education technology articles

REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.  

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

• Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
• Survey the best available evidence on interventions that match those conditions—the evidence; and
• Closely monitor the results of innovations before they are scaled up—the prognosis.

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The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

• Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
• Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
• Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

• Scaling up quality instruction, such as through prerecorded quality lessons.
• Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
• Expanding opportunities to practice.
• Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas.  The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

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What 126 studies say about education technology

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In recent years, there has been widespread excitement around the transformative potential of technology in education. In the United States alone, spending on education technology has now exceeded $13 billion . Programs and policies to promote the use of education technology may expand access to quality education, support students’ learning in innovative ways, and help families navigate complex school systems.

However, the rapid development of education technology in the United States is occurring in a context of deep and persistent inequality . Depending on how programs are designed, how they are used, and who can access them, education technologies could alleviate or aggravate existing disparities. To harness education technology’s full potential, education decision-makers, product developers, and funders need to understand the ways in which technology can help — or in some cases hurt — student learning.

To address this need, J-PAL North America recently released a new publication summarizing 126 rigorous evaluations of different uses of education technology. Drawing primarily from research in developed countries, the publication looks at randomized evaluations and regression discontinuity designs across four broad categories: (1) access to technology, (2) computer-assisted learning or educational software, (3) technology-enabled nudges in education, and (4) online learning.

This growing body of evidence suggests some areas of promise and points to four key lessons on education technology.

First, supplying computers and internet alone generally do not improve students’ academic outcomes from kindergarten to 12th grade, but do increase computer usage and improve computer proficiency. Disparities in access to information and communication technologies can exacerbate existing educational inequalities. Students without access at school or at home may struggle to complete web-based assignments and may have a hard time developing digital literacy skills.

Broadly, programs to expand access to technology have been effective at increasing use of computers and improving computer skills. However, computer distribution and internet subsidy programs generally did not improve grades and test scores and in some cases led to adverse impacts on academic achievement. The limited rigorous evidence suggests that distributing computers may have a more direct impact on learning outcomes at the postsecondary level.

Second, educational software (often called “computer-assisted learning”) programs designed to help students develop particular skills have shown enormous promise in improving learning outcomes, particularly in math. Targeting instruction to meet students’ learning levels has been found to be effective in improving student learning, but large class sizes with a wide range of learning levels can make it hard for teachers to personalize instruction. Software has the potential to overcome traditional classroom constraints by customizing activities for each student. Educational software programs range from light-touch homework support tools to more intensive interventions that re-orient the classroom around the use of software.

Most educational software that have been rigorously evaluated help students practice particular skills through personalized tutoring approaches. Computer-assisted learning programs have shown enormous promise in improving academic achievement, especially in math. Of all 30 studies of computer-assisted learning programs, 20 reported statistically significant positive effects, 15 of which were focused on improving math outcomes.

Third, technology-based nudges — such as text message reminders — can have meaningful, if modest, impacts on a variety of education-related outcomes, often at extremely low costs. Low-cost interventions like text message reminders can successfully support students and families at each stage of schooling. Text messages with reminders, tips, goal-setting tools, and encouragement can increase parental engagement in learning activities, such as reading with their elementary-aged children.

Middle and high schools, meanwhile, can help parents support their children by providing families with information about how well their children are doing in school. Colleges can increase application and enrollment rates by leveraging technology to suggest specific action items, streamline financial aid procedures, and/or provide personalized support to high school students.

Online courses are developing a growing presence in education, but the limited experimental evidence suggests that online-only courses lower student academic achievement compared to in-person courses. In four of six studies that directly compared the impact of taking a course online versus in-person only, student performance was lower in the online courses. However, students performed similarly in courses with both in-person and online components compared to traditional face-to-face classes.

The new publication is meant to be a resource for decision-makers interested in learning which uses of education technology go beyond the hype to truly help students learn. At the same time, the publication outlines key open questions about the impacts of education technology, including questions relating to the long-term impacts of education technology and the impacts of education technology on different types of learners.

To help answer these questions, J-PAL North America’s Education, Technology, and Opportunity Initiative is working to build the evidence base on promising uses of education technology by partnering directly with education leaders.

Education leaders are invited to submit letters of interest to partner with J-PAL North America through its  Innovation Competition . Anyone interested in learning more about how to apply is encouraged to contact initiative manager Vincent Quan .

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Articles on Technology in Education

Displaying 1 - 20 of 21 articles.

Do smartphones belong in classrooms? Four scholars weigh in

Louis-Philippe Beland , Carleton University ; Arnold Lewis Glass , Rutgers University ; Daniel G. Krutka , University of North Texas , and Sarah Rose , Staffordshire University

ChatGPT is the push higher education needs to rethink assessment

Sioux McKenna , Rhodes University ; Dan Dixon , University of Sydney ; Daniel Oppenheimer , Carnegie Mellon University ; Margaret Blackie , Rhodes University , and Sam Illingworth , Edinburgh Napier University

5 challenges of doing college in the metaverse

Nir Kshetri , University of North Carolina – Greensboro

Six benefits that the metaverse offers to colleges and universities

School tech: teachers explain what they need to make it work better

Craig Blewett , University of KwaZulu-Natal

Marrying technology and home language boosts maths and science learning

Mmaki Jantjies , University of the Western Cape

Why putting the words ‘learning’ and ‘Facebook’ together isn’t an oxymoron

Technology can help kids learn, but only if parents and teachers are involved

Yashwant Ramma , Mauritius Institute of Education

To stay in the game universities need to work with tech companies

Martin Hall , University of Cape Town

It’s true, internet surfing during class is not so good for grades

Susan Ravizza , Michigan State University

Why schools shouldn’t approach technology like businesses once did

Schools must get the basics right before splashing out on technology

Technology is no longer a luxury for universities – it’s a necessity

Michael Rowe , University of the Western Cape

Which digital books work best in the classroom?

Natalia Kucirkova , The Open University

Why replacing teachers with automated education lacks imagination

George Veletsianos , Royal Roads University

Unlocking the habits of Britain’s smartphone generation

Leslie Haddon , London School of Economics and Political Science

The value of MOOCs lies with employers

Dan Jerker B. Svantesson , Bond University

How to choose the best educational app for your child

Videogames should be a teacher’s best friend

Michael Kasumovic , UNSW Sydney

As laptop scheme ends, what next for families and learning?

Jason M Lodge , The University of Melbourne

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2024 National Educational Technology Plan Addresses Three Digital Divides

Rebecca Torchia is a web editor for  EdTech: Focus on K–12 . Previously, she has produced podcasts and written for several publications in Maryland, Washington, D.C., and her hometown of Pittsburgh.

The U.S. Department of Education released the highly anticipated update to the National Educational Technology Plan Monday. The 2024 NETP focuses on closing the digital use, access and design divides.

“The Biden-Harris Administration has made bold investments aimed at closing the digital divide and ensuring all students can equitably access the latest digital tools and technology,” U.S. Secretary of Education Miguel Cardona said in a press release . “The 2024 National Educational Technology Plan is a forward-thinking approach to reframing and realizing the potential of educational technology to enhance the instructional core, reduce achievement gaps, and improve student learning in our schools.”

The NETP has been updated numerous times since its initial release in 2000. Before Monday’s announcement, the most recent update was in 2016. These past versions of the resource served as an examination of the state of K–12 education . The 2024 NETP, however, departs from this structure to instead highlight three barriers, or digital divides, that limit educational technology’s ability to transform teaching and learning.

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Ed Tech Opportunities Through the Lens of Three Digital Divides

The breadth of the digital divide in K–12 education was brought to light at the onset of the pandemic. Students’ ability to access and use digital resources varied greatly across the country, sparking conversations about equity and opportunity.

In this update to the NETP, the digital divide is categorized in three ways: the digital use divide, the digital access divide and the digital design divide. Each addresses opportunities pertaining to the use and availability of educational technology.

The digital use divide describes the opportunities students have to use technology to further their learning. According to the press release, this includes “dynamic applications of technology to explore, create, and engage in critical analysis of academic content and knowledge.”

The digital access divide describes students’ and educators’ equitable access to technology , which encompasses devices, digital content and connectivity. Accessibility and digital health, digital safety and digital citizenship are key elements of digital access.

The digital design divide pertains to educators’ ability to expand their professional development and their capabilities to design learning experiences enabled by technology.

Within the 2024 NETP, each of these divides is mapped to the ways schools, districts and states can design valuable learning experiences with ed tech. The plan also provides action-oriented recommendations for closing the three digital divides , with examples from across the country.

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Trends and Topics in Educational Technology, 2022 Edition

• Column: Guest Editorial
• Published: 23 February 2022
• Volume 66 , pages 134–140, ( 2022 )

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• Joshua M. Rosenberg 2  

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This editorial continues our annual effort to identify and catalog trends and popular topics in the field of educational technology. Continuing our approach from previous years (Kimmons, 2020 ; Kimmons et al., 2021 ), we use public internet data mining methods (Kimmons & Veletsianos, 2018 ) to extract and analyze data from three large data sources: the Scopus research article database, the Twitter #edtech affinity group, and school and school district Facebook pages. Such data sources can provide valuable insights into what is happening and what is of interest in the field as educators, researchers, and students grapple with crises and the rapidly evolving uses of educational technologies (e.g., Kimmons et al., 2020 ; Trust et al., 2020 ; Veletsianos & Kimmons, 2020 ). Through this analysis, we provide a brief snapshot of what the educational technology field looked like in 2021 via each of these lenses and attempt to triangulate an overall state of our field and vision for what may be coming next.

What Were Trending Topics in Educational Technology Journals in 2021?

Educational technology research topics for 2021 were very similar to previous years, with a few exceptions. In total, we collected titles for 2368 articles via Scopus published in top educational technology journals as identified by Google Scholar. We then analyzed keyword and bigram (two words found together) frequencies in titles to determine the most commonly referenced terms. To assist in making sense of results, we also manually grouped together keywords and bigrams into four information types: contexts, methods, modalities, and topics. Contexts included terms referring to the research setting, such as “COVID-19” or “higher education.” Methods included terms referring to research methods involved in the article, such as “systematic review” or “meta-analysis.” Modalities included terms referring to the technical modality through which the study was occurring, such as “virtual reality” or “online learning.” Last, Topics included terms referring to the intervention, objective, or theoretical goal of the study, such as “computational thinking,” “learning environment,” or “language learning.” The most common bigrams and keywords for each type may be found in Table  1 ; a few items of interest follow.

Bigrams generally provide more specificity for interpreting meaning than do keywords, simply because keywords might have greater variety in usage (e.g., “school” might be used in the context of “primary school,” “secondary school,” “school teacher,” and so forth). So, when interpreting Table 1 , the bigram column is generally more useful for identifying trending topics, though the keyword column may at times be helpful as a clarifying supplement.

“Computational thinking” and “learning environments” were the two most-researched topical bigrams in 2021, and “virtual reality” and “online learning” were the most-researched modality bigrams. Most-referenced methods included “systematic review” and “meta-analysis,” which is noteworthy because such methods are used to conduct secondary analyses on existing studies, and their dominance may suggest an interest in the field to identify what works and to synthesize findings across various contexts within a sea of articles that is ever-increasing in size.

Due to the ongoing COVID-19 pandemic, this contextual term was regularly mentioned in many article titles (5.4%). “Pandemic” (3.4%), “emergency” (1.2%), and “shift to” (e.g., digital, online, blended; 0.9%) were also commonly referenced. This suggests that as the world continues to grapple with this multifaceted crisis, educational technology researchers are heavily engaged in addressing educational concerns associated with it (and remote teaching, particularly).

Grade level references in titles further suggested that educational technology research is being conducted at all levels but that it is most prominent at the higher education or post-secondary level and reduces in frequency as grade levels go down, with high school or secondary terms being more prominent than elementary or primary terms, with “higher education” (3.5%) being referenced twice as frequently as “K-12” (1.7%). This is noteworthy as it suggests that research findings associated with educational technology are currently mainly focused on older (and even adult) students and that if results are applied to understanding learners generally, then the needs of adolescents and younger children may currently be relatively underrepresented.

What Were Trending #Edtech Topics and Tools on Twitter in 2021?

Twitter is a valuable source of information about trends in a field because it allows researchers and practitioners to share relevant resources, studies, and musings and categorize posts via descriptive hashtags. The #edtech hashtag continued to be very popular during 2021, and we collected all original tweets (ignoring retweets) that included the #edtech hashtag for the year. This included 433,078 original tweets posted by 40,767 users, averaging 36,090 tweets per month ( SD  = 2974).

Because users can include multiple hashtags on a tweet, we aggregated the frequencies of additional (co-occurring) hashtags to determine the intended audiences (e.g., #teachers, #k12) and content topics (e.g., #elearning, #ai) of tweets. Some of the most popular additional hashtags of each type are presented in Table  2 . To better understand results, we also calculated the representation of each additional hashtag in the overall dataset (e.g., 2% of all #edtech tweets also included the #teachers hashtag) and the diversity of authorship (i.e., the number of users divided by the number of tweets). This diversity score was helpful for understanding how some hashtags were used by relatively few accounts for purposes such as product promotion. For example, the #byjus hashtag, which refers to an educational technology company founded in India, was tweeted 19,546 times. Still, the diversity score was only 3%, revealing that though this was a very popular hashtag in terms of tweet counts, it was being included by relatively few accounts at very high frequencies, such as via focused marketing campaigns.

Notably, several community or affinity space hashtags (Carpenter & Krutka, 2014 ; Rosenberg et al., 2016 ) were among the most common included with #edtech, such as #edchat, #edutwitter, and #teachertwitter. In particular, 13.9% of #edtech tweets also were tagged as #educhat, and 25.7% of #educhat tweets were also tagged as #edtech, revealing relatively high synchronicity between these two spaces. Furthermore, regarding institutional level, #k12 ( n  = 1712) and #highered ( n  = 1770) exhibited similar user counts, as did #school ( n  = 1284) and #highereducation ( n  = 1161), but, interestingly, the #k12 and #school hashtags exhibited nearly twice as many tweets as their #highered and #highereducation counterparts. This suggests that although the communities tweeting about topics for each group may be of similar size, the K-12 community was much more active than the higher education community.

Regarding topics, #elearning, #onlinelearning, #remotelearning, #distancelearning, #virtuallearning, and #blendedlearning were represented at a relatively high rate (in 16.1% of tweets), perhaps reflecting ongoing interest associated with #covid19. Other prominent topical hashtags included emerging technologies, such as #ai ( n  = 2112), #vr ( n  = 917), #ar ( n  = 679), and #blockchain ( n  = 545), as well as subject areas (e.g., #stem) and general descriptors (e.g., #innovation).

Furthermore, one of the primary reasons for tweeting is to share resources or media items. An analysis of these #edtech tweets revealed that 94.4% included either a link to an external site or an embedded media resource, such as an image or video. Regarding external links, prominent domains included (a) news sites, such as edsurge.com , edtechmagazine.com , or edutopia.org , (b) other social media, such as linkedin.com , instagram.com , or facebook.com , (c) multimedia resources, such as youtube.com , anchor.fm, or podcasts.apple.com , and (d) productivity and management tools, such as docs.google.com , forms.gle, or eventbrite.com (cf., Table  3 ).

Twitter communications in 2021 regarding #edtech included chatter about a variety of topics and resources. Shadows of #COVID-19 might be detected in the prevalence of this hashtag with others, like #remotelearning and #onlinelearning, but in many ways it seems that conversations continued to focus on issues of #education and #learning, as well as emerging topics like #ai, #vr, and #cybersecurity, suggesting some level of imperviousness to the pandemic.

What Were Trending Topics among Schools and School Districts on Facebook in 2021?

To examine trending educational technology topics on Facebook, we studied the posts by 14,481 schools and school districts on their public pages. First, one aspect of this analysis concerned the number of posts shared. In our last report, we documented how schools and districts posted more posts than in any other month during March, April, and May 2020—during the earliest and perhaps most tumultuous months of the COVID-19 pandemic, suggesting the importance of communication during this crisis period, as others have documented with Twitter data (Michela et al., 2022 ). Notably, in 2021, those months remained the most active; apart from those months, the numbers of posts by schools and districts in 2021 were roughly comparable to the numbers in 2019 and 2020 (see Fig.  1 ).

The Number of Posts on Facebook by Schools and School Districts

To understand which technologies were shared on these Facebook pages, we examined the domain names for all of the hyperlinks that were posted. Despite the myriad social and other changes experienced by schools from 2019 to 2021, link domains shared on Facebook exhibited remarkable consistency: Youtube, Google Docs, Google, and Google Drive—Google or tools created by Google—were the four most frequently shared for each of these years (Table  4 ). Note that the n represents the number of schools or districts sharing one or more links to these domains (of the 14,481 total school and school district pages). Thus, the 8278 indicates that 57.2% of schools and districts posted one or more links to YouTube over the 2021 year. These were followed by Zoom, which was also widely shared in 2020 (though not in 2019), and then Google Sites (which was shared frequently in 2020). The CDC and 2020 Census’s websites dropped from the list of the top ten most frequently shared domains in 2021, despite having been widely shared in 2020. Otherwise, the results are largely comparable between 2019, 2020, and 2021, indicating that schools and districts continued to use a core set of productivity tools despite the many disruptions and changes over this period.

We also examined the contents of the messages of schools’ and school districts’ posts. To do so, we considered the technologies identified by Weller ( 2020 ) in his history of the past 25 years of educational technology, as in our report for last year. Specifically, we searched the contents of the messages posted by schools and districts for the inclusion of the terms that correspond to technologies Weller identified as being representative of a particular year. While the domains shared by schools and districts demonstrated remarkable consistency, the contents of the messages posted by schools and districts varied substantially, especially when considering the changes from 2019 to 2020 and from 2020 to 2021. To illustrate, consider mentions of “e-learning,” which Weller identified as the focal point of 1999. In 2019, 834 messages that mentioned “e-learning” were posted by schools and districts, but in 2020, the number increased around ten-fold to 8326 mentions. Though it may have been expected for mentions of “e-learning” to remain somewhat constant during 2021, instead we saw a marked downturn to 1899 (or a 78% drop). This trend—a sizable increase in how often certain technologies were mentioned in 2020 relative to 2019 that was not sustained in 2021—was also found for mentions of “learning management systems,” “video,” and “Second Life and virtual worlds,” among others. Indeed, the only noteworthy increase in mentions of these technologies from 2020 to 2021 was for “artificial intelligence”.

Summary and Discussion

By triangulating the 2021 snapshots of each of these three data sources—Scopus, Twitter, and Facebook—we can begin to see a state of the educational technology field pressing into the future. Results on specific terms or topics may be useful for individual researchers and practitioners to see the representation of their areas of interest. Still, some common takeaways that emerge from all three sources include the following.

First, we found an emphasis on “e-learning”—particularly in Twitter and Facebook posts—as well as “blended learning” (Twitter) and “online learning” (journal articles). Notably, COVID-19 (and related terms) were also frequently mentioned. These findings align with how mentions of “e-learning” spiked during the 2020 year when the effects of the COVID-19 pandemic on education were especially disruptive, but their ongoing presence also suggests that interest in these topics will likely extend outside and beyond the context of the pandemic.

Second, we note a keen interest in emergent technologies like artificial intelligence and virtual reality, particularly on the part of researchers (as evidenced by how frequently these terms were mentioned in journal articles published in 2021). At the same time, we note that this interest has not yet crystallized into the sustained adoption and use of these emergent technologies—a point bolstered by the relatively limited mention of these technologies in the Facebook posts of schools and school districts. Thus, we think we as a field must wait and see whether interest in these technologies is lasting or transient.

Last, we found an ever-increasing reliance on several corporate entities for productivity and sharing. This was especially the case for Google and tools created by Google: YouTube, Google Docs, and Google Drive, in particular. Indeed, such tools are such an established part of our work (and educational) context that we might hardly think of them as tools. Furthermore, tools created by Google and several other corporations—including social media platforms themselves—were also prevalent in the content of the tweets we analyzed. While we do not believe it is a bad decision on the part of individuals or educational institutions to use these and other tools, there are also some potential downsides to their use that we think invite critical questions (Burchfield et al., 2021; Krutka et al., 2021 ).

As a result of these common takeaways, we will now conclude with three questions for educational technology researchers and practitioners to consider.

Pandemic Bump Vs. Ubiquity

First, many have wondered whether changes in educational technology catalyzed by the pandemic will yield sustained, ubiquitous changes to the field, or if adjustments represent only a short-term bump of interest—as may be the case with emergency remote teaching tools and strategies used in the early days of the pandemic (Hodges et al., 2020 ). One of the takeaways from our Facebook analysis was that while some productivity technologies appeared to have remained consistently used on the basis of our domain analysis (e.g., Google Docs), mentions of many specific technologies in the messages of the posts by schools and districts appeared to have been more transitory in nature, such as in the cases of “e-learning” and “learning management systems.” This suggests at least two possible interpretations. One is that these technologies were used in transient response to an unprecedented period of emergency remote instruction—though tools associated with remote teaching and learning continue to be used, their use was primarily a temporary, emergency measure. Another is that these tools were mentioned less because they have become a more ubiquitous but less visible tool used by teachers and learners. Learning management systems may still, of course, be widely used, but schools and districts may be sharing about their role less through their public social media platforms because they may already be familiar to students and their parents. While we cannot say why there was a dramatic increase followed by a decrease in the use of many educational technologies over the period from 2019 through 2021, our analysis indicates that many tools are, at least, being communicated about much less over the past year than in the preceding year when the pandemic began in the U.S.

Technocentrism Vs. Focusing on Learners and Improving Educational Systems

Second, though emerging technologies are obviously an essential component of our field, one of the perennial challenges we must grapple with is our relationship to these technologies. Are we technocentric, as Papert ( 1987 , 1990 ) warned, or do we focus on learning and improvement? In our results, we notice that technologies such as artificial intelligence, virtual reality, and augmented reality were very frequently referenced in comparison to most other modalities or topics of research. As processing and graphical rendering capabilities continue to become more compact and inexpensive via headsets, smartphones, and haptic devices, we would expect these technologies to continue to receive ongoing attention. Though there are certainly valuable learning improvement opportunities associated with such technologies (Glaser & Schmidt, 2021 ), we might also justifiably wonder whether the volume of attention that these technologies are currently receiving in the literature is concomitant to their actual (or even hypothetical) large-scale learning benefits—or whether current fascination with such technologies represents a repeat of other historical emphases that may not have panned out in the form of systemic educational improvement, such as in the case of MUVEs (cf., Nelson & Ketelhut, 2007 ).

Limited Broader Impacts on Larger Social Issues

Finally, to reiterate our critiques from previous years (Kimmons, 2020 ; Kimmons et al., 2021 ), we continued to see a dearth of references to important social issues in scholarly article titles, including references to social matters upon which educational technology should be expected to have a strong voice. For instance, terms relating to universal design ( n  = 0), accessibility ( n  = 4), privacy ( n  = 8), ethics ( n  = 12), security ( n  = 8), equity ( n  = 6), justice ( n  = 1), and (digital and participatory) divides ( n  = 1) were all very uncommon. Though “ethics” was the most common of these terms, it only was represented in 1-in-200 article titles, and though current “practices with student data represent cause for concern, as student behaviors are increasingly tracked, analyzed, and studied to draw conclusions about learning, attitudes, and future behaviors” (Kimmons, 2021 , para. 2; cf., Rosenberg et al., 2021 ) and proctoring software becomes increasingly ubiquitous (Kimmons & Veletsianos, 2021 ), “privacy” was only mentioned in 1-in-333 article titles and “proctor*” was only in 1-in-600 titles. In our current pandemic context, we have often heard educational technologists lament the fact that decision-makers and those in power may not seek our guidance in addressing issues related to the pandemic that would clearly benefit from our expertise. And yet, the absence of other socially-relevant topics from our research suggests that we may be challenged to leverage our work toward addressing matters of larger social or educational importance ourselves. A focus on the social matters and the social context around educational technology use, then, remains an opportunity for research and development by the educational technology community in the years ahead. This seems especially salient as our data suggests that the field is heavily influenced by big technology corporations like Google and Facebook that historically have been critiqued for violating ethical expectations of privacy and failing to support social good. As educational technology researchers and practitioners, we are primed with the position and expertise necessary to shape the future of ethical technology use in education. Hopefully, we can step up to this challenge.

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How Important Is Technology in Education? Benefits, Challenges, and Impact on Students

Many of today’s high-demand jobs were created in the last decade, according to the International Society for Technology in Education (ISTE). As advances in technology drive globalization and digital transformation, teachers can help students acquire the necessary skills to succeed in the careers of the future.

How important is technology in education? The COVID-19 pandemic is quickly demonstrating why online education should be a vital part of teaching and learning. By integrating technology into existing curricula, as opposed to using it solely as a crisis-management tool, teachers can harness online learning as a powerful educational tool.

The effective use of digital learning tools in classrooms can increase student engagement, help teachers improve their lesson plans, and facilitate personalized learning. It also helps students build essential 21st-century skills.

Virtual classrooms, video, augmented reality (AR), robots, and other technology tools can not only make class more lively, they can also create more inclusive learning environments that foster collaboration and inquisitiveness and enable teachers to collect data on student performance.

Still, it’s important to note that technology is a tool used in education and not an end in itself. The promise of educational technology lies in what educators do with it and how it is used to best support their students’ needs.

Educational Technology Challenges

BuiltIn reports that 92 percent of teachers understand the impact of technology in education. According to Project Tomorrow, 59 percent of middle school students say digital educational tools have helped them with their grades and test scores. These tools have become so popular that the educational technology market is projected to expand to $342 billion by 2025, according to the World Economic Forum.

However, educational technology has its challenges, particularly when it comes to implementation and use. For example, despite growing interest in the use of AR, artificial intelligence, and other emerging technology, less than 10 percent of schools report having these tools in their classrooms, according to Project Tomorrow. Additional concerns include excessive screen time, the effectiveness of teachers using the technology, and worries about technology equity.

Prominently rising from the COVID-19 crisis is the issue of content. Educators need to be able to develop and weigh in on online educational content, especially to encourage students to consider a topic from different perspectives. The urgent actions taken during this crisis did not provide sufficient time for this. Access is an added concern — for example, not every school district has resources to provide students with a laptop, and internet connectivity can be unreliable in homes.

Additionally, while some students thrive in online education settings, others lag for various factors, including support resources. For example, a student who already struggled in face-to-face environments may struggle even more in the current situation. These students may have relied on resources that they no longer have in their homes.

Still, most students typically demonstrate confidence in using online education when they have the resources, as studies have suggested. However, online education may pose challenges for teachers, especially in places where it has not been the norm.

Despite the challenges and concerns, it’s important to note the benefits of technology in education, including increased collaboration and communication, improved quality of education, and engaging lessons that help spark imagination and a search for knowledge in students.

The Benefits of Technology in Education

Teachers want to improve student performance, and technology can help them accomplish this aim. To mitigate the challenges, administrators should help teachers gain the competencies needed to enhance learning for students through technology. Additionally, technology in the classroom should make teachers’ jobs easier without adding extra time to their day.

Technology provides students with easy-to-access information, accelerated learning, and fun opportunities to practice what they learn. It enables students to explore new subjects and deepen their understanding of difficult concepts, particularly in STEM. Through the use of technology inside and outside the classroom, students can gain 21st-century technical skills necessary for future occupations.

Still, children learn more effectively with direction. The World Economic Forum reports that while technology can help young students learn and acquire knowledge through play, for example, evidence suggests that learning is more effective through guidance from an adult, such as a teacher.

Leaders and administrators should take stock of where their faculty are in terms of their understanding of online spaces. From lessons learned during this disruptive time, they can implement solutions now for the future. For example, administrators could give teachers a week or two to think carefully about how to teach courses not previously online. In addition to an exploration of solutions, flexibility during these trying times is of paramount importance.

Below are examples of how important technology is in education and the benefits it offers to students and teachers.

Increased Collaboration and Communication

Educational technology can foster collaboration. Not only can teachers engage with students during lessons, but students can also communicate with each other. Through online lessons and learning games, students get to work together to solve problems. In collaborative activities, students can share their thoughts and ideas and support each other. At the same time, technology enables one-on-one interaction with teachers. Students can ask classroom-related questions and seek additional help on difficult-to-understand subject matter. At home, students can upload their homework, and teachers can access and view completed assignments using their laptops.

Personalized Learning Opportunities

Technology allows 24/7 access to educational resources. Classes can take place entirely online via the use of a laptop or mobile device. Hybrid versions of learning combine the use of technology from anywhere with regular in-person classroom sessions. In both scenarios, the use of technology to tailor learning plans for each student is possible. Teachers can create lessons based on student interests and strengths. An added benefit is that students can learn at their own pace. When they need to review class material to get a better understanding of essential concepts, students can review videos in the lesson plan. The data generated through these online activities enable teachers to see which students struggled with certain subjects and offer additional assistance and support.

Curiosity Driven by Engaging Content

Through engaging and educational content, teachers can spark inquisitiveness in children and boost their curiosity, which research says has ties to academic success. Curiosity helps students get a better understanding of math and reading concepts. Creating engaging content can involve the use of AR, videos, or podcasts. For example, when submitting assignments, students can include videos or interact with students from across the globe.

Improved Teacher Productivity and Efficiency

Teachers can leverage technology to achieve new levels of productivity, implement useful digital tools to expand learning opportunities for students, and increase student support and engagement. It also enables teachers to improve their instruction methods and personalize learning. Schools can benefit from technology by reducing the costs of physical instructional materials, enhancing educational program efficiency, and making the best use of teacher time.

Become a Leader in Enriching Classrooms through Technology

Educators unfamiliar with some of the technology used in education may not have been exposed to the tools as they prepared for their careers or as part of their professional development. Teachers looking to make the transition and acquire the skills to incorporate technology in education can take advantage of learning opportunities to advance their competencies. For individuals looking to help transform the education system through technology, American University’s School of Education online offers a Master of Arts in Teaching and a Master of Arts in Education Policy and Leadership to prepare educators with essential tools to become leaders. Courses such as Education Program and Policy Implementation and Teaching Science in Elementary School equip graduate students with critical competencies to incorporate technology into educational settings effectively.

Learn more about American University’s School of Education online and its master’s degree programs.

Virtual Reality in Education: Benefits, Tools, and Resources

Data-Driven Decision Making in Education: 11 Tips for Teachers & Administration

Helping Girls Succeed in STEM

BuiltIn, “Edtech 101”

EdTech, “Teaching Teachers to Put Tech Tools to Work”

International Society for Technology in Education, “Preparing Students for Jobs That Don’t Exist”

The Journal, “How Teachers Use Technology to Enrich Learning Experiences”

Pediatric Research, “Early Childhood Curiosity and Kindergarten Reading and Math Academic Achievement”

Project Tomorrow, “Digital Learning: Peril or Promise for Our K-12 Students”

World Economic Forum, “The Future of Jobs Report 2018”

World Economic Forum, “Learning through Play: How Schools Can Educate Students through Technology”

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Listen to the essay, as read by Antero Garcia, associate professor in the Graduate School of Education.

As a professor of education and a former public school teacher, I’ve seen digital tools change lives in schools.

I’ve documented the ways mobile technology like phones can transform student engagement in my own classroom.

I’ve explored how digital tools might network powerful civic learning and dialogue for classrooms across the country – elements of education that are crucial for sustaining our democracy today.

And, like everyone, I’ve witnessed digital technologies make schooling safer in the midst of a global pandemic. Zoom and Google Classroom, for instance, allowed many students to attend classrooms virtually during a period when it was not feasible to meet in person.

So I want to tell you that I think technologies are changing education for the better and that we need to invest more in them – but I just can’t.

Given the substantial amount of scholarly time I’ve invested in documenting the life-changing possibilities of digital technologies, it gives me no pleasure to suggest that these tools might be slowly poisoning us. Despite their purported and transformational value, I’ve been wondering if our investment in educational technology might in fact be making our schools worse.

Let me explain.

When I was a classroom teacher, I loved relying on the latest tools to create impressive and immersive experiences for my students. We would utilize technology to create class films, produce social media profiles for the Janie Crawfords, the Holden Caulfields, and other literary characters we studied, and find playful ways to digitally share our understanding of the ideas we studied in our classrooms.

As a teacher, technology was a way to build on students’ interests in pop culture and the world around them. This was exciting to me.

But I’ve continued to understand that the aspects of technology I loved weren’t actually about technology at all – they were about creating authentic learning experiences with young people. At the heart of these digital explorations were my relationships with students and the trust we built together.

“Part of why I’ve grown so skeptical about this current digital revolution is because of how these tools reshape students’ bodies and their relation to the world around them.”

I do see promise in the suite of digital tools that are available in classrooms today. But my research focus on platforms – digital spaces like Amazon, Netflix, and Google that reshape how users interact in online environments – suggests that when we focus on the trees of individual tools, we ignore the larger forest of social and cognitive challenges.

Most people encounter platforms every day in their online social lives. From the few online retail stores where we buy groceries to the small handful of sites that stream our favorite shows and media content, platforms have narrowed how we use the internet today to a small collection of Silicon Valley behemoths. Our social media activities, too, are limited to one or two sites where we check on the updates, photos, and looped videos of friends and loved ones.

These platforms restrict our online and offline lives to a relatively small number of companies and spaces – we communicate with a finite set of tools and consume a set of media that is often algorithmically suggested. This centralization of internet – a trend decades in the making – makes me very uneasy.

From willfully hiding the negative effects of social media use for vulnerable populations to creating tools that reinforce racial bias, today’s platforms are causing harm and sowing disinformation for young people and adults alike. The deluge of difficult ethical and pedagogical questions around these tools are not being broached in any meaningful way in schools – even adults aren’t sure how to manage their online lives.

You might ask, “What does this have to do with education?” Platforms are also a large part of how modern schools operate. From classroom management software to attendance tracking to the online tools that allowed students to meet safely during the pandemic, platforms guide nearly every student interaction in schools today. But districts are utilizing these tools without considering the wider spectrum of changes that they have incurred alongside them.

Antero Garcia, associate professor of education (Image credit: Courtesy Antero Garcia)

For example, it might seem helpful for a school to use a management tool like Classroom Dojo (a digital platform that can offer parents ways to interact with and receive updates from their family’s teacher) or software that tracks student reading and development like Accelerated Reader for day-to-day needs. However, these tools limit what assessment looks like and penalize students based on flawed interpretations of learning.

Another problem with platforms is that they, by necessity, amass large swaths of data. Myriad forms of educational technology exist – from virtual reality headsets to e-readers to the small sensors on student ID cards that can track when students enter schools. And all of this student data is being funneled out of schools and into the virtual black boxes of company databases.

Part of why I’ve grown so skeptical about this current digital revolution is because of how these tools reshape students’ bodies and their relation to the world around them. Young people are not viewed as complete human beings but as boxes checked for attendance, for meeting academic progress metrics, or for confirming their location within a school building. Nearly every action that students perform in schools – whether it’s logging onto devices, accessing buildings, or sharing content through their private online lives – is noticed and recorded. Children in schools have become disembodied from their minds and their hearts. Thus, one of the greatest and implicit lessons that kids learn in schools today is that they must sacrifice their privacy in order to participate in conventional, civic society.

The pandemic has only made the situation worse. At its beginnings, some schools relied on software to track students’ eye movements, ostensibly ensuring that kids were paying attention to the tasks at hand. Similarly, many schools required students to keep their cameras on during class time for similar purposes. These might be seen as in the best interests of students and their academic growth, but such practices are part of a larger (and usually more invisible) process of normalizing surveillance in the lives of youth today.

I am not suggesting that we completely reject all of the tools at our disposal – but I am urging for more caution. Even the seemingly benign resources we might use in our classrooms today come with tradeoffs. Every Wi-Fi-connected, “smart” device utilized in schools is an investment in time, money, and expertise in technology over teachers and the teaching profession.

Our focus on fixing or saving schools via digital tools assumes that the benefits and convenience that these invisible platforms offer are worth it.

But my ongoing exploration of how platforms reduce students to quantifiable data suggests that we are removing the innovation and imagination of students and teachers in the process.

Antero Garcia is associate professor of education in the Graduate School of Education .

In Their Own Words is a collaboration between the Stanford Public Humanities Initiative  and Stanford University Communications.

If you’re a Stanford faculty member (in any discipline or school) who is interested in writing an essay for this series, please reach out to Natalie Jabbar at [email protected] .

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Some states are banning phones in schools to reduce classroom distraction and cyberbullying. Tell us about your experience with tech in schools.

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Natasha Singer has interviewed hundreds of educators, students, parents, researchers and executives as part of her coverage of technology in schools.

Digital devices and apps can be great tools in schools. They can also be a classroom distraction or even a weapon.

Some students spend so much class time on their smartphones, commenting on social media or texting their friends, that it hampers their learning. Some school children and teenagers have used their phones to bully or sexually exploit their classmates, or post videos of student fights on social media. And classroom devices like Chromebooks and iPads, which can be helpful, can also sometimes enable distractions and facilitate problems like bullying.

I write about technology in schools for The New York Times including innovations, like artificial intelligence-powered chatbots and classroom tutoring bots . This year, I’ve also been reporting on school tech problems — including an article about a group of middle school students who impersonated their teachers on TikTok , and a podcast on high school boys who used A.I. “nudification” apps to make fake nude images of their female classmates .

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How technology is reinventing education.

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of  Stanford Graduate School of Education  (GSE), who is also a professor of educational technology at the GSE and faculty director of the  Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately  worried  that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or  coach  students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the  AI + Education initiative  at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of  CRAFT  (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the  Digital Learning initiative  at the Stanford Accelerator for Learning, which runs a program exploring the use of  virtual field trips  to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Technology Integration in Higher Education During COVID-19: An Assessment of Online Teaching Competencies Through Technological Pedagogical Content Knowledge Model

1 Department of Education, Northeast Normal University, Changchun, China

Yang Yingxiu

Ahmad samed al-adwan.

2 Department of Electronic Business and Commerce, Al-Ahliyya Amman University, Amman, Jordan

Ali Alkhalifah

3 Department of Information Technology, Qassim University, Buraidah, Saudi Arabia

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The COVID-19 pandemic significantly shifted education from traditional to an online version, which was an emergent state for teachers and students. The substantive situation thus raises the importance of technology integration in education, and teachers are required to update their competencies, respectively. In this regard, the study assessed online teaching competencies of faculty members following, technological pedagogical content knowledge (TPACK) model. Closed-ended surveys were employed for quantitative analysis of randomly selected 256 faculty members from public universities in Karachi, Pakistan. Results indicated that teachers possessed adequate levels of knowledge across all the domains of TPACK. The highest competency was obtained by content knowledge (CK), while technological knowledge (TK) was reported at the lowest level. Furthermore, a significant difference was noted in terms of gender and teaching experience. Correspondingly, the study proposes that the TPACK model should be employed in the professional development programs to develop teachers’ TPACK for integrating information communication and technology in the pedagogical practices. The findings of the study present a constructive overview of teachers’ digital competencies and technology use in teaching and learning in the time of the COVID-19 and also play a significant role in the integration of technology in the post-pandemic time in higher education. The study also suggests relevant educational authorities and policymakers for assessing and enhancing the technological competencies of teachers for quality online education.

Introduction

As it can be seen worldwide, the COVID-19 pandemic has caused a significant interruption in all the domains of human lives. Likewise, the educational sector also encountered many challenges by the institutional closure from schools to universities, and traditional education shifted to the online paradigm ( UN, 2020 ). The scenario of this technological transition affected the education of about half of the student population globally ( UNESCO, 2020 ). Thereby, the situation raises the importance of technology integration in education, and teachers are required to update their competencies to endow quality education and make changes to their curriculum and instruction accordingly. Regarding the application of information communication and technology (ICT) in education, however, instructors and learners are familiar with the traditional technological teaching aids, such as Smartboards and PowerPoint; still, their practical employability is required in the teaching practices ( Nikolopoulou and Gialamas, 2016 ; Guillén-Gámez et al., 2018 ). Besides, this provisional period raised the necessity, especially for the teachers, to gain competency in applying ICT in their teaching practices. Meanwhile, the application of ICT in higher education has remained a major subject of concern for decades at the global level (e.g., US Department of Education, 2017 ; Daniela et al., 2018 ). Many studies have highlighted that the application of ICT in the classroom setting has become a critical factor for meeting the needs of the learner in the knowledge society ( Martins et al., 2019 ). Besides, the successful integration of ICT can make the learning process more exciting and keep learners motivated ( Hanafi et al., 2017 ), which are considered as the significant predictors of their academic performance ( Xu et al., 2021 ). In the same manner, the utilization of ICT is suggested by the government of Pakistan to optimize the educational outcomes, as it helps to enhance the pedagogical competencies of teachers and boost learners to learn actively [ Pakistan Ministry of Education (MoE), 2018 ].

Moreover, the effective integration of ICT is essential in systematizing an efficient online educational program. The successful application of ICT not only contributes to learners’ satisfaction but also helps individuals to acquire their desired outcomes ( Cervero et al., 2020 ). It is, therefore, essential to develop competencies in teachers to use ICT effectively in their pedagogical practices by organizing professional development programs ( Guillén-Gámez et al., 2020 ). However, teachers’ professional training for the efficient use of ICT in teaching did not apply because of the sudden pandemic situation and put students at risk ( Guillén-Gámez et al., 2020 ; Hong et al., 2021 ). Consequently, it caused excessive pressure on teachers to achieve students’ required educational attainment ( Rodriguez-Segura et al., 2020 ). Although teachers made every effort to continue students’ learning, yet they had to encounter several challenges in adopting digital platforms for teaching, which include insufficient inter-institutional coordination ( Talsma et al., 2021 ), little understanding, and investment in advanced technologies ( Akram et al., 2021 ). In the past decade, however, in Pakistan, online learning has also been handled significantly, still been endured with the various constraints that prevent the effective integration of ICT in educational practices ( Kanwal and Rehman, 2017 ; Salam et al., 2017 ). Earlier studies indicate that students generally show better academic performance in digital platforms comparing with the traditional ones ( Shehzadi et al., 2020 ). On the other hand, the digital competencies of teachers are found inadequate, particularly in the formulation of lesson plans ( Farid et al., 2015 ). However, most of the teachers are digitally literate and can conduct online lessons, yet they are found incapable of integrating ICT efficiently in their teaching practices ( Al-Samarraie and Saeed, 2018 ). Consequently, their digital instructional approaches may remain unsuccessful in delivering the content effectively ( Adnan and Anwar, 2020 ). In this regard, the situation raises the importance of teachers’ professional learning to acquire technological competency, as a successful pedagogical practice would mainly be possible if teachers possess a sound technological competency. The relationship between technological competency with educational content was considered necessary by Mishra and Koehler (2006) and presented this in their framework, namely, technological pedagogical content knowledge (TPACK). Their primary focus was derived on the basis of the premise that teachers are required to acquire technological competency to use it effectively in the instructional approaches. Regarding evaluation, several studies have presented instruments to evaluate the technological competencies of teachers differently, but their main focus remained on teachers’ knowledge, beliefs, and adaptation ( Ertmer, 2005 ; Aldunate and Nussbaum, 2013 ; Kim et al., 2017 ). The complementary fact in various studies was that they comprised only one of the components of the concept.

In contrast, technological competency involves all the major components, such as knowledge (technological, pedagogical, and content), skills, and attitudes ( Voogt et al., 2015 ), whereas limited literature and studies have been found regarding all the major components. In addition, the acquisition of TPACK depends on social, cultural, and contextual attributes, which may vary from one country to another. However, several studies have been investigated teachers’ digital competencies through all the determinants of TPACK in various countries (i.e., Lin et al., 2013 ; Scherer et al., 2018 ; Ortega-Sánchez and Gómez-Trigueros, 2019 ; Acikgul and Aslaner, 2020 ; Castéra et al., 2020 ). But, to the best of our knowledge, this is the first study that aims to examine teachers’ digital competencies via all the mentioned sub-components of TPACK during the pandemic phase, specifically in the context of higher education in Pakistan. In this regard, the present study examines the integration of ICT in faculty members’ pedagogical practices by unfolding their technological competencies level. Subsequently, lecturers and professors from public universities of Karachi city of Pakistan were considered for a case study under the guidance of the following research questions:

• What are the levels of TPACK of faculty members across higher institutions of Karachi?
• Is there any significant difference between faculty members’ TPACK regarding their gender and teaching experience?

Review of Literature

Online teaching competencies.

The term online teaching can be exemplified with the help of these principles: (1) The learner and teacher interconnected with each other distantly via different digital platforms, (2) learning and learning materials can be accessed through technology, (3) the interaction between teacher and learner takes place via technology, and (4) teacher assists learner with the help of different digital channels of communication ( Anderson, 2011a ). In a general manner, online teaching is viewed as similar to the teaching for all other formal learning/teaching environments ( Anderson, 2011b ). On the other hand, teaching competency signifies the skills, attitudes, and knowledge of the teachers that enable them to perform in a way that meets or exceeds the expected standards successfully ( Richey et al., 2011 ). Without having adequate competencies, it is difficult for teachers to execute and organize online instructional programs efficiently as teaching is characterized by selecting a number of tactics for a specified discourse, which may involve lesson planning or instructional and learning materials. In this regard, the previous literature finds several categories and characteristics of online teaching competencies. For instance, Thomas and Graham (2017) emphasize course design as the core component of teachers’ competencies. Bigatel et al. (2012) focused on teaching behaviors and did not focus on instructional design. Contrarily, few researchers provide a brief description of teachers’ online competencies by means of personal, social, pedagogical, and technological characteristics ( Guasch et al., 2010 ; Baran et al., 2011 ; Palloff and Pratt, 2013 ). Other researchers propose a framework to demonstrate the features of teaching competencies. Among those, the widely used and renowned model is considered as the TPACK model, developed by Mishra and Koehler (2006) . The present study employs the TPACK model to investigate online teaching competencies.

Technology Integration in Pedagogical Practices

Several studies draw attention to the importance of technology integration in pedagogical practices and imply that it does not facilitate only students but also the teacher in the learning process ( Salam et al., 2019 ). Islam et al. (2019) indicate that the utilization of technology in teaching makes teacher competent in pedagogical as well as content areas in the classrooms and helps learners to learn efficiently by the use of technological tools. Several studies highlight the advantages of technology use for teachers. For instance, the study of Vongkulluksn et al. (2018) highlights that the teachers prefer to spend more time teaching in the classrooms, who are good at utilizing technology. Furthermore, the technological competencies of teachers enable them to adapt other teaching strategies and approaches easily; as a result, their performance gets enhanced.

Oliva-Córdova et al. (2021) ascertain that the usage of technology in teaching practices enables learners to learn effortlessly; however, its efficient application generally depends upon teachers’ technological and pedagogical competencies. Various studies have identified the importance of these competencies and knowledge of teachers in teaching practices. Ifinedo et al. (2020) indicate that teachers’ technological knowledge either explicitly or implicitly contributes significantly to integrating ICT successfully, while teachers’ ICT pedagogical practices have found the lowest technology integration predictor. The results further suggested including professional training to assist teachers in integrating ICT efficiently by enhancing their technological competencies. To investigate the impact of teachers’ training programs on their online teaching effectiveness, Brinkley-Etzkorn (2018) conducted a survey. The findings revealed a significant change in teaching competencies and designing course syllabi in teachers, while no significant difference in teaching was observed according to their student perceptions.

Moreover, the knowledge of technology and expertise in the utilization of technology are considered two different modes of competencies ( Instefjord and Munthe, 2017 ). For instance, it is identified by some studies that despite having technology literacy, teachers were not capable of using technology in teaching efficiently ( Dinçer, 2018 ; Alanazy and Alrusaiyes, 2021 ). It indicates that technological knowledge and using technology in pedagogical practices are two different concepts. Several studies and theories have been proposed to highlight this aspect. Briefly, it can be summarized that the effective use of technology in teaching practices is possible only if teachers are equipped with all the fundamental competencies ( Ifinedo et al., 2020 ). Likewise, the TPACK model also ascertains that ICT cannot be integrated efficiently in educational practices until teachers do not possess all the essential technological skills ( Mishra and Koehler, 2006 ). This model is comprised of three main components of teachers’ knowledge or acquaintance (shown in Figure 1 ), i.e., technological, pedagogical, and content. Although all three components of the model seem different and separate knowledge domains, interfaces and associations among these core concepts establish the central point of the overall framework ( Archambault and Barnett, 2010 ). After following the convergence of the mentioned components, knowledge of teachers can be classified as technological content knowledge (TCK), pedagogical content knowledge (PCK), and technological pedagogical knowledge (TPK), while the complete form of all components of knowledge is represented as TPACK ( Schmidt et al., 2009 ).

The technological pedagogical content knowledge (TPACK) framework (retrieved from http://tpack.org ).

Teachers’ TPACK Concerning Their Gender and Teaching Experience

It is indicated by several empirical studies that teachers’ characteristics also play a significant role in integrating ICT, which may vary across the countries; for instance, some studies have identified a significant difference in gender with a more inclination of males toward digital instructional development than females ( Marín-Díaz et al., 2020 ). In terms of TPACK, studies also indicate the gender difference; for instance, Lin et al. (2013) identified higher pedagogical knowledge in female teachers with lower technological knowledge. Scherer et al. (2017) revealed that in all technological domains of TPACK, male teachers reported significantly higher competencies than females. In contrast, the TCK of female teachers was reported higher than the male teachers ( Ortega-Sánchez and Gómez-Trigueros, 2019 ). However, a study by Castéra et al. (2020) came across different results and found no significant difference between genders in terms of teachers’ TPACK.

Another element of central concern in the acquisition of digital competencies is the teaching experience of teachers. Regarding years of teaching experience, studies show mixed results. For instance, Koh et al. (2014) identified a significant difference in ICT integration concerning the teaching experience and determined that TPACK of novice teachers was higher than experienced teachers. In contrast, Jang and Tsai (2012) identified that senior teachers’ technological skills were higher than novice teachers. Therefore, the hypotheses of the study can be posited as:

H1: “There is a significant difference between faculty members’ TPACK with respect to their gender.”

H2: “There is a significant difference between faculty members’ TPACK with respect to their teaching experience.”

Methodology

For examining faculty members’ TPACK, a quantitative survey design was employed as it was considered the most appropriate approach to gain accurate reflection via numerical representation ( Watson, 2015 ). Subsequently, the study was guided by questionnaires, which were mailed and also emailed by the researcher to various universities.

Participants of the Study

The population of the study comprises all the faculty members from public universities of Karachi, which consists of 11 universities with estimated 785 faculty members [ Higher Education commission (HEC), 2015 ]. For ensuring a stable data analysis, the sample size was calculated by applying the Yamane Taro sample formula for a finite population ( Israel, 1992 ) and obtained a sample size of 260 respondents. The sample size for conducting this study was appropriate, as the size of the sample between 30 and 500 at a 5% confidence level is identified as adequate ( Altunışık et al., 2004 ). Subsequently, the questionnaires were distributed to lecturers/professors who were selected randomly from different public universities in Karachi. After excluding questionnaires with incomplete information, 256 questionnaires were considered for the data analysis. The ages of the respondents ranged from 29 to 54years, encompassing 44.1% ( n =113) were females and 55.8% ( n =143) were male faculty members. Their further details are presented in Table 1 .

Demographic statistics of the respondents.

Ethical Concerns

In order to ensure the reliability of the findings, the study followed all the ethical concerns to conduct the study. These concerns include the granted approval from the supervisor on account of the ethical committee. The other ethical concerns include assurance of the privacy and honor of the participants of the study, and questionnaires were filled after taking their consent.

Survey Instrument

The instrument utilized in this study was adopted from the validated scale formulated by Schmidt et al. (2009) , which was devised on the basis of the TPACK theoretical framework to examine teachers’ competencies within three basic domains, i.e., pedagogies, technology, and content. The said questionnaire was intensively used by other researchers (e.g., Scherer et al., 2018 ; Ortega-Sánchez and Gómez-Trigueros, 2019 ). Before conducting data, the questionnaire was modified according to the study’s approach; for instance, the questions from the domain (content knowledge) were rephrased from a specific subject to a general subject. Furthermore, the last three qualitative questions were also excluded from the survey. The modified form of the questionnaire comprised seven dimensions of 38 items, including (1) technological knowledge (TK) 7 items, (2) content knowledge (CK) 3 items, (3) pedagogical knowledge (PK) 7 items, (4) PCK 4 items, (5) TCK 4 items, (6) TPK 5 items, and (7) TPACK 8 items. The responses of each group’s items were laid down upon a five-point Likert scale extending from “Strongly Disagree” to “Strongly Agree.”

Confirmation of the Model Fitness

In order to increase the reliability of the findings, it is imperative to align empirical data with the theoretical framework of the study. Thereby, the fitness of all the dimensions of the TPACK model was investigated through confirmatory factor analysis as shown in Table 2 . The chi-square value was less than 5 (i.e., χ 2/ df =4.1), which indicates the significant fitness of the model ( Schermelleh-Engel et al., 2003 ). The other indicators were also reported significant (shown in Table 2 ), as their values were less than the threshold values, i.e., RMSEA≤0.06, CFI≥0.95, TLI≥0.95 ( Hu and Bentler, 1999 ); SRMR<0.05 ( Cangur and Ercan, 2015 ).

Confirmation of the model fitness.

Reliability of the Instrument

The reliability of all the constructs of TPACK was investigated through Cronbach’s alpha scale. The value of each construct was above 70% (shown in Table 3 ), which shows satisfactory consistency, as the collected data are reviewed as reliable if the alpha value is more than 60% ( Tavakol and Dennick, 2011 ).

Reliability evaluation.

Data Analysis

All the collected data were analyzed by employing various descriptive and inferential statistical tests, i.e., descriptive test (mean and standard deviation) and inferential test ( T -test and ANOVA). Subsequently, the analysis was completed by applying the receiver operating characteristic (ROC) curve, which enabled the examination of the differences between subsamples with respect to their TPACK scores. The ROC curve is a two-dimensional graphical representation of the values of sensitivity vs. 1-specificity ranges from 0 to 1, which helps in determining the difference between different subgroups ( Bradley, 1997 ).

Research Question 1

Technological pedagogical content knowledge of faculty members was investigated by means of descriptive statistical tests, i.e., mean and standard deviation, which are shown in Table 4 . Knowledge of all the domains of TPACK was rated above 3, which demonstrates that faculty members possess adequate knowledge as M ≥3 ( Rabe-Hesketh and Everitt, 2003 ). Among all domains of TPACK, the highest mean value was obtained by the content knowledge (CK), i.e., 4.6, while technological knowledge (TK) obtained the least mean value.

Descriptive analysis of teachers’ TPACK.

Research Question 2 (Hypotheses)

Before checking hypotheses, the normality test was conducted through Shapiro–Wilk test to know whether the data meet the criteria of conducting a parametric test since it is considered the most prevailing test to investigate normality ( Razali and Wah, 2011 ). Results showed that the data were normally distributed as S-W value was 0.83 and the significant value was greater than 0.5, i.e., 0.61, which allows parametric tests to be conducted. Subsequently, the posited hypotheses of the study were checked by employing inferential statistics, i.e., T -test and ANOVA, where T -test was employed to investigate the difference between faculty members’ TPACK with respect to their gender and ANOVA was applied to test the hypothesis regarding teaching experience of faculty members.

Hypothesis 1

All the components of TPACK were compared by applying the T -test (shown in Table 5 ). Results revealed a significant statistical difference between male and female respondents (i.e., T =10.34; p =0.000) at alpha level 0.05. Therefore, the hypothesis regarding faculty members’ TPACK with respect to their gender was accepted. Furthermore, male faculty members got a significantly higher mean score (4.12) than the female teaching faculty (3.67), which shows that the TPACK of male faculty members was greater than the female ones.

T -test analysis by gender of teachers.

In addition, the gender difference with respect to TPACK scores was represented graphically through the ROC curve. The results shown in ( Table 6 ; Figure 2 ) showed high sensitivity and specificity with an area under curve (AUC) of 0.921 with a significant statistical difference, i.e., p =0.000 at alpha level 0.05.

ROC curve parameters (female gender).

Receiver operating characteristic (ROC) curve (gender).

Furthermore, to investigate the most optimal predictors of teachers’ TPACK, the mean of all the sub-components was compared with respect to their gender (shown in Figure 3 ). Results reveal that the TK of male faculty members was significantly greater than the female ones. However, the CK was found significantly higher in female faculty members than the male ones.

Distribution of components of TPACK by gender.

Hypothesis 2

For examining the distinction between faculty members’ TPACK with regard to their teaching experience, the mean of TPACK was compared with the teaching experience of all the faculty members by applying the ANOVA test (shown in Table 7 ). Results reveal a significant difference between faculty members’ teaching experiences with their TPACK. Therefore, the hypothesis regarding the teaching experiences of faculty members was accepted, which further demonstrates that the TPACK of teachers with experience of 2–5years is higher than the novice and inexperienced teachers.

ANOVA by teaching experience of teachers.

In order to find out the further differences across all possible pairs of the faculty members’ teaching experiences, Tukey’s honestly significant difference post-hoc test was conducted. Since Tukey’s HSD test helps to compare the means of all the possible pairs ( Abdi and Williams, 2010 ). Results from Tukey’s post-hoc test ( Table 8 ) demonstrate that only one out of three groups had a significant difference among each other, i.e., teaching experiences up to 1year vs. 2–5years.

Post-hoc test.

In addition, the difference in teaching experience with respect to TPACK scores was represented graphically through the ROC curve. The results shown in ( Table 9 ; Figure 4 ) illustrated high sensitivity and specificity with an AUC of 0.716 with a significant statistical difference, i.e., p =0.000 at alpha level 0.05.

ROC curve parameters (teaching experience).

ROC curve (teaching experience).

COVID-19 outbreak has transformed the traditional educational practices and brought teaching-learning around digital format across the world. This transformation not merely raises the importance of the educational technology infrastructure of the country but also establishes a prerequisite for teachers to integrate technology in their pedagogical practices effectively to sustain students’ learning. Since the systematic implementation of ICT in teaching practices is remained at an early stage and a limited focus has been given at the higher education level. In this regard, the current study gives a deep insight to understand the levels of core competencies of faculty members’ TPACK with the role of personal variables (i.e., gender and teaching experience) in the acquisition of digital competencies during the COVID-19 period.

In view of the obtained findings, the study reveals that faculty members possess adequate knowledge in all the sub-components of the TPACK model, which shows that teachers have sufficient knowledge and skills regarding technology use in their pedagogical practices. This finding shows consistency with the findings of Mouza et al. (2014) , where participants experienced a significant gain in all sub-components of TPACK. Hence, our results indicate that TPACK is an excellent framework to examine teachers’ competencies in the context of universities’ teachers of Pakistan.

The results further indicate that the highest competence of faculty members among all other domains was obtained by the content knowledge (CK), which shows that faculty members seem more confident in their content knowledge than other domains of expertise. A similar finding is also identified by Acikgul and Aslaner (2020) ; accordingly, teachers’ content knowledge was found adequate. In contrast, the study conducted by illustrated that teachers were confident primarily in the pedagogical knowledge (PK). It is therefore imperative to draw attention toward teachers’ content knowledge through continuous monitoring and by offering in-service workshops to sustain the students’ learning outcomes, as it helps learners to understand concerning concepts significantly.

Technological knowledge involves operating particular technologies, which plays a crucial role in integrating technology in the process of teaching and learning ( Chai et al., 2010 ). Besides, successful e-learning can only be yielded when teachers can use technology in their pedagogical practices appropriately. On the other hand, the technological knowledge (TK) of faculty members was found at the lowest level among all other domains, which indicates that teachers lack technological competence. Thus, they require professional guidance to update their technological skills. The findings of Schmid et al. (2021) also indicated that teachers’ technological and TCK emerged as the least acquired competencies. Therefore, to enhance the technological literacy in teachers, ICT training centers with ICT professionals should be originated at the national and provincial levels.

The knowledge regarding the interaction between all domains of TPACK plays a significant role in the development of an innovative learning environment ( Ortega-Sánchez and Gómez-Trigueros, 2019 ). However, the other reported lowest competence of faculty members was found in the domain of TPACK. This finding reflects the finding of Lye (2013) , who indicated that teachers possess low TPACK, and they need improvement in several areas of TPACK. In light of this result, teachers should be given a range of guidance in terms of all the domains of TPACK and the interaction between those domains by providing both initial and ongoing support to implement the technologies in their pedagogical practices successfully.

This study further found that male teachers’ TPACK was significantly higher than female faculty members. This finding reflects the finding of Koh et al. (2010) , where male teachers showed more positive attitudes, competencies, and knowledge with respect to technology use. This result indicates that female faculty requires more support to gain their competencies in all the sub-components of TPACK. Regarding teaching experience, it is usually expected that teachers’ knowledge increases with the increase in years of experience. In contrast, the results showed a statistical significance in the TPACK of faculty members’ knowledge, where faculty members with experience of 2–5years shown higher TPACK than the teachers with more experience and novice teachers. This finding supports the results of Claro et al. (2018) , where years of teaching experience were found significantly associated with the TPACK of teachers. Based on the personal factors, policymakers and teachers should be aware of gender differences’ effect on technological knowledge and competencies; therefore, gender differences should be monitored closely by conducting longitudinal TPACK studies. There should be an equal emphasis on training programs on pre-service as well as in-service teachers so that teachers of all levels may learn effectively to integrate technology into their educational practices.

In addition, the study suggests that the new technological instructional context in the COVID-19 phase appeared as an important moderator for teachers in upgrading their competencies in terms of TPACK. Regarding the contextual environment, Mishra (2019) indicates that the addition of contextual knowledge (XK) may reveal the situational and institutional limitations that teachers work within. During the COVID-19 pandemic, teachers and learners experienced their practices in multiple new and unfamiliar contexts, which impacted teachers’ abilities to teach successfully remotely in the digital environment. Therefore, future studies should examine teachers’ XK comprehensively to determine the influence of different contextual factors on teachers’ TPACK with a focus on facilitating teachers with contextual change.

Finally, remote work and online learning are teaching conditions that will continue to advance steadily. In turn, the post-COVID-19 reactivation and recovery processes do not seem to contemplate that the teaching and learning processes as before. Therefore, future research should be aimed not only at understanding human behavior while studying or teaching virtually but also at understanding the TPACK model and building better ways to integrate technology into educational practices. In this regard, the findings of the study are highly significant, not particularly in determining the technology integration during the COVID-19 pandemic phase, which is currently the most crucial issue, but also for the integration of technology in the post-pandemic time in higher education.

Based on the obtained results, the study affirms that the COVID-19 pandemic phase significantly influenced the digital competencies of faculty members and reveals adequate knowledge in all the sub-components of the TPACK, with a significant difference in terms of gender and teaching experience. Regarding consistency, the TPACK model was verified by means of factorial analyses in terms of seven sub-components and in the context of higher education faculty members in Pakistan, which supports the value and appropriateness of the model. Accordingly, the study suggests that the TPACK model should be employed in the professional development programs to develop teachers’ TPACK for integrating technology efficiently by bridging the gap between ICT knowledge and ICT practice.

Implications and Limitations

The findings of this study contribute to society in several ways. Regarding theoretically, this study has enriched the literature on the technological competencies of teachers during the COVID-19 transitory period and verified the reliability of the TPACK model in the context of Karachi, Pakistan. It can be further used for verification in other cities and countries. In terms of methodological contribution, the study provides tentative insight in evaluating the impact of the COVID-19 transitory period on teachers’ digital competencies and their state of implementation in pedagogical practices. Regarding academics, this study provides a pragmatic direction to relevant educational authorities and policymakers for the improvement of online education by providing pertinent solutions and recommendations as per the situation. In addition, the future planning of professional development and training programs for the teachers can be based on the feedback provided by the faculty members. The study can further contribute to elevating e-learning outcomes and satisfaction during as well as post-pandemic phase.

Furthermore, the study also noted some limitations. Firstly, faculty’s response biasness may have affected the results since digital competencies were assessed self-reported quantitatively. Therefore, the future studies may select other approaches to unfold the understanding of teachers and establish the criteria for evaluating the TPACK of teachers. Secondly, the current study only focused on the TPACK model to assess the digital competencies of faculty. The findings of this study can be further strengthened in the future by employing other indicators to examine the teachers’ competencies in teaching with technology.

Finally, the analysis was cross-sectional and evaluated the teaching practices of university teachers during the period of the COVID-19 pandemic. Online technological, pedagogical, and content competencies of teachers may change over time, which should also be observed. Therefore, a longitudinal study should be conducted to strengthen the evidence by understanding the causal effects and interrelationships among various other variables, critical in elevating the online pedagogical practices at the higher level in Pakistan.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

HA is the principal investigator of the study. From conceptualization to the data analysis, she conducted by herself. All authors contributed to the article and approved the submitted version.

Conflict of Interest

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

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Apple spotlights how its technology is helping 4-H youth development

Mark Light has introduced thousands of young people to Apple technology through bus events in rural, suburban, and urban communities across Ohio. (Source: Apple)

The Community Education Initiative (CEI) started in 2019 in a partnership with Tennessee State University. It began with the aim of exposing students at historically black colleges and universities, to opportunities in app development.

Since then, Apple's CEI has expanded, with the company saying in 2022, that it was then working with over 600 communities around the world. Now in a new profile of its CEI ambitions, Apple says that it is presently working with tens of thousands of students, across all 50 states, plus 99 other countries and regions.

For one example, Apple says a 4-H — "Head, Heart, Hands, and Health" — mobile classroom is housing kids learning to code in Swift . They do it to then drive Sphero robots, controlled via iPads .

"It was really cool, and I especially liked working with the robots and drawing on iPad," says 12-year-old Jobie Thinthapthai, who is learning about medical technology, too. ""Medicine is constantly advancing with technology, so learning about that can help with my future."

"And it's the same with 4-H," continued Thinthapthai, "technology is giving us more tools to use with our projects, so we're learning skills that we can use later on in life."

That mobile classroom is run by a team of 4-H educators, including Mark Light. Previously a civil engineer, he now leads STEM programming in Ohio for 4-H.

"Technology is a big part of 4-H, and when kids pick up an iPad or Apple Pencil on the bus, it becomes the spark that gets them excited about learning new skills," he says. :I love when we have parents saying 'It's time to get off the bus and go on fair rides,' and the kids don't want to leave because they're so engaged."

Apple's CEI is also supporting STEM work a partnership with Rutgers University-Newark and 4-H clubs through the 4-H Computer Science Pathways project.

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• Education News

Top 5 engineering colleges of Delhi, according to NIRF Ranking

Indian Institute of Technology (IIT) Delhi

• Computer Science and Engineering
• Mechanical Engineering
• Electrical Engineering
• Mathematics and Computing Engineering
• Computer Science and Engineering: 118 (2023), 116 (2024)
• Mathematics and Computing Engineering: 352 (2023), 332 (2024)
• Electrical Engineering: 582 (2023), 625 (2024)
• Raghuram Rajan (Economist and former RBI Governor)
• Sachin Bansal (Co-founder and former CEO & Chairman of Flipkart)
• Ashneer Grover (Former Co-founder of BharatPe)
• Chetan Bhagat (Author)
• Amol Parashar (Actor)

Jamia Millia Islamia

• BTech in Civil Engineering
• BTech in Computer Science Engineering
• BTech in Mechanical Engineering
• BTech in Electrical Engineering
• BTech in Electronics and Communication Engineering
• Computer Science Engineering: 15,487
• Electronics and Communication Engineering: 27,358
• Electrical Engineering: 36,816
• Mechanical Engineering: 43,231
• Civil Engineering: 49,097
• Barkha Dutt (News Anchor)
• Shah Rukh Khan (Actor)
• Javed Ali Khan (Former Member of Rajya Sabha)
• Roshan Abbas (Radio Jockey/Film Director)

Delhi Technological University (DTU)

• BTech in Computer Science and Engineering
• BTech in Mathematics and Computing Engineering
• Electronics and Communication Engineering: 11,787
• Computer Science and Engineering: 6,406
• Mathematics and Computing Engineering: 10,289
• Ravi B. Grover (Scientist and first VC and founder of Homi Bhabha National Institute)
• Sushant Singh Rajput (Late Actor)

National Institute of Technology (NIT) Delhi

• BTech in Electronics and Communication
• BTech in Artificial Intelligence and Data Science
• Computer Science Engineering: 9,814
• Electronics and Communication Engineering: 13,698
• Artificial Intelligence and Data Science: 11,270

Netaji Subhas University of Technology (NSUT)

• BTech in Computer Engineering
• BTech in Artificial Intelligence
• Computer Engineering: 1,896 (JEE Mains)
• Mechanical Engineering: 1,60,216 (JAC Delhi)
• Electrical Engineering: 1,26,482
• Artificial Intelligence: 25,502 (JAC Delhi)

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