The extent to which teachers adopt technology in their teaching practice has long been in the focus of research. Indeed, a plethora of models exist explaining influential factors and mechanisms of ...technology use in classrooms, one of which—the Technology Acceptance Model (TAM) and versions thereof—has dominated the field. Although consensus exists about which factors in the TAM might predict teachers’ technology adoption, the current field abounds in some controversies and inconsistent findings. This meta-analysis seeks to clarify some of these issues by combining meta-analysis with structural equation modeling approaches. Specifically, we synthesized 124 correlation matrices from 114 empirical TAM studies (N = 34,357 teachers) and tested the fit of the TAM and its versions. Overall, the TAM explains technology acceptance well; yet, the role of certain key constructs and the importance of external variables contrast some existing beliefs about the TAM. Implications for research and practice are discussed.
•The Technology Acceptance Model (TAM) explains teachers' technology adoption.•Relations among variables in the TAM are synthesized meta-analytically.•The TAM and its versions fit the data well—even for subsamples of teachers.•Within the TAM, direct effects of PU on BI and ATT on USE exist.•The TAM explains behavioral intentions and technology use significantly.
The Technology Acceptance Model (TAM) is a key model describing teachers' intentions to use technology. This meta-analysis clarifies some of the contradictory findings surrounding the relations ...within the TAM for a sample of 45 studies comprising 300 correlations. We evaluate the overall fit of the TAM and its structural parameters, and quantify the between-sample variation through meta-analytic structural equation modeling. The TAM fitted the data well, and all structural parameters were statistically significant. On average, the TAM variables explained 39.2% of the variance in teachers' intentions to use technology. Several sample, measurement, and publication characteristics, including teachers’ experience and the representation of the TAM variables, moderated the relations within the TAM. Overall, the TAM represents a valid model explaining technology acceptance—however, the degree of explanation and the relative importance of predictors vary across samples. Implications for further research, in particular the generalizability of the TAM, are discussed.
•The structural relations in the TAM are synthesized meta-analytically.•Parameter-based MASEM suggests random effects in regression coefficients.•The direct effect PU.→BI is statistically significantly different from zero.•Variation in regression coefficients can be partly explained by study features.•The TAM is a valid model describing teachers' technology use intentions.
The COVID-19 pandemic has forced a shift to online teaching and learning (OTL) in colleges and universities across the globe, requiring teachers to adapt their teaching in a very short ...time—independent of whether they were prepared. Drawing from an international sample of N = 739 higher education teachers in 58 countries, the present study sheds light on teachers' readiness for OTL at the time of the pandemic by (a) identifying teacher profiles based on a set of key dimensions of readiness; (b) explaining profile membership by individual teacher characteristics, contextual aspects of the shift to OTL, and country-level indicators representing educational innovation and cultural orientation. We conducted latent profile analysis and identified three teacher profiles with consistently high or low readiness or an inconsistent readiness profile—hence, teachers in higher education are not a homogeneous group. Importantly, key individual and contextual variables, such as teachers’ gender and prior OTL experience, the context of the OTL shift, the innovation potential in education, and cultural orientation, explained profile membership. We discuss these findings with respect to the nature of the profiles, how they can be understood with respect to key determinants, and their implications for OTL in higher education.
•Teachers' readiness for OTL (Online Teaching and Learning) is multifaceted.•Three readiness profiles existed (high, low, and inconsistent readiness).•Gender, prior experience, and OTL preparation explained profile membership.•Countries ready for OTL avoided uncertainty and were long-term oriented.•Innovation and culture explained profile membership across countries.
The main aim of this two-step mixed-method study was to explore the effectiveness of the strategies used to prepare pre-service teachers for technological pedagogical content knowledge (TPACK). ...Specifically, we focused on the strategies included in the synthesis of qualitative evidence (SQD) model: (1) using teacher educators as role models, (2) reflecting on the role of technology in education, (3) learning how to use technology by design, (4) collaboration with peers, (5) scaffolding authentic technology experiences, and (6) providing continuous feedback. To explore the relation between the perceived occurrences of the SQD-strategies and TPACK (controlled for pre-service teachers’ general attitudes towards technology), survey data were collected from a sample of 688 final-year pre-service teachers in Belgium. In a next step, 16 telephone interviews and 6 in-depth interviews were conducted to gain a more in-depth insight into the nature of the 6 strategies and their influences on TPACK. The quantitative analyses indicated positive correlations between the SQD-strategies and TPACK, controlled for general attitudes towards technology. The findings from the qualitative analyses showed that teachers acknowledged the importance of the six strategies. However, the respondents emphasized that some of the six strategies are often underutilized. Based on the quantitative and qualitative results, the discussion provides recommendations to improve the potential of pre-service training to enhance future teachers’ TPACK.
Does computer programming teach students how to think? Learning to program computers has gained considerable popularity, and educational systems around the world are encouraging students in schools ...and even children in kindergartens to engage in programming activities. This popularity is based on the claim that learning computer programming improves cognitive skills, including creativity, reasoning, and mathematical skills. In this meta-analysis, we tested this claim performing a 3-level, random-effects meta-analysis on a sample of 105 studies and 539 effect sizes. We found evidence for a moderate, overall transfer effect (g = 0.49, 95% CI 0.37, 0.61) and identified a strong effect for near transfer (g = 0.75, 95% CI 0.39, 1.11) and a moderate effect for far transfer (g = 0.47, 95% CI 0.35, 0.59). Positive transfer to situations that required creative thinking, mathematical skills, and metacognition, followed by spatial skills and reasoning existed. School achievement and literacy, however, benefited the least from learning to program. Moderator analyses revealed significantly larger transfer effects for studies with untreated control groups than those with treated (active) control groups. Moreover, published studies exhibited larger effects than gray literature. These findings shed light on the cognitive benefits associated with learning computer programming and contribute to the current debate surrounding the conceptualization of computer programming as a form of problem solving.
Educational Impact and Implications Statement
In this meta-analysis, we tested the claim that learning how to program a computer improves cognitive skills even beyond programming. The results suggested that students who learned computer programming outperformed those who did not in programming skills and other cognitive skills, such as creative thinking, mathematical skills, metacognition, and reasoning. Learning computer programming has certain cognitive benefits for other domains.
Value-added (VA) models are used for accountability purposes and quantify the value a teacher or a school adds to their students' achievement. If VA scores lack stability over time and vary across ...outcome domains (e.g., mathematics and language learning), their use for high-stakes decision making is in question and could have detrimental real-life implications: teachers could lose their jobs, or a school might receive less funding. However, school-level stability over time and variation across domains have rarely been studied together. In the present study, we examined the stability of VA scores over time for mathematics and language learning, drawing on representative, large-scale, and longitudinal data from two cohorts of standardized achievement tests in Luxembourg (N = 7,016 students in 151 schools). We found that only 34-38% of the schools showed stable VA scores over time with moderate rank correlations of VA scores from 2017 to 2019 of r = .34 for mathematics and r = .37 for language learning. Although they showed insufficient stability over time for high-stakes decision making, school VA scores could be employed to identify teaching or school practices that are genuinely effective-especially in heterogeneous student populations.
Considerable research has demonstrated that teachers' self-efficacy plays a major role in implementing instructional practices. Only few studies, however, have examined the interplay between how ...teachers' self-efficacy and the challenges that lie outside their influence are related to their implementation of cognitive-activation strategies (CASs), especially in science classrooms. Using the Trends in Mathematics and Science Study 2015 data from science teachers in Grades 4, 5, 8, and 9, we explored the extent to which teachers' self-efficacy in science teaching and the perceived time constraints explained variations in the enactment of general and inquiry-based CAS. Findings from the overall sample showed that highly self-efficacious teachers reported more frequent implementation of both general and inquiry-based CAS, whereas those who perceived strong time constraints reported a less frequent use of inquiry-based CAS. These relationships also existed across grade levels, except on the relations between perceived time constraint and inquiry-based CAS, which was only significant for the science teachers in Grade 9. We discuss these findings in light of variations in the core competencies of science curriculum, teachers' competences, and the resources for science activities between primary and secondary education. We also point to the theoretical implications of this study for enhancing the conceptual understanding of generic and specific aspects of CAS and the practical implications for teacher education, professional development, and educational policy.
In a very short time, secondary school education across the globe transitioned to online learning and teaching, in response to the Covid-19 pandemic. This study aims at identifying teacher profiles ...in secondary education to better understand perceptions of both individual and institutional readiness to transition to online teaching. To do this, the current study grouped teachers on the basis of their TPACK self-efficacy beliefs, online presence and perceived institutional support for online teaching. To date, data have been collected from teachers (N = 222) from 20 countries. The data were submitted to latent profile analysis to identify readiness profiles. The added value of the current study lies in the combined view of individual and institutional readiness and the uniqueness of the dataset. It provides a large-scale international perspective and a wide range of possible experiences. Findings inform how education institutions can personalise and support transitions to online teaching.
Computer coding—an activity that involves the creation, modification, and implementation of computer code and exposes students to computational thinking—is an integral part of today's education in ...science, technology, engineering, and mathematics (STEM) (Grover and Pea, 2013). As technology is advancing, coding is becoming a necessary process and much-needed skill to solve complex scientific problems efficiently and reproducibly, ultimately elevating the careers of those who master the skill. With many countries around the world launching coding initiatives and integrating computational thinking into the curricula of higher education, secondary education, primary education, and kindergarten, the question arises, what lies behind this enthusiasm for learning to code? Part of the reasoning is that learning to code may ultimately aid students' learning and acquiring of skills in domains other than coding. Researchers, policy-makers, and leaders in the field of computer science and education have made ample use of this argument to attract students into computer science, bring to attention the need for skilled programmers, and make coding compulsory for students. Bill Gates once stated that “learning to write programs stretches your mind, and helps you think better, creates a way of thinking about things that I think is helpful in all domains” (2013). Similar to the claims surrounding chess instruction, learning Latin, video gaming, and brain training (Sala and Gobet, 2017), this so-called “transfer effect” assumes that students learn a set of skills during coding instruction that are also relevant for solving problems in mathematics, science, and other contexts. Despite this assumption and the claims surrounding transfer effects, the evidence backing them seems to stand on shaky legs—a recently published paper even claimed that such evidence does not exist at all (Denning, 2017), yet without reviewing the extant body of empirical studies on the matter. Moreover, simply teaching coding does not ensure that students are able to transfer the knowledge and skills they have gained to other situations and contexts—in fact, instruction needs to be designed for fostering this transfer (Grover and Pea, 2018).
In this opinion paper, we (a) argue that learning to code involves thinking processes similar to those in other domains, such as mathematical modeling and creative problem solving, (b) highlight the empirical evidence on the cognitive benefits of learning computer coding that has bearing on this long-standing debate, and (c) describe several criteria for documenting these benefits (i.e., transfer effects). Despite the positive evidence suggesting that these benefits may exist, we argue that the transfer debate has not yet to be settled.
The concept of school climate has received much attention as a predictor of educational outcomes, including students’ well-being, academic achievement, and motivation. To measure this concept, ...international large-scale assessments often rely on students’ perceptions of its different dimensions, such as their sense of belonging, teacher support, and disciplinary climate. However, students may perceive these dimensions differently and, ultimately, create inter-individual variation—a variation that has been explained only to a limited degree in the current body of literature. The present study explores this variation for the Norwegian PISA 2015 data (
N
= 5313). Using the person-centered approach of latent profile analysis, we found evidence for the existence of three student profiles: (1) students with consistently positive perceptions, (2) students with moderately negative perceptions, and (3) students with extremely negative perceptions, especially concerning teachers’ fairness and bullying. These results support the hypothesis of individual differences in school climate perceptions.
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