chai, 2017 ; Musti-Rao et al., 2015 ), programming skills (Taylor, 2018 ), and engagement (McCoy et al., 2017 ) or social skills (Dueñas et al., 2021 ; Jung & Sainato, 2015 ; Pellegrino et al., 2020 ), while a few reported no clear, functional relation between the intervention and dependent variable (Cardon et al., 2019 ; Dennis et al., 2016 ).

Results of this review indicate a large range of interventions, devices, and intervention targets for the inclusion of technology in early childhood settings. Results clearly indicate robust use of technology in the education of young children with and without disabilities across the included years. In the 35 included studies, there were interventions across both academic and social communication-dependent variables, although most studies targeted academic skills, mostly literacy. Tablets were by far the most used technology in the classroom, with a variety of other technologies (e.g., computers, digital whiteboards, augmentative reality technology) represented as well. Across the included studies, there were mixed effects of technology-based interventions. While some studies reported robust and differential effects of technology-mediated intervention compared to traditional “paper and pencil” intervention, many studies showed no substantial difference between business as usual and instruction or intervention mediated by technology.

As the availability of technology increases in the typical early childhood environment, technology is included in intervention incidentally more frequently, rather than the purpose of the study to investigate the role of technology. For example, research on group instruction may capture the use of a digital whiteboard without the goal of the study being to evaluate the efficacy of the use of a digital whiteboard. Likewise, evaluations of functional communication training that utilize a speech-generating application on an iPad may not evaluate the effectiveness of the iPad but rather the teaching procedure for increasing functional communication. Given the ubiquity of technology in a typical preschooler’s daily world, it is possible the literature presented in this review did not capture the scope of technology literature for children in early childhood settings. Particularly, technology is frequently used in interventions for children with disabilities outside of the classroom context, which would not be captured by this review. Studies included in this review and captured via the search procedures used varied as whether they delivered an intervention that could only be delivered via technology (e.g., a math computer game) or the differential effectiveness of an intervention delivered through a technological mode or an analog mode (e.g., early literacy interventions). Future reviews and research may consider partitioning out these two separate types of studies to better evaluate the effectiveness of technology as an intervention agent.

Notably, only eight studies included in this review reported including students with disabilities or targeting special education classrooms (e.g., Cardon et al., 2019 ; Chai, 2017 ; Jung & Sainato, 2015 ; Kim et al., 2019 ; Lee & Tu, 2016 ; Musti-Rao et al., 2015 ; Sullivan & Bers, 2018 ; Taylor, 2018 ). Technology such as iPads, speech generating devices, and video modeling technology have a likely larger footprint in special education settings; however, many of these studies may not have been captured by this review. In several of the included studies, technology-based instruction or intervention served as a bridge for students who require additional supports. Instructional technology to adapt general education content to meet the needs of children with disabilities is supported by the studies included in this review. Attitudes towards technology in early childhood may also differ in special education versus general education settings. A differential analysis of the utility and frequency of use of technological interventions in early childhood general and special education may illuminate some potential avenues to improve inclusion.

Within the included interventions, there were a range of intervention targets. Despite only including intervention studies that took place in early childhood settings, the large majority of included studies were academic focused. There was a relative lack of technology-mediated studies focused on social communication interventions, play, or adaptive skills, all of which may benefit from technology (e.g., Saleh et al., 2021 ; Schmidt & Glaser, 2021 ). In a systematic review specific to ASD, authors found potential merits of using technology to support social communication deficits inherent to the ASD diagnosis (Pham et al., 2019 ). As more children have ready access to tablets, smart phones, and gaming platforms at home from a young age, interventions to support social communication capitalizing on the reinforcing nature of these technologies may be especially useful. Gamification, which was only minimally represented in this review, may be a compelling way to include technology in typical instruction of adaptive skills in early childhood settings, as video modeling has been shown to be effective.

Finally, the landscape of technology in education, while always rapidly evolving, has had to evolve significantly due to the COVID-19 pandemic. This trend is emphasized within this review by the exponential growth of included studies across the search years. With many if not all learning opportunities moving online even for very young children, technology may not have been optional in early childhood educational settings in the last year as it was in the studies included in this review. New methods to move instruction of very young children to an online platform need analysis and will likely change the perceived effectiveness of many of these interventions as well as potentially the early childhood philosophy of technology use in the early years. As more educational opportunities are moved online, technology-based interventions for young children will likely become more common, and teacher perceptions of technology may change.

Limitations and Future Research

This review, while systematic, did have several limitations. Primarily, we constrained the reported results to only early childhood educational settings. Studies conducted in clinical settings or with parents at home were not included and may have captured more or different technology use. Our definition of technology was also broad by design in order to best capture the complete footprint of technology in interventions in academic settings for young children. A more focused review, however, might have allowed for more analysis of certain technologies likely to be considered for adaptation for use in early childhood settings, for example, digital whiteboards. Additionally, both experimental and quasi-experimental studies were included in this review in order to provide a more complete picture of the existing research in this emerging area. When interpreting the results, care should be taken to ensure conclusions from more rigorous experimental studies are not overshadowed by contradictory findings from less rigorous quasi-experimental studies. We would also like to highlight the search terms used as a limitation of this study. Potentially the addition of more search terms could have broadened the number of studies returned for examination. Additionally, an ancestral search of included studies was not included in this paper which serves as another limitation to the findings of this review. Finally, due to the diversity of research methods and analyses, we were unable to present effect sizes or mathematically evaluate the role of technology in early childhood settings. Statistical analyses of the effectiveness of these interventions could have led to more effective recommendations for practice.

We identified several areas for future research and practice. Future research should further explore the utility of technology in early childhood settings for intervention targets outside academic areas, as most of the included studies addressed academic targets. Social skill interventions facilitated by technology and embedded into the larger classroom curriculum, for example, may benefit young children with ASD and other developmental disabilities at school (Heinrich et al., 2016 ; Simpson et al., 2004 ). Additionally, all available guidelines for the technology use of young children indicates that the quality of the technology and the degree to which technology use is mediated by adult attention is critical to determine whether technology is a value-added or a potentially harmful factor. Many of these studies did not clearly describe dosage and adult involvement in technology use. In addition, very little information is given in many of these studies as to whether the teachers were trained or how well they implemented the technology. Further, many of the included interventions were packaged interventions including a computer-based or tablet-based component. Future analyses should partition out the use of the technology from a well-planned delivery of the intervention in order to determine whether technology-mediated intervention is in fact more valuable. Social validity measures should also be carefully considered in future research, as preference may play a large role in the increased effectiveness of some of the technology-mediated studies.

Evidence from this review gives suggestions for practice that mirror those of professional organizations such as NAEYC (Copple et al., 2013 ). It appears that technology can improve outcomes for students on a range of skills, but the ability of interventions utilizing technology to improve the effectiveness of evidence-based practices likely depends upon a range of factors including dosage, student preference, teacher participation, teacher familiarity with technology, and the culture of the classroom towards technology. Interventions included in this review that demonstrated higher effectiveness of technology-based intervention than analog interventions likely improved one or more of those factors leading to increased engagement, greater dosage or ease of use of the intervention, or more efficient intervention delivery. Early childhood educational environments should include technology in instruction intentionally and moderated by skilled teachers who can monitor learning and provide meaningful connections. Training teachers effectively in how to teach using technology is critical to effective interventions.

Declarations

The authors declare no competing interests.

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*Indicates studies included in the review

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In This Article Expand or collapse the "in this article" section Technology Education in Early Childhood

Introduction, foundational/survey publications.

• Young Children Learning About Technology
• Math and Science
• Games and Play
• Equitable Access
• Accessibility Issues
• Influence of Culture
• Evaluating Technology
• Teachers’ Professional Development
• Large-Scale Research Studies/ Research Reviews

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Technology Education in Early Childhood by Kathy-ann Daniel-Gittens LAST REVIEWED: 13 August 2018 LAST MODIFIED: 31 March 2016 DOI: 10.1093/obo/9780199756810-0144

Technology in early education emerged as a discrete research area following the publication of Seymour Papert’s 1980 seminal text, Mindstorms: Children, Computers and Powerful Ideas . In this work, Papert encouraged the idea that computers should be used as creative tools to spark children’s imaginations and drive their cognitive development. Notably, he made a distinction between computers and older technologies such as television and radio. There are several researchers who have continued this line of inquiry, focusing their investigative work exclusively on computer technology use by very young children; specifically the age group birth–eight years. This research area has not developed as quickly as the study of technology use among older children and adolescents, however. One reason for this has been ethical conflicts expressed by educational and pediatric researchers who question the benefits and dangers of integrating technology into the education of children this young. Despite a slow start, there has been an accumulating body of work led by researchers and child-focused agencies that believe there should be research-based evidence to guide technology education practices for very young learners. Researchers such as Douglas Clements with his 1999 publication “Young Children and Technology” have been influential in this field. Susan Haugland has also been a pioneer with her 1999 and 2000 publications in which she asked—and answered—What Role Should Technology Play in Young Children’s Learning? Currently, a rapidly increasing array of Internet-connected computer devices such as tablets, smartphones and technology-enabled toys and games are being marketed for young learners. Because this trend is expected to grow, issues of how educators and parents can integrate these technology devices into young children’s lives and education, in productive ways, become even more significant. The headings for this topic are derived from a consideration of how technology is currently used in early education as well as critical issues that surround its use. The headings include topics such as Games and Play , the evaluation of technology, Teachers’ Professional Development , and Accessibility Issues for learners with disabilities. The citations selected represent the most influential writings and researchers as well as the most recent research on the topics. Included also are citations that survey the status of the field internationally and outlying research studies that appear to confound established thinking in the topic area. Large-scale research studies by child-focused agencies are also included.

This section introduces a representative sample of the more influential works that have guided current thinking and research on technology in early education. It includes Papert 1980 , Mindstorms: Children, Computers, and Powerful Ideas , and studies by researchers such as, Haugland ( Haugland 2000 : “What Role Should Technology Play in Young Children’s Learning? Part 2”) and Clements ( Clements 1999 , “Young Children and Technology”). It also includes the most recent survey publications: Donohue 2015 , Technology and Digital Media in the Early Years: Tools for Teaching and Learning , and Plowman, et al. 2010 , “ Growing Up with Technology: Young Children Learning in a Digital World . Plowman, et al. 2010 is based on their research in the United Kingdom. In addition to individual researchers, national and international organizations focused on early childhood education and Information and Communication Technology (ICT) in education have published on the topic. The United Nations Educational Scientific and Cultural Organization (UNESCO) Institute for Information Technologies in Education, in 2012, published a policy brief on ICTs in early childhood. The policy document, “ICTs in Early Childhood Care and Education,” looks at several areas of ICT implementation in early education and provides recommendations for policy and practice. Two influential organizations in early childhood education in the United States, the National Association for the Education of Young Children and the Fred Rogers Center for Early Learning and Children’s Media regularly publish position and policy papers that provide guidelines for practice in the area of technology education in early childhood. Their most up-to-date position paper is, “Technology and Interactive Media as Tools in Early Childhood Programs Serving Children from Birth through Age 8.” One opponent, which is countering advocacy for technology education for young children, is an international, nonprofit organization, The Alliance for Childhood. This organization is steadfastly opposed to technology education for young children on the grounds that it is both developmentally inappropriate and poses serious health risks. In 1999, this group published an influential report, “Fool’s Gold: A Critical Look at Computers in Childhood” ( Cordes and Miller 1999 ), in which the authors developed their case against the use of computers in early childhood education and listed their alternative recommendations. In concert with other international organizations, they continue to publish research reports, guidelines, and position papers advocating against technology education for young children. The edited publications, policy documents, and position papers in this section represent the advancement of thinking and practice in technology education in early childhood education.

Clements, Douglas. 1999. Young children and technology. In Dialogue on early childhood science, mathematics, and technology education . By Project 2061. Papers commissioned for the Forum on Early Childhood Science, Mathematics, and Technology Education, 6–8 February 1998, Washington, DC. Washington, DC: American Association for the Advancement of Science.

Provides a thorough overview of state-of-the-art with technology and young children at the time. It presents the accumulated, empirically validated knowledge about young children’s technology use in concise form. This work became part of a baseline platform of knowledge, establishing directions for further research in the area.

Cordes, C., and E. Miller. 1999. Fool’s gold: A critical look at computers in childhood . College Park, MD: Alliance for Childhood.

An online edited report that uses child development research to lay out the developmental changes that take place in early childhood. The report goes on to list perceived hazards and risks to these developmental changes caused by computers. Hazards include risks to children’s physical, social, emotional, intellectual, and moral development.

Donohue, Chip, ed. 2015. Technology and digital media in the early years: Tools for teaching and learning . New York: Routledge.

Using established theoretical principles for young children’s education as guiding frameworks, this survey text examines present-day technology use in early childhood education settings. The book also reviews models for how technology can connect home, school, and community to the benefit of young children. Book was updated to a 2015 edition.

Haugland, Susan W. 2000. What role should technology play in young children’s learning? Part 2: Early childhood classrooms in the 21st century: Using computers to maximize learning. Young Children 55:12–18.

Discusses the ways in which computers can be used to promote learning in young children in early education. The paper also discusses two models for integrating computers into the education of young learners that were presented in Part 1 of this paper. It also presents models for teachers’ professional development for integrating computers into classroom curricula. Includes suggestions for evaluating hardware technology, software, and interventions.

National Association for the Education of Young Children and the Fred Rogers Center for Early Learning and Children’s Media. 2012. Technology and Interactive Media as Tools in Early Childhood Programs Serving Children from Birth through Age 8 .

A position paper by two influential organizations in the field of early childhood education. The position paper sets out the current issues as identified by the organizations and presents the principles and guidelines they advocate as developmentally appropriate for addressing the issues and working with young children.

Papert, Seymour. 1980. Mindstorms: Children, computers, and powerful ideas . New York: Basic Books.

Considered the most influential text by one of the early and major researchers into children’s use of computers. Based on his research into the use of computers to promote children’s problem-solving skills, Papert advocates for the idea that computers should be used as tools to promote children’s general cognitive development.

Plowman, Lydia, Christine Stephen, and Joanna McPake. 2010. Growing up with technology: Young children learning in a digital world . London: Routledge.

Text provides a thorough research-based survey of issues related to different technologies and young children’s learning at home and in school. Also examines the theoretical foundations that guide work and study in this area.

UNESCO Institute for Information Technologies in Education. 2012. ICTs in early childhood care and education .

A policy brief on implementing ICTs in early childhood. Identifies layers of perspectives on the implementation process. Also highlights risks and proposes recommendations in support of the implementation process.

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• Technology in the Early Childhood Environment -- Is It Appropriate?

Is the Use of Technology Appropriate in the Early Childhood Environment?

Over the past several years, integrating technology in early childhood learning environments has become a topic of concern among the early childhood professional community. Educators worry that children spend too much time on electronic devices at home and it may not be developmentally appropriate to utilize technology at a young age.

How Much Time Are Children Spending on Technology?

There is no denying that young children are growing up in the digital age and technology is an integral part of their world. A study published in 2017 by Common Sense Media revealed statistics of technology use for children from birth to age 8 by comparing usage from 2011 to 2017. The statistics revealed:

• The percentage of families who used mobile devices rose from 52% in 2011 to 98% in 2017.
• A third of all screen time is mobile for this age group. This is an increase of 31% from 2011.
• The average time spent on mobile media has tripled, increasing from 5 minutes to 48 minutes per day.
• Overall, from birth to age 8, children spend an average of just over 2 hours per day with screen media.

Does Technology Have a Place in the Early Childhood Curriculum?

A joint statement released by the National Association for the Education of Young Children (NAEYC) and the Fred Rogers Institute  in 2012 was the beginning of a movement to start integrating technology into the early childhood environment. Prior to this, many felt that young children should not be exposed to technology in the early learning environment because they already have enough exposure in the home environment. However, this statement indicated that technology, when used appropriately and with intention, can be an effective tool to support learning and development. The phrase “with intention” is a key point in this statement and refers to technology being considered only if it is an effective way to extend learning beyond what traditional methods can offer.

According to Highscope’s Position Statement on Technology , there are some important considerations when adding technology into the early learning environment. Early learning educators should have a choice about whether or not to offer technology in the classroom, but the educators should be mindful that lack of exposure to technology may negatively impact children's school readiness when compared to programs that do incorporate technology. Developing digital literacy skills has increasingly become a part of the early childhood curriculum.

What Are the Common Obstacles When Implementing Technology Into the Curriculum?

To be used effectively, technology depends on the tools being used in the right way by skilled users. Early childhood educators will want to provide opportunities for children to gain new skills and have access to new content. According to a study by Head Start , the primary obstacle for early childhood programs when trying to successfully implement technology effectively is the lack of staff technology literacy. Therefore, in order to provide extended learning, many teachers may need professional development to familiarize themselves with new strategies and techniques in the areas of digital literacy and technology.

Highscope’s Position Statement indicates that early learning should still primarily occur through interactions with a child’s peers, adults, hands-on materials, and actual experiences. This consideration is shared by the NAEYC . When identifying the key factors that create a high-quality early learning environment, NAEYC includes interacting with caring adults, hands-on learning, play-based activities, as well as providing opportunities to explore and experience independently. Consequently, finding ways to appropriately integrate technology into these types of activities can be challenging for many early learning programs.

How Can Early Learning Programs Use Technology Effectively and Appropriately?

The Office of Educational Technology within the Department of Education believes guidance is needed when using technology with early learners. This is based on the reality that families and early educators have access to a wide range of technology including apps, games, video chat software, and digital books. The Department has created four principles to help guide parents and educators on the appropriate use of this technology for early learners. These guiding principles are as follows:

• Technology—when used appropriately—can be a tool for learning.
• Technology should be used to increase access to learning opportunities for all children.
• Technology may be used to strengthen relationships among parents, families, early educators, and young children.
• Technology is more effective for learning when adults and peers interact or co-view with young children

Although accessing some form of technology has become commonplace with young children, these principles clearly guide early educators with the appropriate use of technology in the learning environment. The principles stress that technology should not be used for technology's sake alone. Integrating technology should only be done if it is used for learning and can help meet developmental objectives. Technology can be used along with art activities, books, and play materials.

The Department of Education describes the use of technology as either active or passive. When children are passively participating in technology, they can be watching a television program without any participation. Active participation is defined as content that should “enable deep, cognitive processing, and allow intentional, purposeful learning at the child’s developmental level.” For example, a child can be using a computer but engaging with imagination or responses and include learning with intention.

According to the American Academy of Pediatrics’ (AAP) Brief “ Media and Young Minds ,” children under the age of 2 are not encouraged to engage in technology because it is not developmentally appropriate. However, for young children ages 2 through 5, technology can be beneficial if used appropriately. An example provided by the AAP is that technology can aid in expanding cultural diversity in the early learning environment by exposing children to communities and cultures outside of their own. When used in this way, we can expand children's understanding of the world around them in a way they haven't experienced, with only a discussion or a book. By adding technology to these other methodologies of learning, a child can experience learning at a deeper level.

Learn More About Early Childhood Development and Education

If you are interested in early childhood development and education, you may consider a bachelor of science in early childhood administration . Students in Purdue Global’s early childhood administration program learn the skills to provide developmental and learning opportunities for children from birth to age 8. For more details, request more information .

About the Author

Carol Laman, MA

Carol Laman is a full-time adjunct instructor at Purdue Global. The views expressed in this article are solely those of the author and do not represent the views of Purdue Global.

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Technology in Early Childhood Education

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As technology advances, so does the classroom. Technology in early childhood education was very different 10-20 years ago; teachers would interact with the tech and children would observe. Now, toddlers and preschoolers can interact with technology. Interactive media, used on smart devices such as phones, televisions, tablets, and gaming systems, give young children the ability to participate and guide themselves through learning. 

Technology isn’t only a staple in life, but also in education. It can be a beneficial tool that allows you to educate, engage, and interact with young learners. In this article, we'll cover why technology is important in early childhood education and effective ways to incorporate it in the classroom. 

Technology in early childhood education

Technology in early childhood education has transformed over the last 50 years. At first, televisions were used to strengthen lessons and capture the interest of students. Then, televisions would graduate to personal computers and smart technology through phones, tablets, and apps. 

In early childhood education, technology can be a tool to facilitate learning. In conjunction with interactive media, it plays a large role in guiding the development of young children. It enables them to play, express themselves, and cultivate their skills in a safe, engaging way.

Still, concerns are typically raised regarding technology, interactive media, and early childhood education. Some public health organizations and child advocacy groups concerned with child development and health issues, such as obesity, have recommended that passive, non-interactive technology and screen media not be used in early childhood programs. 

With parents and guardians playing a significant role in early childhood education, there are additional concerns with the passive use of screen media in the home. An example is keeping the television on and playing in the background. It can cause irregular sleep patterns, focus and attention problems, and an increase in screen time. However, the findings on the value of technology in children’s development can be confusing as they’re divided.

While many are likely to agree that too much screen time —the amount of time spent using a device with a screen—can be detrimental, technology, when used right, can be helpful to the educational and developmental success of young children.

Why is technology important in early childhood education

In your experience as an educator, you’ve likely learned that there is rarely just one way to do something; technology is a clear example of this. In the past, teachers had very few resources at their disposal. They didn’t have access to much more than textbooks and props. Now, technology offers more for educators and children. Technology can give you access to more resources, innovative teaching methods, and variety as you create an active learning environment for your children.

Incorporating technology into your children's development and education plan also enables you to create lessons for multiple learning styles . Visual learners can use smart boards or tablets to draw pictures and look at other visual aids, while reading/writing learners can use the same media to absorb information or write down ideas. Music streaming platforms or audiotapes can be helpful for auditory learners. As for tactile or kinesthetic learners, these young children can learn by acting out a scene they saw in a video or using interactive media on smart technology devices.

The importance of technology in early childhood education doesn’t end there. There are many associated benefits. Technology can:

• Support the development of fine motor skills
• Strengthen coordination and reaction time
• Improve social and emotional development 
• Promote collaboration and relationships
• Build cultural awareness
• Help language development
• Offer opportunities for information processing

Again, technology is a tool to facilitate learning, and when used appropriately, it can have remarkable effects on early childhood education.

How to incorporate technology in the classroom

When introducing technology to the classroom, you have many options. You now have access to smart devices that feature apps, digital books, games, and more. With the abundance of technology, how can you manage it? How do you decide when, how much, and how often to use it?

The Department of Education lists four guiding principles for using technology :

• Technology—when used appropriately—can be a tool for learning.
• Technology should be used to increase access to learning opportunities for all children.
• Technology may be used to strengthen relationships among parents, families, early educators, and young children.
• Technology is more effective for learning when adults and peers interact or co-view with young children.

1. Technology—when used appropriately—can be a tool for learning

Technology isn’t always used for learning, but it can be. Author Lisa Guernsey of Screen Time: How Electronic Media—From Baby Videos to Educational Software—Affects Your Young Child provides guidance on how early childhood educators can determine technology use for young children. You can use the Three C’s—the content, the context, and the needs of the individual child—to create parameters. 

You might ask: How does this help children learn, engage, express, imagine, or explore? What kinds of social interactions (such as conversations with parents or peers) happen before, during, and after using the technology? What does this child need right now to enhance their growth and development? 

Technology allows young children to engage and explore. When used appropriately, it allows them to participate actively in their education through playing and problem-solving.

2. Technology should be used to increase access to learning opportunities for all children

It’s no secret that the learning materials of the past were limited. Depending on when you went through early childhood education and development, you likely had access to textbooks, library books, and possibly a few videos. With technology, the resources for today’s children are virtually limitless.

The learning opportunities go beyond the words in a textbook and can introduce children to information and cultures beyond their classroom and community. Technology allows you to present diversity to young learners, for example, by exposing them to different types of people, music, and family structures.

3. Technology may be used to strengthen relationships among parents, families, early educators, and young children

The COVID-19 pandemic and its effects on education demonstrated how important technology is in the classroom. If the pandemic had occurred before the turn of the century, it’s impossible to guess how millions of children and early educators would have kept up with lessons. Fortunately, with greater access to technology—computers, smartphones, tablets—teachers could still teach, connect, and strengthen their relationships with children through video-chat interactions.

Additionally, technology also allows families to strengthen their relationship with their children and with you. Digital portfolios and progress reports allow you to share what lessons the children are learning, but it also allows families to be more active participants in their child’s education by tracking their progress. It enables you, the educator, and families to work collaboratively to strengthen and reinforce what they learn in the classroom.

Brightwheel makes it easy to track student progress with streamlined milestone tracking and customizable portfolio templates. You can created individualized progress reports that measure each child's progress against state standards pre-loaded in the app or your program's learning framework. Easy digital sharing with families means you can continue to foster engagement and support. 

A child’s education is not solely left up to educators and administrators. With the help of technology, all parties—teachers, families, and administrators—can be actively aware of a child’s progress and use technology and interactive media to work towards their success.

4. Technology is more effective for learning when adults and peers interact or co-view with young children

Many technological devices are designed for singular use. Computers, smartphones, and tablets are typically meant to be used by one person at a time; however, the Department of Education explains that children learn more from content when parents or early educators watch and interact with children.

Engaging with and encouraging children during and after a lesson can help solidify the information and lead to more effective learning.

It’s up to you to determine how you use technology in the classroom. As long as you plan active, engaging activities, focus on content, monitor screen time, and work together, you can use technology to create a successful educational and developmental learning environment for young children.

Examples of technology in early childhood education

Depending on their age, young children have different exposure to technology. For infants and toddlers, their experience is almost exclusively guided by their teachers, parents, and families. As they grow to preschool age, they develop more autonomy and independence. This can lead to technology acting as a creative outlet and learning resource.

There are many ways in which technology such as televisions, smartphones, and computers, and interactive media like apps and games, can create a successful classroom environment.

• Use technology to introduce young children to diverse images of people and things.
• Explore digital materials through shared technology time. Through activities like shared book reading, you can create opportunities to engage with young children by talking to them and introducing new vocabulary.
• Incorporate technology activities to teach digital literacy skills. 
• Create progress reports using audio or visual files to share digital updates with families.
• Use video-chat software to communicate with children and families outside the classroom.
• Document children’s drawings and create digital books with photos to explore digital storytelling.
• Expose children to concepts like science, technology, engineering, arts and mathematics (STEAM) by including STEAM activities in your lesson plans.

Technology creates an abundance of learning opportunities in the classroom. With its vast capabilities and your creativity, you can create countless lessons for your children.

Technology is best when it brings people together

Technology is many things. It’s a video demonstrating the leaves changing from season to season. It’s an interactive lesson about the difference between the letters “ b ” and “ d ”. It’s an opportunity to connect with children and families outside the classroom. 

Technology is a bridge that fills in the gaps of the past. Children, teachers, families, and administrators are now equipped with tools that promote safe, active learning in the classroom and beyond. As an educator, you take the lead in shaping the early childhood education and development of young learners. With technology acting as your tool, you can educate, engage, and interact with your children in new, innovative ways.

Brightwheel is the complete solution for early education providers, enabling you to streamline your center’s operations and build a stand-out reputation. Brightwheel connects the most critical aspects of running your center—including sign in and out, parent communications, tuition billing, and licensing and compliance—in one easy-to-use tool, along with providing best-in-class customer support and coaching. Brightwheel is trusted by thousands of early education centers and millions of parents. Learn more at mybrightwheel.com.

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School and Teacher Information, Communication and Technology (ICT) readiness across 57 countries: The alignment optimization method

• Published: 25 July 2022
• Volume 28 , pages 1273–1297, ( 2023 )

Cite this article

• Rongxiu Wu   ORCID: orcid.org/0000-0003-0457-2738 1 ,
• Weipeng Yang   ORCID: orcid.org/0000-0002-8057-2863 2 ,
• Graham Rifenbark   ORCID: orcid.org/0000-0003-1467-6469 1 &
• Quan Wu   ORCID: orcid.org/0000-0002-0495-3111 3  

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This study investigated the measurement invariance of school and teacher Information, Communication and Technology (ICT) readiness among 57 countries that participated in the Program in International Student Assessment (PISA) 2018 assessment. School and teacher ICT readiness scale is 11-item scale with two subfactors: school ICT readiness and teacher ICT readiness subscales (Bozkus, International Online Journal of Education and Teaching, 8 (3), 1560–1579, 2021 ). With the novel alignment optimization method, we revealed that the school ICT readiness subscale was invariant for unbiased country comparisons but overall noninvariance was identified for the teacher ICT readiness subscale. Additionally, the rank of the school ICT readiness factor means indicated that Singapore, Sweden, B-S-J-Z (regions of China), United Arab Emirates and United States were among the top league, while countries like Indonesia, Poland, Ireland in between, and Japan, Mexico, Colombia, Argentina and Brazil ranked comparatively the lowest. Measures of school location, school type and class size further confirmed the validity of the school ICT readiness subscale. It was expected that the study would enhance our understanding of school and teacher ICT readiness across countries with the application of an alternative alignment optimization approach in examining ICT related scales.

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1 Introduction

The digital age in the last decade has witnessed a rapid development of Information and Communication Technology (ICT) in the education field across the world (Cradler et al., 2002 ; OECD, 2016 ; Sezer, 2017 ). A growing body of literature has examined the ICT-related constructs as well as how it reflects educational quality and equity (Gumus & Atalmis, 2011 ; Lowther et al., 2008 ; Novak et al., 2018 ). Currently, under the ongoing circumstances of the worldwide COVID-19 pandemic, digital technology has especially gained much more attention for schools worldwide since online teaching becomes a necessary alternative to in-person classroom teaching (Kong et al., 2022 ). Nonetheless, facing the global emergency, schools have met unpredictable complex challenges, such as lack of infrastructure of digital device (Kim et al., 2021 ) and teachers’ limited capacity using digital device (Bozkus, 2021 ). All of these have unraveled the important supporting role of a good school and teacher ICT readiness environment to students with online learning (Morse, 2004 ; Norris, 2001 ; Van Dijk, 2020 ).

Throughout the years, the international large-scale assessments such as Program for International Student Assessment (PISA) have recognized the significance of ICT and measured ICT use across the countries through various aspects, such as school ICT resources, teacher ICT self-efficacy, and student ICT familiarity, skills, self-efficacy and engagement (OECD, 2005 , 2016 ; 2019a , b ). In PISA 2018, a developing scale consisting of school infrastructure of digital device and teachers’ capacity using digital devices was used to measure school and teacher ICT readiness across the countries. To make valid cross-country comparisons, an assessment to ensure the scales function the same way across the countries is a prerequisite. However, it remains an open question as to whether cross-cultural invariance of many ICT-related scales is supported. Therefore, establishing measurement invariance is an indispensable pre-procedure to ensure the scale’ validity when researchers aim to conduct multiple country comparisons and latent mean comparisons (Tracey & Xu, 2017 ; Vandenberg & Lance, 2000 ). A novel measurement invariance alignment method, proposed by Asparouhov and Muthén ( 2014 ), has been gradually recognized by researchers in conducting invariance tests across multiple groups.

To date, there has been no in-depth measurement invariance analysis of the scale measuring PISA 2018 school and teacher ICT readiness. There is a great need to examine whether this feature holds before it is widely adopted into use for researchers to conduct comparisons directly. Therefore, the first purpose of this study is to examine whether the measurement invariance of the scale across the countries is supported at an acceptable level with alignment method, using a set of items provided by PISA 2018. Second, if measurement invariance of the scale holds, the countries’ ICT readiness factor scores are comparable. Lastly, we would like to select a few relevant variables to examine group mean differences as validation measures. Specifically, this study starts by outlining the conceptual background of PISA 2018 school and teacher ICT readiness scale, and elaborating on the logic of alignment method, literature of application of alignment method on ICT-related scale to test for measurement invariance; then, followed by the detailed data selection, analysis procedure and result; lastly ended with a discussion of the methodological and practical significance of this approach to measure invariance of ICT readiness scale, with attention to its implications for future cross-country research using large-scale surveys.

2 Conceptual background

A large body of existing studies have been exploring the reasons of successful ICT implementation in schools, from the individual student and teacher level to the wider school context (e.g., Davies & West, 2014 ; Eickelmann, 2011 ; Inan & Lowther, 2010 ; Lim et al., 2013 ; Petko, 2012 ). Petko ( 2012 ) proposed a “skill, will, tool” theoretical model emphasizing the teacher skills, self-efficacy in technology can achieve the technology integration together with the infrastructure of digital devices within schools. School infrastructure of digital devices is usually considered as school ICT readiness, a prerequisite for supporting teachers’ capacity using digital devices (Liu et al., 2016 ; Petko, 2012 ; Petko et al., 2018 ). Liu et al. ( 2016 ) have identified a few factors, such as school technology support, and school access to technology in classroom as impacts affecting classroom technology integration. Teachers’ capacity to utilize the digital devices is often considered as teacher ICT readiness, more successful to implement when having the school support (Daly et al., 2009 ; Richardson & Placier, 2001 ).

2.1 School ICT readiness: School infrastructure of digital devices

Infrastructure of digital devices that schools provide, regarded as school ICT readiness, affects both the way that teachers use for teaching, and students’ ICT-related learning quality, engagement and familiarity in use (Fraillon et al., 2014 ; Lau & Sim, 2008 ; Liu et al., 2016 ; Ma & Qin, 2021 ; Murillo & Román, 2011 ; Woessmann & Fuchs, 2004 ; Zhang & Liu, 2016 ). Digital infrastructure is identified as the first barrier in technology integration even among the current “digital native” generation who grew up with digital technologies (Li et al., 2015 ; Sang et al., 2011 ). Students in a technology-rich school environment are more motivated, more confident in their digital abilities and tend to perform better in ICT-related performance (Sun et al., 2018 ; Wastiau et al., 2013 ). With a multi-level data analysis, Liu et al. ( 2016 ) found out that school digital access and support greatly affected teacher use of technology and classroom technology integration in primary schools. Saal et al. ( 2021 ) reported that computer availability and frequency of use in the mathematical classes were positively associated with the students’ mathematics achievement. An inverted U-shape relationship was identified between school internet use and student performance in mathematics and reading (Woessmann & Fuchs, 2004 ). Factors that affect the frequency of digital use and technology integration in schools are such as, colleagues’ support and principals’ discretion over technology spending (Karaca et al., 2013 ; Miranda & Russell, 2011 ).

2.2 Teachers ICT readiness: Teachers’ capacity using digital devices

Teachers’ capacity using digital devices, another aspect of digital readiness, has often been considered as teacher ICT readiness. It refers to the teachers’ confidence in their abilities to use digital technologies and willingness to utilize them in education (Fraillon et al., 2014 ; Petko et al., 2018 ; Wastiau et al., 2013 ), which directly associates with successful technology implementation and integration in schools (Petko et al., 2015 ; Petko et al., 2018 ). Many factors impede the realization of teachers’ capacity utilizing the digital devices. It is claimed that teachers’ belief and lack of sufficient skills were the main obstacles impeding the digital technology integration (Petko et al., 2018 ). First, teachers who get used with the traditional approach of teaching has a pedagogical prejudice and negative belief against the usage of digital technologies in the classroom (Ertmer et al., 2015 ). Teachers’ self-efficacy affects their confidence in effectively using digital technology for education as well (Fraillon et al., 2014 ). Second, if teachers do not feel competent and lack of sufficient skills in utilizing the technologies, it also impacts the effective application of digital devices in classroom teaching. Speaking of teachers’ skills, technological pedagogical content knowledge (TPACK) currently serves as an important model in this domain (Blackwell et al., 2016 ), which differentiates seven different types of knowledge about pedagogy, technology and content as well as their combinations (Voogt et al., 2013 ). Necessary training is required for effective digital technology integration together with administrative, and peer support.

2.3 Measurement invariance

Measurement invariance is a prerequisite for valid scale comparison research (Vandenberg & Lance, 2000 ). Invariance of the measure represents the scale functions the same across the groups. Specifically, it means the psychometric properties (e.g., factor loadings, item intercepts) relating the observed variables to the latent factor(s) should be similar across groups. Alignment method, proposed as an alternative to the traditional multi-group confirmatory factor analysis (MGCFA) in recent years, can conveniently estimate the means and intercepts of two or more groups. It overcomes the limitations of MGCFA such as labor-intensive and error-prone when the number of items and groups increase (Byrne & Vijver, 2017 ; Magraw-Mickelson et al., 2020 ; Muthen & Asparouhov, 2014 ) and allows for approximate rather than exact measurement invariance. Through automating invariance testing among groups with expected non-invariance, alignment method can estimate the factor loadings, item intercepts and factor means across groups in the presence of partial invariance, which greatly simplifies the testing procedures and has been considered as an optimal pattern of measurement invariance (Asparouhov & Muthén, 2014 ; Flake & Luong, 2021 ; Flake & McCoach, 2018 ; Muthen & Asparouhov, 2014 ).

There are usually two steps involved when conducting alignment analysis. The first step is called FREE alignment, through which a configural model is established and factor loadings and indicator intercepts are freely estimated for each group. The factor means are fixed at 0 and factor variance are fixed at 1. The second step is FIXED alignment optimization, in which the factor means and variances are freely estimated, and for every group factor mean and variance parameter, there are factor loading and intercept parameters that yield the same likelihood estimation as the configural model, therefore, model fit of the M0 is unaffected by alignment optimization and should be equal to the model fit of M1 (Asparouhov & Muthén, 2014 ). Based on the item-level significance tests for good performance, the cutoff point 25% of non-invariant parameters is recommended as a “rough rule of thumb” (Muthen & Asparouhov, 2014 ). FIXED alignment is required when there are only two groups compared and FREE alignment is recommended to work better when there are three and more groups involved. Moreover, researchers can assess the invariance effect size measure, which quantifies how much variability in the item parameter estimates can be explained by the groups’ factor means and variances. An R 2 near 1 indicates complete invariance because the variability in item parameters is completely explained by group mean differences, whereas an R 2 near 0 indicates that group mean differences explain none of the variability in the item parameter (Byrne & Vijver, 2017 ; Asparouhov & Muthén, 2014 ). Collectively, the information can serve as a guide for the follow-up decisions regarding item functioning and development.

Nonetheless, though the importance and necessity of measurement invariance before conducting the group mean comparisons has been recognized since the inception of large-scale national and cross-national assessments such as PISA, it is still rarely used partly due to difficulty in interpretation and implementation when more groups are involved with the traditional MGCFA, and partly due to unfamiliarity with alignment method. Meng et al. ( 2019 ) established the measurement invariance at the scalar level from PISA 2015 ICT student engagement scale with MGCFA but only limited to the comparison between just two countries German and China. Alignment method has still rarely been known and it is even less used in testing the measurement invariance of ICT-related scales. There was only one measurement invariance study on students’ mathematics, science and ICT familiarity scale across PISA 2015 participating countries with the alignment method (Odell et al., 2021 ).

2.4 The present study

School and teacher ICT readiness scale has been studied as a multidimensional construct, in which the two aspects are interrelated with each other (Bozkus, 2021 ). To our knowledge, measurement invariance of this newly introduced school and teacher ICT readiness scale in PISA 2018 has not been found to be addressed (OECD, 2020 ). Recognizing its importance, this study would like to initially assess the measurement invariance of the school and teacher ICT readiness scale before applying it into robust group comparisons. If measurement invariance of the ICT-related questionnaires holds at an acceptable level, then it will be valuable to compare its factor score means across the groups. Moreover, to have a better understanding of the scale with other relevant variables, a few school level indicators would also be use as validation measures.

3.1 Data source and sample description

The data source for this study was the international large-scale assessment PISA 2018, a two-stage stratified assessment which mainly focused on 15-year-old students’ reading, science, and mathematics literacy. PISA 2018 is the latest seventh cycle, which focuses on reading in a digital context (OECD, 2019b ). The questionnaires are designed by the PISA Governing Board, in which the content specialists and measurement experts work together to test its applicability in measuring students’ performance (OECD, 2019a ). For the detailed sampling procedure, please refer to the specific PISA technical report (Kastberg et al., 2021 ).

The dataset for the current analysis was from the school-level questionnaires administered to school principals who participated in PISA 2018. Since the questionnaire was optional for the participating countries (OECD, 2019a ), we have removed the countries that chose not to take the surveys (e.g., Cyprus and Moscow City (RUS)), and those that had very limited responses from the schools (< 100) (e.g., Malta, Brunei Darussalam, Montenegro). The final 57 countries (37 were OECD participating countries) for analysis were selected from America, Europe, Africa, the Middle East, Asia and Oceania, which was a good representation of different geographical regions and distinctive education systems. The country ID variable CNTRYID was used to define the 57 countries as latent classes for further measurement invariance analysis. The total schools from these countries were 18,041 and the average of the school numbers was 316, ranging from 142 in Iceland to 1089 in Spain. The country ID, Country name and number of schools that participated in the survey for each country was provided in Table  1 .

3.2 Variables

School and teacher ICT readiness scale is a self-reported 11-item four-point Likert-type scale. As evidenced by Bozkus ( 2021 ), this scale is a two-factor construct: one is school infrastructure of digital devices, which is measured with five items SC155Q01HA to SC155Q05HA (e.g., The number of digital devices connected to the Internet is sufficient ) and the other is teachers’ capacity using digital devices, which is measured with six items SC155Q06HA to SC155Q11HA (e.g., Teachers have the necessary technical and pedagogical skills to integrate digital devices in instruction ). School principals were asked to rate their agreement with each statement by selecting from four response options (“Strongly disagree”; “Disagree”; “Agree”; “Strongly agree”). The scale had appropriate reliability across the participating countries (Omega ω = .90). A total of 18,041 school principals answered to the list of questionnaires, in which 17,305 principals fully responded to all items. For the details regarding how the scale was administered, please refer to specific PISA manual (Kastberg et al., 2021 ).

To explore whether and how the factor may relate with some school and teacher level variables, a few relevant measures such as school location (Looker & Thiessen, 2003 ; Zhao & Frank, 2003 ), school type (Besley & Ghatak, 2001 ) and class size (Hislop & Ellis, 2004 ; Van de Vord & Pogue, 2012 ) were used for validity. For instance, school location variable SC001Q01TA , which included five ordinal categories from 1 = “A village, hamlet or rural area (fewer than 3 000 people) ”, 2 = “ A small town (3 000 to about 15 000 people) ”, 3 = “ A town (15 000 to about 100 000 people) ”, 4 = “ A city (100 000 to about 1 000 000 people) ” and 5 = “ A large city (with over 1 000 000 people) ”; school type variable SC013Q01TA , which describes whether the school is managed by a public education authority, government agency or a non-government org; and class size variable CLSIZE , which includes nine categories from “15 students or fewer ” to the largest size “ More than 50 students ”. The descriptive statistics of each item in the scale and the relevant validity measures were provided in Table  2 .

3.3 Analytical approach

First, before testing the measurement invariance of school and teacher ICT readiness scale, a conceptually consistent and cross-country measurement model needed to be tested. Therefore, a single-group confirmatory factor analysis (CFA) using robust weighted least squares mean and variance (WLSMV; Bowen & Masa, 2015 ) would be conducted to examine the factor structure of the ICT scale for the selected countries (regions). The model fit indices include comparative fit index (CFI; Bentler, 1990 ) and Tucker-Lewis index (TLI; Tucker & Lewis, 1973 ) with acceptable fit ≥ .90 and good fit ≥ .95, root mean square error of approximation (RMSEA; Steiger & Lind, 1980 ) with acceptable fit < .06 and standardized root mean residual (SRMR; Joreskog & Sorbom, 1981 ) with acceptable fit ≤ .08 (Hu & Bentler, 1999 ). Chi-square statistics is also reported but only for model comparisons not for accessing model fit since usually a statistically significant chi-square will be produced due to a large sample size (Chen, 2007 ). To account for the uneven probability of selection of schools within each country, school-level weighting variable W_FSTUWT_SCH_SUM was incorporated into the analysis.

After testing the conceptually consistent and cross-country measurement model, the next step is to test the measurement invariance with alignment method. Due to non-implementation of cross-loading in alignment method, five-item subscale of school infrastructure of digital devices and six-item subscale of teachers’ capacity of using digital devices were conducted separated with the goal of comparing mean scores in each subscale across the selected countries. Since FREE alignment is more recommended than FIXED alignment in more than two group comparison, we would first adopt the FREE alignment. If it produces any warning message, we would switch it the FIXED alignment. In the FREE alignment, all factor means are allowed to be estimated, but requires greater factor loading non-invariance (Muthen & Muthen, 2019 ). The reference group used was the country with the factor mean closest to 0, either positive or negative. In the FIXED alignment, the factor mean was constrained to zero for a particular group, similar to typical identification methods in CFA. Given that small number of valid missing responses on individual items existed for some responses, the full maximum likelihood estimation with robust standard errors (MLR; Yuan & Bentler, 2000 ) estimator was adopted. Both the CFA and alignment procedure were conducted in M plu s 8.4 (Muthen & Muthen, 1989– 2019 ) and all other data cleaning and analysis was conducted using R version 4.0.3 (R Core Team, 2020 ).

4.1 Evidence of factor structure

As mentioned in the analytical procedure, school and teacher ICT readiness scale was evidenced as a two-factor construct (Bozkus, 2021 ). We conducted a CFA across all the groups and found that the factor structure was supported with good model fit, χ 2 (43, N  = 17, 305) = 1275.996 ( p  < .001), CFI = .981 > .95, TLI = .976 > .95, RMSEA = .041[.039, .043] < .06, SRMR = .059 < .08. As stated previously, the chi-square test is possibly to be rejected with large sample size. Therefore, based on the model evaluation criteria, we concluded that there was adequate evidence of factor structure of the school and teacher ICT readiness scale to conduct measurement invariance test.

4.2 Alignment method analysis of school and teacher ICT readiness scale

Measurement invariance with FREE alignment approach was initially performed separately on the two subscales of the construct. No warning of untrustworthy standard errors was produced; therefore, we would adopt the FREE alignment approach for the optimization analysis. Table  3 demonstrated an invariance pattern with the two alignment fit indices: (a) fit function contribution value and (b) R 2 value. First, high fit function contribution value is an indication of possible noninvariant item. For school readiness subscale, the intercept of SC155Q02HA showed higher absolute fit function than those of the other items indicating a higher noninvariance feature of SC155Q02HA than that for the other items. For teacher readiness subscale, SC155Q10HA showed much higher absolute fit function than those of the other items, indicating a higher noninvariance feature of SC155Q10HA than that for the other items. Second, the higher R 2 is, the more likely the item is invariant. Contrarily, the lower R 2 is, the more likely the item is non-invariant. In Table 3 , SC155Q01HA and SC155Q03HA were close to 0, which was an indication of high noninvariance of these two items. Comparing the fit values between school ICT readiness subscale and teacher ICT readiness subscale, teacher ICT readiness showed higher values in fit function contribution and much lower R 2 values (all <.06), which partly showed that the items in the subscale teacher ICT readiness subscale was more noninvariant than that for those in the subscale school ICT readiness subscale. Additionally, the overall fit function contribution values for the factor loadings were lower than those for the intercepts for both subscales, indicating a higher degree of invariance among the loadings than that for the intercepts.

The noninvariance of items and intercepts across countries was shown in Appendix Table  6 , where a parenthesized group is an indication of noninvariance. For instance, Country ID 191, 440, 616 and 203 that have been parenthesized for SC155Q01HA intercept indicated that these four countries Croatia, Lithuania, Poland and Czech Republic had noninvariant factor intercepts for SC155Q01HA . Overall, the total number of parentheses in intercepts was much larger than the total number of parentheses in loadings, which suggests that the intercepts of the items were more noninvariant than the loadings of the items. Therefore, metric invariance might hold but not possible scalar invariance. In terms of school ICT readiness, given 5 items and 57 countries, 8 noninvariant parameters (of a total 285 (57*5) parameters) revealed evidence of factor loading noninvariance to be exceedingly low at 2.8%. Turning to the intercepts, though 53 noninvariant parameters were found, their overall percentage of 10.7% was still substantially lower than the recommended 25% cut-off point noted above. When it came to teacher ICT readiness, 103 noninvariant parameters of a total of 342 (57*6) parameters revealed that 30.1% of parameters were noninvariant, which was far higher than the 25% cut-off point. Therefore, overall, we felt confident in the trustworthiness of the latent mean estimates and comparison for the school ICT readiness subscale but not for teacher ICT readiness subscale across the 57 countries. Figure  1 also provided a comparison of proportions of invariant parameters between ICT readiness subscales and threshold visually.

Comparisons of proportion of invariant parameters between ICT readiness subscale and threshold

4.3 Factor mean values for school ICT readiness subscale across countries

The factor mean values for school ICT readiness subscale by country were shown in Table  4 , which was arranged in an ordered list ranging from high to low. As showed in the table, New Zealand (country ID 554) was selected as the reference group with a factor mean closed to 0 ( M  = −.002). The rank order of factor means demonstrated that Singapore (702) had the highest factor mean in school infrastructure of digital devices, whereas Brazil (76) showed the lowest. The five countries with the highest school ICT readiness factor means were Singapore (702), Sweden (752), B-S-J-Z (regions of China) (975), United Arab Emirates (784) and United States (840). The lowest five countries in school ICT readiness factor score were Japan (392), Mexico (484), Colombia (170), Argentina (32) and Brazil (76).

4.4 School ICT readiness factor scores across the school location, type and class size

The result showed that schools from large cities had much higher school ICT readiness ( M  = 0.24) than those from village ( M  = −0.24, d  = 0.47), those from small town ( M  = −0.06, d  = 0.30), and those from town ( M  = −0.05, d  = 0.29). Though school ICT readiness scores were positive for schools both from cities and large cities, there still existed difference ( M large cities  = 0.24, M cities  = 0.04, d  = 0.19). Private schools ( M  = 0.42) had a statistically significant higher school ICT readiness than those from public schools ( M  = −0.13, d  = 0.57). Class sizes that were between 16 to 30 students had much higher school ICT readiness factor scores ( M  = 0.04 to 0.07) than the ones that were “15 students or fewer” or “31 to more than 50 students” ( M  = −0.27 to −0.08). Table  5 and Fig.  2 provided both statistics and visual picture of how these groups performed in school ICT readiness.

School ICT readiness factor score and 95% confidence intervals across validation measures

5 Discussion

With the alignment method, this study examined the measurement invariance of school and teacher ICT readiness scale using PISA 2018 dataset. Using dataset from America, Europe, Africa, the Middle East, Asia and Oceania, it revealed that approximate level of measurement invariance existed for school ICT readiness subscale across all 57 countries, but overall non-invariance existed for teacher ICT readiness subscale. It provided a practical example of how to apply the newly developed alignment method into ICT-related scale with an aim of measurement invariance testing across multiple groups, which was a novel approach in ICT-related area that has not been widely known, implemented and accepted by researchers. It overcomes the tendinous numerous modification indices and error-prone procedures that occurs in traditional MGCFA and should be widely implemented in measurement invariance test for multiple groups.

Measurement invariance testing are recommended to be conducted before any cross-group ICT-related mean score comparisons for researchers and practitioners. Through alignment method, researchers and practitioners could gain a large amount of nuanced knowledge on the fit index and significance testing of the intercepts and loadings of a specific ICT construct, either the scale is on the student, teacher, or school level. Moreover, researchers and practitioners may broaden their understanding of scale’s cross-country differences by focusing on only noninvariant ICT items, and further identifying the sources of noninvariance. In our study, teacher ICT readiness subscale was identified to be noninvariant across the countries overall. It would be of high value to investigate the sources of noninvariance, especially when distinct cultural factors might affect the item responses. Understanding the existence of noninvariance and what contribute to the noninvariance in ICT readiness scale will assist researchers and practitioners with developing more culturally invariant items of scales in the further item development process.

The result from alignment method indicated that factor mean scores can be compared for the school ICT readiness subscale across the countries. By comparing the factor mean scores across the countries (regions) together, it was found that there was a big difference in the mean scores. For instance, Singapore showed the highest factor mean in school infrastructure of digital devices. This could be related with the Singapore’s long-term governmental support of technology use in schools. Early back to 2008, the ministry of education (MOE) in Singapore established five “Schools of the Future”, which served as a model in not only the curriculum design, teaching and learning but also the material resources (Lim, 2015 ). The independencies of the constituent elements among the self-organizing capacity, coevolution with other systems and fitness development and policymaking in ICT reform have brought Singapore’s stable leading position of school ICT readiness (Toh & So, 2011 ). Other countries such as Sweden, B-S-J-Z (regions of China), United Arab Emirates and United States all ranked among the top league of the assessment. Though these countries reside in different continents, they shared similar characteristics regarding digital resources as reported (Ikeda, 2020 ). Regardless of the socio-economic background of their students, a higher proportion of schools from these countries had an effective online learning support platform and computers with high-speed Internet connectivity and broad bandwidth; provided guidance on the use of digital devices and had specific programs to prepare students for disciplined Internet behavior. However, Japan, Mexico, Colombia, Argentina and Brazil were in the lowest rank. It is reported that less than 30% of students in these schools in these countries had similar platform as those in top ranked countries and it is mainly due to the large socio-economic disparity these counties have (Ikeda, 2020 ). The variance of the factor scores directly reflected that investment and support of school infrastructure of digital devices were treated quite differently with similar or distinct cultures and socio-economic disparities across the globe. Though development in technology infrastructure in schools is a worldwide investment, education equity is still a central issue of the education system across the world.

Notably, technology integration is a complex process, which requires cooperation from various aspects, such as teachers and school administrators work together with the classroom environment, curriculum, and everyday routines (Yang et al., 2021 ). It is a pity that teacher ICT readiness subscale could not be directly compared across the countries in our study. However, for teacher ICT readiness subscale, there was an alternative way to examine the cross-country differences in further studies. If we group the countries based on their similar characteristics or cultural background, then measurement invariant might hold. It also reflected the complexity involved in the attempt to attain cross-group invariance of both the factor loadings and item intercepts related to psychological assessment scales when multiple groups are involved and with a cross-cultural nature.

School ICT readiness factor scores were also found to be closely related with a few school background factors, such as school location, school type and class size. School locations have an impact on pattern of use and attitudes to technology (Looker & Thiessen, 2003 ). Schools from city and large cities usually have relatively sufficient financial support to develop its school digital infrastructure, therefore schools from these areas have much higher ICT readiness score. However, schools from rural areas such as village, small town or town are often lack of support from government or funding department, and their school ICT readiness scores accordingly are lower than those in the other areas. In terms of school type, there has been hot discussions of the division of responsibility between the private and public schools (Besley & Ghatak, 2001 ). A big contrast of the ICT readiness scores was found between public and private schools in our study. Another interesting finding was the differences of school ICT readiness scores across various class size. Hislop and Ellis ( 2004 ) reported that class size for on the online versions was on average 19.3 and 26 for the in-person class. In our analysis, class sizes ranged from 16 to 30 students had much higher factor means in school ICT readiness than those that were 15 students or fewer. Class size between 16 to 30 students can achieve optimal effect even in the school ICT readiness score, which was consistent with the previous research. During the COVID-19 pandemic period, most schools switched to the online teaching format, which could bring more pressure to schools and teachers in village, small town or town areas. It was also worth investigating the differences of school ICT readiness between the in-person class size and online class size.

6 Limitation

Several limitations must be acknowledged in the current study. Practically, PISA 2018 was conducted far before the eruption of COVID-19 across the world. The scale we estimated might not be able to accurately reflect the current global situation in the school and teacher ICT readiness. It is worth examining the same issue with the PISA 2021 dataset, which will better reflect the reality of global ICT readiness when facing significant change of teaching format. Methodologically, cross loadings still cannot be accommodated with the alignment method (Asparouhov & Muthén, 2014 ). Therefore, the invariance analysis was conducted separately for each subscale in our study. Lastly, although we are not able to explore possible mechanisms for non-invariance, future research should consider how external variables might explain non-invariance across cultures by using the alignment and/or alignment-within-CFA frameworks (Marsh et al., 2018 ).

7 Conclusion

With the novel alignment optimization approach in measurement invariance, this study was expected to provide researchers and other stakeholders with more nuanced knowledge of the school and teachers ICT readiness. The invariance of school ICT readiness subscale across the globe allowed the researchers to have a better understanding of how school ICT readiness performs in the participating countries. The non-invariance of teacher ICT readiness subscale encouraged the researchers to explore the substantive and methodological sources that cause the root source of non-invariance.

Data availability

The data that support the findings of this study are publicly available from the OECD PISA 2018 official website https://www.oecd.org/pisa/data/2018database/ .

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Wu, R., Yang, W., Rifenbark, G. et al. School and Teacher Information, Communication and Technology (ICT) readiness across 57 countries: The alignment optimization method. Educ Inf Technol 28 , 1273–1297 (2023). https://doi.org/10.1007/s10639-022-11233-y

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Interest in artificial intelligence has surged among K-12 and college educators, who are looking at ways it can be used to support both students and teachers. But in the early childhood arena, those discussions are still in the beginning stages. I asked Isabelle Hau, the executive director of Stanford Accelerator for Learning, to share about the potential benefits and challenges of AI in early learning. Our conversation below is edited for length and clarity.

Interest in AI has obviously surged the past couple of years in K-12, for both teachers and students. With early childhood, the use of AI may be a little less obvious. Have you noticed that trend in early childhood classrooms — are teachers interested in using AI or teaching about it?

Hau: I’m observing some activity in a few areas. One is interest in novel forms of assessment, or assessment areas that have been a big pain point for early childhood teachers for a long time, because observational assessments take a long time. There are some innovations that are starting to materialize in making assessments less visible, or invisible maybe, at some point. So discussion around how to leverage, for example, computer vision or some form of voice inputs in classrooms, or some gamified approaches that are AI-based.

Are there any specific ways you’re seeing AI technology emerge in early childhood classrooms?

Hau: At Stanford, we have one super interesting project that is not necessarily in a classroom but could be in a classroom context. It’s a tool my colleague, Dr. Philip Fisher, has developed called FIND that looks at child-adult interactions and takes video of that interaction. It is very expensive for humans to look at those videos and analyze the special moments in those interactions. Now, artificial intelligence is able to at least take a first pass at those interactions in a much more efficient manner. FIND is now an application for early childhood educators ; it used to be mostly for parents, initially.

Two of my colleagues, one in the school of medicine and one at the school of education, have partnered to build Google Glasses that children with challenges recognizing emotions can wear. And based on the advances that are happening with AI, especially in the area of image recognition, the glasses that young children can wear help them detect emotions from adults or other young people they are interacting with. Feedback, especially from parents and families of young children, is quite moving. Because for the first time, some of those young kids are able to actually recognize the emotions from the people they love.

Others have been working on language. Language is a complicated topic because we have, in the U.S., more and more children who speak multiple languages. As a teacher, it’s very complicated. Maybe you’re bilingual or trilingual at best, but if you have a child who speaks Vietnamese and a child who speaks Mandarin or Spanish, you can’t speak all of those languages as a teacher. So how do we correctively support those children with huge potential to thrive when they may not be proficient in English when they arrive in this classroom? Language is a really interesting use case for AI.

When you look up AI tools or products for early educators online, a lot comes up. Is there anything you would be cautious about?

Hau: While I’m excited about the potential, there are lots of risks. And here we are speaking about little ones, so the risks are even heightened. I’m excited about the potential for those technologies to support adults – I have a lot of questions about exposing young children.

For adults, where it’s very confusing right now is privacy. So no teacher should enter any student information that’s identifiable in any of those systems, especially if they are part of a district, without district approval.

That information should be highly private and is not meant to go in a system that seems innocuous but is, in fact, sharing information publicly. There are huge risks associated with that, the feeling of intimacy for a system that doesn’t exist. It’s a public place.

And then one concern is on bias. We’ve done some research at Stanford on bias sentiments in those systems, and we have shown that systems right now are biased against multilingual learners. I can see that myself, as a non-native English speaker. When I use those systems, especially when I use voice, they always mess up my voice and accent. These biases exist, and being very mindful that they do. Biases exist everywhere, but certainly they do exist in [AI] systems. And we have proven this in multiple ways. And then I also have huge concerns on equity. Because right now some AI systems are paid, some are free.

Are there any other ways you could see AI used to fill a need in early childhood?

Hau: Right now, a lot of parents are struggling to find care. You have people who are providing care – it could be center, it could be home-based, nanny, preschool, Head Start, you have all these different types. And then you have families. It’s a mess right now – the connection between the two. Of course it’s a mess because we don’t have enough funding, we don’t have enough slots, but generally, it’s a mess. This is an area that, over time, I’m hoping there will be better solutions powered by technology.

If I want to dine tonight at a restaurant in Palo Alto, this is really easy. Why don’t we have this for early childhood? ‘I’m a low-income parent living in X, and I’m looking for care in French, and I need hours from 8 to 5,’ or whatever it is. It would be really nice to have [technology] support for our millions of parents that are trying to find solutions like this. And right now, it doesn’t exist.

Do you have any tips for teachers who want to learn more about AI programs to use in class?

Hau: For safety, in particular, I really like the framework the EdSAFE AI Alliance has put together. It’s mostly oriented toward K-12, but I think a lot of their accommodations on when is it OK to use AI and when it is not are very clear and very teacher-friendly. There are some great resources at other organizations, like TeachAI or AI for Education , that I really like. At Stanford, we partner with those organizations because we feel like this is an effort that needs to be collaborative, where research needs to be at the table. We need to build coalitions for effective and safe and equitable use of those technologies.

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Technology and Interactive Media in Early Childhood Programs: What We’ve Learned from Five Years of Research, Policy, and Practice

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In one seemingly simple activity, Kimberly Buenger, early childhood special education teacher at Harmony Early Childhood Center, in the Olathe Unified School District, accomplishes goals related to technology use, language development, social skills, and assessment:

I serve children ages 3 to 5 in an integrated special education setting, with many demonstrating developmental delays. I use technology to support learning and development in several ways. One of my favorites is through a classroom job called the journalist. The journalist is responsible for taking pictures on the tablet during center time to document the activities of the other students in the class, and reporting about one picture during closing circle. The picture is shown through the projector so all the children can easily see it. I facilitate the discussion about the picture, adjusting my level of questioning for each child. This activity provides a natural way to assess a variety of communication skills, such as a student’s ability to recall events and answer a variety of wh questions. Giving the journalist the freedom to document the activity of his or her choosing makes the activity meaningful, increasing motivation to share in front of the larger group. The simplicity of the activity makes it easy to implement in a variety of settings, using different technology tools, with the only requirement being the ability to take a picture. (Personal communication with Kimberly Buenger, 2017.)

Kimberly’s budding journalists are a model for intentional, supportive use of technology in early childhood education.

Kimberly’s learning environment is far richer than anything we could have imagined just 10 years ago, when the Fred Rogers Center for Early Learning and Children’s Media convened a group of experts (including us) at a preconference symposium during the 2007 NAEYC professional development institute. Participants discussed the role of technology in early childhood professional development and in the lives of young children, especially in early childhood programs.

Realizing that few educators were as technologically savvy as Kimberly (even given the more limited technology options of the time), conference participants recommended that NAEYC and the Fred Rogers Center draft a joint position statement to help early childhood professionals integrate technology in developmentally appropriate ways. As Jerlean Daniel, then-executive director of NAEYC, recalls, the field was embroiled in serious debates:

Prior to the development of the current position statement on technology and young children, NAEYC had three statements—all in need of revision—on technology, television, and violence in the media. These were reflective of the grave concerns in the field about the exposure children had to violent themes delivered into their homes by television and the potentially inappropriate use of computers in early childhood education programs. As the quantity and diverse types of screens multiplied quickly, the field was quite divided about the developmental appropriateness of any technology for young children.Prior

The question of equity loomed large as well. Many children whose home language was not English used television as a tool to learn English. For Black children from low-income families living in underresourced communities, television was often a heavily used source of entertainment. White children from middle-income families were more likely to have a variety of screens at home, while rural children typically had spotty access to the Internet.

Such charged controversy has always signaled the need for an NAEYC position statement. But we needed a highly respected partner, one with a proven track record for developmentally appropriate use of technology. No entity came close to the stellar reputation of the Fred Rogers Center for Early Learning and Children’s Media, a unique combination of child development and media knowledge. The transparent back-and-forth of consensus building was not easy, but all parties knew their concerns had been given serious consideration. The various factions saw their issues acknowledged in the final position statement. (Personal communication with Jerlean Daniel, 2017.)

Building consensus was neither fast nor easy, but in 2012, NAEYC and the Fred Rogers Center issued a joint position statement titled “Technology and Interactive Media as Tools in Early Childhood Programs Serving Children from Birth through Age 8.” (For the full position statement and a two-page summary with the key messages, visit www.naeyc.org/content/technology-and-young-children .)

Key messages

Grounded in developmentally appropriate practice (Copple & Bredekamp 2009), the statement provided a clear framework for effective, appropriate, and intentional use of technology and media with young children in the digital age of smartphones, multitouch screens, and apps. The following key messages were intended to guide educators in early childhood settings on the selection, use, integration, and evaluation of technology tools for learning:

• Intentional use requires early childhood teachers and administrators to have information and resources regarding the nature of these tools and the implications of their use with children.
• Limitations on the use of technology and media are important.
• Special considerations must be given to the use of technology with infants and toddlers.
• Attention to digital citizenship and equitable access is essential.
• Ongoing research and professional development are needed.

Our long-term vision was to develop “digitally literate educators who . . . have the knowledge, skills, and experience to select and use technology tools and interactive media that suit the ages and developmental levels of the children in their care, and . . . know when and how to integrate technology into the program effectively” (NAEYC & Fred Rogers Center 2012, 4).​

The NAEYC/Fred Rogers Center’s joint statement has served as one of my important resources about technology and its effect on young children. As stated on the technology section of our website, at the Pike School “we believe that a successful technology program is measured not so much by which technologies you use or by your frequency of using them but rather by what you choose to do with technology and how you use it.” —Jennifer J. Zacharis, Technology Integrationist/Coach, Pike School, Andover, Maryland

Now that the position statement is five years old, we are seeing more and more digitally literate educators. Take Sydney E. Spann, for example. A kindergarten teacher and innovation coach at Rodriguez Elementary, in Austin, Texas, Spann carefully selects technology to help children build knowledge:

Early last October, my kindergartners were working hard to learn all about fall, though it was still too early to see many of the indicators of the season change here in central Texas. One marker of the season that my students were able to observe was butterfly migration. Swarms of butterflies were migrating through Texas, and we were lucky enough to walk under a cloud of monarchs on our way inside from recess.

We immediately looked at pictures online of the area in Michoacán, Mexico, where many of these butterflies would end their journey. Then I showed my students the Butterflies of Austin iPad app. All the introduction they needed was a quick demonstration of how to change the pictures, and they were ready to explore and record! They spent days looking through the photos of butterflies, caterpillars, and pupae and recording the images in their science notebooks. My students’ use of this simple app showed me that the way children interact with technology is not that different from the way they interact with any other learning tool. It’s not flashy features and bright colors that engage them, but simply the fact that there is new knowledge that can be gained. (Personal communication with Sydney E. Spann, 2017.)

Alignment with recent statements, guidelines, and reports

The NAEYC and Fred Rogers Center joint position statement was the first in a series of guidelines and research-based recommendations about technology and young children published by organizations focused on child development and early childhood education (Donohue 2016, 2017). The following resources summarize recent research, which reinforces central tenets of the NAEYC and Fred Rogers Center position statement.

• “Screen Sense: Setting the Record Straight—Research-Based Guidelines for Screen Use for Children under 3 Years Old.” 2014. ZERO TO THREE. ( www.zerotothree.org/resources/series/screen-sense-setting-the-record-straight )
• “Using Early Childhood Education to Bridge the Digital Divide.” 2014. Santa Monica, CA: RAND Corporation. ( www.rand.org/pubs/perspectives/PE119.html )
• “Using Technology Appropriately in the Preschool Classroom.” 2015. HighScope Extensions 28 (1): 1–12. ( http://membership.highscope.org/app/issues/162.pdf )
• “Early Learning and Educational Technology Policy Brief.” 2016. US Department of Education and Department of Health and Human Services. ( https://tech.ed.gov/files/2016/10/Early-Learning-Tech-Policy-Brief.pdf )
• “Media and Young Minds.” 2016. Policy statement. American Academy of Pediatrics, Council on Communications and Media. ( http://pediatrics.aappublications.org/content/pediatrics/early/2016/10/19/peds.2016-2591.full.pdf )
• “Technology and Interactive Media for Young Children: A Whole Child Approach Connecting the Vision of Fred Rogers with Research and Practice.” 2017. Fred Rogers Center ( www.fredrogerscenter.org/frctecreport ) and the Technology in Early Childhood (TEC) Center at Erikson Institute ( http://teccenter.erikson.edu/tec/tecfrcreport/ ).

Two of the three most recent policy statements were released by the American Academy of Pediatrics (AAP) and the US Departments of Education and Health and Human Services (ED/DHHS) on the same day in October 2016. The AAP statement on “Media and Young Minds” includes recommendations for parents about technology and media use in the home with children from birth through age 8.

According to the AAP, parents need to be mindful about the risks of displacing or replacing essential developmental experiences in the early years due to overuse of technology. Limits on media use for children birth to 18 months, 18 to 24 months, and 2 to 5 years can provide adequate time for young children to play and be physically active, to spend time indoors and outdoors, to have social time with friends, to enjoy one-to-one time with siblings and parents, and for family time without screen disruptions. Parents are encouraged to create a family media plan that includes tech-free zones and times, including no media use during meals and one hour before bedtime. The AAP emphasis on joint engagement, relationships with family and friends, preserving essential early childhood experiences, and careful selection of appropriate, high-quality content are closely aligned with the principles and guidelines in the NAEYC and Fred Rogers Center joint position statement.

In this era of uncertainty around the role of technology with all of us, especially young children, I am deeply appreciative of the position statement for offering a thorough examination of the strengths and possibilities of technology as well as the possible misuses. Through this research, we have seen educators willing to try new things and open doors to new worlds for themselves and children. —Alex Cruickshank, Community Outreach Specialist, Boulder Journey School, Boulder, Colorado 

The ED/DHHS report “Early Learning and Educational Technology Policy Brief” includes four guiding principles:

• Technology, when used properly, can be a tool for learning
• Technology should be used to increase access to learning opportunities for all children
• Technology can be used to strengthen relationships among parents, families, early educators, and young children
• Technology is more effective for learning when adults and peers interact or coview with young children

In regard to screen time, ED/DHHS ask that families and early educators consider far more than time when evaluating technology. The report points to content quality, context, and the extent to which technology could be used to enhance relationships as key factors. These guiding principles from AAP and ED/DHHS build on and deepen the key messages from the NAEYC and Fred Rogers joint position statement, adding to our understanding of emerging research-based practices.

The fact that these two organizations are working together serves as an inspiration and reminder to others (teachers, parents, home visitors, therapists, children’s media producers, etc.) to work together and support each other as we learn to navigate the digital age. —Stacey Landberg, Speech-Language Pathologist, American Speech–Language–Hearing Association

As the NAEYC and Fred Rogers Center joint position statement said, “When used wisely, technology and media can support learning and relationships. Enjoyable and engaging shared experiences that optimize the potential for children’s learning and development can support children’s relationships both with adults and their peers” (2012, 1).

In many ways, this finding simply codifies what digitally literate educators have already demonstrated. Used well—as one of many tools to enhance exploration and learning—technology brings wonder and excitement to everyday learning environments. As Claudia Haines, a youth services librarian at the Homer Public Library, in Homer, Alaska, explains, those savvy educators and those rich environments are not found only in schools:

Several mornings a week, preschoolers and toddlers scamper through the front door of the Homer Public Library with grown-ups—moms, dads, grandparents, neighbors, or nannies—in tow. Year-round, the centerpiece of these weekly visits for many families is Storytime, a free program that uses high-tech and low-tech media to foster lifelong learning and early literacy skills. The public library connects families from all walks of life with information and resources, as well as each other. At Storytime, we read, talk, play, sing, explore, and create together.   For families who cannot afford preschool and for those supplementing it, the library’s Storytime offers supported access to thoughtfully reviewed traditional and new media. And just as important, in the Storytime setting grown-ups also learn how to use media of all kinds in positive ways to support their young children’s learning and development. Every book, song, app, art supply, and STEM activity we share is chosen with intention because it is high quality and supports research-based early literacy practices. (Personal communication with Claudia Haines, 2017.)

Consensus emerges

A synthesis of the position statements, reports, research reviews, guidelines, and recommendations released between 2012 and 2017 identifies strong agreement on a set of foundational elements necessary for successful technology integration with young children (Donohue 2015, 2016, 2017; Donohue & Schomburg 2015). For early childhood educators and the field, the takeaways about what matters most include:

• Relationships—A child’s use of media and technology should invite and enhance interactions and strengthen relationships with peers, siblings, and parents.
• Coviewing and active parent engagement—Using media together improves learning. Talking about what the child is seeing and doing, and connecting what is on the screen with real-life experiences, builds language skills and vocabulary, encourages interactions, and strengthens relationships.
• Social and emotional learning—Technology should be used in ways that support positive social interactions, mindfulness, creativity, and a sense of initiative.
• Early childhood essentials—Technology use should not displace or replace imaginative play, outdoor play and nature, creativity, curiosity and wonder, solitary and shared experiences, or using tools for inquiry, problem solving, and exploring the world.
• Content, context, and quality—The quality of what children watch on screens is more important than how much they watch.
• Media creation—Young children are moving from being media consumers to media creators. New digital tools provide the opportunity for making and creating at their fingertips.
• Family engagement—In the digital age, technology tools can improve communication between home and school, making it easier to exchange information and share resources. Engaging families improves outcomes for children.
• Adult habits—As the primary role models for technology and media use, adults should be aware of and set limits on their own technology and media use when children are present and focus on children having well-rounded experiences, including moderate, healthy media use.
• Teacher preparation—Preservice teacher education and in-service professional development are needed to provide educators with the media literacy and technology skills to select, use, integrate, and evaluate technology tools for young children.
• Media mentors—Young children need trusted adults who are active media mentors to guide them safely in the digital age.

Perhaps not surprisingly, these takeaways elaborate on a key point in the joint position statement: “Early childhood educators always should use their knowledge of child development and effective practices to carefully and intentionally select and use technology and media if and when it serves healthy development, learning, creativity, interactions with others, and relationships” (NAEYC & Fred Rogers Center 2012, 5).

The Fred Rogers Center saw progress as we implemented the position statement across professional development workshops, reaching thousands of early childhood educators. Our perspective has not changed on the role of technology: we view it as an additional tool for young children, early childhood educators, and parents. The biggest challenge moving forward is providing practical guidance to families. Early in his career, Fred Rogers listed six necessities for children to learn. As the Fred Rogers Center moves forward, we plan to apply those same necessities to technology use with young children. Following Fred’s lead, we ask:

Does it … 1. Create a sense of worth? 2. Create a sense of trust? 3. Spark curiosity? 4. Have the capacity to foster you to look and listen carefully? 5. Encourage the capacity to play? 6. Allow for moments of solitude?

As we develop initiatives around this concept, we look forward to continuing to champion the principles and guidelines from the position statement and working with our partners to implement a strategy that is based on positive and supportive messaging. —Rick Fernandes, Executive Director, Fred Rogers Center

Where to from here?

Although the consensus takeaways show that much progress has been made since the debates of a decade ago, there is still much to learn. We invite you to join us in building on our growing understanding of what matters most and of evidence-based practices. We believe that blending interactive technology and personal interactions with others offers the most promise for using technology as a tool for whole child development in the digital age.

Fred Rogers demonstrated how to use the technology of his day to support early learning with an emphasis on relationships, communication, and social and emotional development. He was a child development expert who always kept the child first and integrated technology in the service of positive self-esteem and healthy relationships. As Fred Rogers said, “No matter how helpful they are as tools (and, of course, they can be very helpful tools), computers don’t begin to compare in significance to the teacher–child relationship, which is human and mutual. A computer can help you to learn to spell H-U-G, but it can never know the risk or the joy of actually giving or receiving one” (Rogers 1994, 89). Fred was a media mentor to countless children, parents, families, and caregivers—and his approach will continue to guide our work.

Five years ago, NAEYC and the Fred Rogers Center took a bold step in laying out a vision for the critical role technology can play in early learning programs. While the position statement was clearly about technology, it wasn’t about which apps to use or how to unlock digital coding. It was directed at early childhood educators and what they, as classroom and program leaders, must know and be able to do in order to effectively use technology.   Five years later, that is still the most important aspect of our work with technology. Neuroscience and behavioral science point to unparalleled cognitive, physical, and social and emotional growth in young children. These sciences have also shown us that our lifelong approaches to learning—things like initiative, curiosity, motivation, engagement, problem solving, and self-regulation—are at their height of development in the early years.   Early childhood educators must redouble their efforts to identify and deploy the most effective uses of technology in order to maximize the learning and development of young children. Think about the acquisition of oral language, the developmental progression of mathematics, the growth of self-regulation and inhibitory control, the mechanics of working memory, and the facilitation of relationships with children and their families—early childhood educators must master a great deal of knowledge and skill in each of these areas. There are many ways effective uses of technology and digital media can support early childhood educators in preparing young children for success in school and in life. —Rhian Evans Allvin, Chief Executive Officer, NAEYC

To read more stories and testimonials and view photos of the NAEYC/Fred Rogers Center joint position statement in practice, visit the Technology in Early Childhood (TEC) Center at Erikson Institute: http://teccenter.erikson.edu/tec/positionstatement5/ .   To learn more about the joint position statement, key messages, and examples of effective practice and technology that support early learning, visit:   NAEYC on Technology and Young Children www.NAEYC.org/content/technology-and-young-children   Fred Rogers Center for Early Learning and Children’s Media at Saint Vincent College www.fredrogerscenter.org   Technology in Early Childhood (TEC) Center at Erikson Institute www.teccenter.erikson.edu/

Copple, C., & S. Bredekamp, eds. 2009. Developmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth through Age 8 . 3rd ed. Washington, DC: National Association for the Education of Young Children (NAEYC).   Donohue, C., ed. 2015. Technology and Digital Media in the Early Years: Tools for Teaching and Learning . New York: Routledge; Washington, DC: NAEYC.   Donohue, C. 2016. “Technology in Early Childhood Education.” In The SAGE Encyclopedia of Contemporary Early Childhood Education , vol. 3, eds. D. Couchenour & J.K. Chrisman, 1344–48. Thousand Oaks, CA: Sage.   Donohue, C. 2017. “Putting the ‘T’ in STEM for the Youngest Learners: How Caregivers Can Support Parents in the Digital Age.” ZERO TO THREE, 37 (5): 45–52. Donohue, C., & R. Schomburg. 2015. “Preparing Early Childhood Educators for the Digital Age.” In Technology and Digital Media in the Early Years: Tools for Teaching and Learning, ed. C. Donohue, 36–53. New York: Routledge; Washington, DC: NAEYC.    NAEYC & Fred Rogers Center for Early Learning and Children’s Media. 2012. “Technology and Interactive Media as Tools in Early Childhood Programs Serving Children from Birth through Age 8.” Joint position statement. Washington, DC: NAEYC; Latrobe, PA: Fred Rogers Center at St. Vincent College. www.naeyc.org/content/technology-and-young-children .   Rogers, F. 1994. You Are Special: Words of Wisdom from America’s Most Beloved Neighbor . New York: Penguin.

Photographs: © iStock

Chip Donohue, PhD, is dean of distance learning and continuing education and director of the TEC Center at Erikson Institute and Senior Fellow and advisor of the Fred Rogers Center for Early Learning and Children’s Media at Saint Vincent College, in Latrobe, Pennsylvania. Donohue and Roberta Schomburg cochaired the working group that revised the 2012 NAEYC & Fred Rogers Center Joint Position Statement on Technology and Interactive Media as Tools in Early Childhood Programs Serving Children from Birth through Age 8.

Roberta Schomburg, PhD, is professor emerita at Carlow University in Pittsburgh, Pennsylvania; senior fellow at the Fred Rogers Center for Early Learning and Children’s Media, and a consultant to the Fred Rogers Company and Daniel Tiger’s Neighborhood. She was an NAEYC Governing Board member from 2010–2014.

Vol. 72, No. 4

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This curriculum is designed to prepare students to work with children from infancy through age eight in a variety of early childhood settings. The curriculum has a core of 34 credit hours directly related to early childhood education. The curriculum is designed so that it can be completed within four semesters, but it can be extended over a longer time. A suggested course sequence for full-time students follows; part-time students should consult an advisor.  This program can be completed either on campus or online.

General Education Course Selections

Click here to view the Foundation/Distribution Courses    for selection to fulfill the General Education course requirements.

Suggested Course Sequence

Students should complete the required  English  and  Math  foundation courses within the first 24 credit hours. All students should review the Program Advising Guide and consult an advisor .

First Semester

• ENGL 101 - Introduction to College Writing 3 semester hours *
• Mathematics Foundation 3 semester hours (MATF)
• COMM 108 - Foundations of Human Communication 3 semester hours (GEEL)
• EDUC 119 - Introduction to Early Childhood Education 3 semester hours
• PSYC 100 - General Psychology 3 semester hours (BSSD)

Second Semester

• ENGL 102 - Critical Reading, Writing, and Research 3 semester hours (ENGF)
• EDUC 115 - Child Health, Safety, and Nutrition 3 semester hours
• EDUC 135 - Child Growth and Development 3 semester hours
• EDUC 153 - Infant and Toddler Development and Curriculum Planning 3 semester hours
• EDUC 154 - School-Age Child Care 3 semester hours
• Any General Education Arts Distribution Course   3 semester hours ( ARTD or HUMD) ***

Third Semester

• EDUC 136 - Curriculum Planning in Early Childhood Education 3 semester hours
• EDUC 170 - First Start: Care of Infants and Toddlers with Disabilities 3 semester hours
• EDUC 201 - Introduction to Special Education 3 semester hours
• EDUC 224 - Social-Emotional Development in Young Children 3 semester hours
• EDUC 227 - Administering Early Childhood Programs 3 semester hours
• Any General Education Behavioral and Social Sciences Distribution Course  3 semester hours (BSSD)

Fourth Semester

• EDUC 180 - Children’s Literature 3 semester hours
• EDUC 243 - Processes and Acquisition of Literacy 3 semester hours
• EDUC 208 - Observation and Assessment of Young Children 3 semester hours
• EDUC 210 - Curriculum Seminar-Science and Mathematics for Young Children 2 semester hours
• EDUC 233 - Practicum in Early Childhood Education 3 semester hours
• Natural Sciences Distribution with Lab 4 semester hours (NSLD) **

Total Credit Hours: 60

* ENGL 101   / ENGL 101A   , if needed for ENGL 102   , or elective.

** BIOL 101    or PSCI 101    or PSCI 102    recommended.

*** AAS programs require one 3-credit Arts or Humanities General Education course.  ISTD 173    is recommended.

This program can be completed either on campus or online.

Program Outline / Degree Requirements

General education requirements, foundation courses.

• ENGL 102 - Critical Reading, Writing, and Research 3 semester hours

Distribution Courses

• Any General Education Arts Distribution Course 3 semester hours ( ARTD or HUMD) ***

General Education Elective

• Any General Education Behavioral and Social Sciences Distribution Course 3 semester hours (BSSD)

Program Requirements

Program outcomes.

Upon completion of this program, a student will be able to:

• Describe the theories and principles of child development and learning and apply the theories and principles to his or her classroom teaching.
• Identify the issues, trends, and historical events in the field of early childhood education.
• Use systematic observations, documentation, and other effective assessment strategies in a responsible way to positively influence children’s learning and development.
• Demonstrate knowledge of supporting and empowering families and communities through respectful, reciprocal relationships.
• Demonstrate understanding of content areas and apply developmentally appropriate approaches to enhance children’s learning and development.
• Create healthy, respectful, supportive, and challenging learning environments to promote children’s learning and development.
• Design, implement, and evaluate meaningful, challenging curricula to promote positive outcomes for all young children.
• Be reflective practitioners to reflect and use the most effective methods of guidance and teaching when working with children.
• Identify and conduct themselves as early childhood professionals who use ethical guidelines and National Association for the Education of Young Children standards related to early childhood practice and who are advocates for sound educational practices and policies.
• Demonstrate excellent written, verbal, critical thinking, and problem-solving skills, which will allow them to effectively make connections between prior knowledge/experience and new learning.

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