Computers and Education Open最新文献

Measuring Technological Pedagogical and Content Knowledge (TPACK) in context is still a pertinent issue, as previously rather decontextualized self-reports have been the predominant measure, while knowledge test instruments are scarce. Self-reports can be interpreted as general measures of performance-related self-beliefs. Still, due to the contextualized nature of TPACK and potential biases, their use as proxies for actual knowledge has been criticized. Self-reports may be especially gender biased as women often underestimate their performance in STEM subjects. Drawing on a sample of N = 161 mathematics in-service and pre-service teachers, we aim to analyze (i) the structure of the self-reported knowledge and (ii) the relationship between self-reported and contextualized actual knowledge. To this end, we used general TPACK self-reports and a test instrument that infers the amount of knowledge separately for each dimension from performance over multiple authentic demands that teachers encounter teaching secondary school mathematics. The current study shows that the TPACK self-beliefs can be separated and measured reliably. Although all self-beliefs show bivariate relations to corresponding actual knowledge dimensions, this changes for PCK and TPCK self-beliefs when other actual knowledge dimensions are controlled. We interpret these findings that TCK self-belief and to a lesser degree CK self-belief seem to be calibrated according to corresponding actual knowledge, while PCK and TPCK self-beliefs are primarily calibrated according to non-pedagogy-related actual knowledge. Lastly, we do not find gender biases, but a small gender effect with lower actual and self-reported knowledge for female teachers over all dimensions.

Designing effective interventions that foster (pre-service) teachers' knowledge to teach with technologies is paramount in education research. Researchers have prominently relied on the TPACK-model as theoretical foundation to design such interventions. However, a myriad of distinct TPACK-based interventions emerged, which likely targeted the different knowledge components of TPACK to varying extents. Given this diversity and the lack of performance-based measures to estimate competence growth, little is known about the effectiveness of respective interventions. In the present synthesis study, we therefore sought to systemize TPACK-based interventions regarding targeted knowledge domains across various contexts. Accordingly, we scrutinized which of the TPACK-components were explicitly targeted in TPACK-based interventions within the framework of a systematic review. Further, we conducted a subsequent meta-analysis based on studies applying performance-based measures to investigate whether the targeted knowledge domains affected the effectiveness of interventions. Based on a set of N = 163 primary intervention studies and one theoretical contribution, our analyses suggest that Technological Knowledge was the most prominent targeted TPACK-component. Interestingly, in more than 20% of the interventions, specific training on Technological Pedagogical Content Knowledge (i.e., TPCK) was absent although TPCK is considered crucial for successful technology integration. Results further revealed that researchers do not seem to have adapted the design of interventions on instructional contexts (such as the expertise level of the target audience). The results of the subsequent meta-analysis (N = 8) further provided no clear evidence that targeted TPACK-components affected the effectiveness of interventions.

Existing Technological Pedagogical and Content Knowledge (TPACK) surveys target pre-service or K-12 teachers, whereas none have been specifically adapted for university teachers. To adequately measure TPACK-competences of university teachers, the specific characteristics of teaching in a university context need to be taken into account. Survey items that are not contextualized to the target participants increase the risk of measurement error and bias. Therefore, we adapted existing TPACK surveys to specifically measure university teachers’ competences for teaching with technology. We shortlisted five existing TPACK surveys and scrutinized their respective subscales and items. We then adapted these items to more adequately capture context-specific experiences for university teachers to ensure construct validity. We collected two waves of data to test our adapted TPACK survey, which comprises 31 items distributed across seven subscales, among teachers from various disciplines in a large university. With confirmatory factor analysis, we confirmed the seven-factor structure of the adapted TPACK survey in both data waves. Moreover, the seven subscales showed adequate internal consistency. An exploration of TPACK competences among teachers from different disciplines showed both similarities as well as dissimilarities. An example of similarities is that university teachers from all disciplines felt most competent in CK and PCK, while they reported relatively low competence ratings for TPCK and TPK. Besides, an example of dissimilarities is PK; teachers from the discipline of science and engineering reported the highest score compared to other disciplines in the prior wave, while they evaluated themselves third lowest in the latter wave.

The technological pedagogical content knowledge (TPACK) framework is a prominent framework for describing the knowledge teachers require for teaching in the digital era. Recently, the component of “context” was officially recognized as an additional domain of knowledge. Since then, this domain has started to gradually gain more attention in TPACK research. Yet research on contextual knowledge (XK) appears somewhat sporadic, indicating the need for greater clarity on this construct. As the domain representing the knowledge of educational contexts, the role of experience in educational contexts emerges as a key point for shedding light on this construct. The present systematic literature review aimed to offer an overview of the representations and functions associated with XK through the lens of experience and investigated this domain through comparing the empirical literature focusing on preservice teachers (less experienced) to that focusing on inservice teachers (more experienced). Systematically screening the literature resulted in a final sample of 15 studies conducted among preservice teachers and 23 on inservice teachers. Records were analyzed through qualitative codings and quantitative comparisons of these codes between these two groups of studies. Findings revealed a consistent multifaceted structure of XK across both inservice and preservice teacher studies with a distinctive greater focus on school-level factors in inservice teacher studies. In addition, across groups, studies provided evidence confirming XK as a domain supporting teachers’ practice, that can be developed, and that relates to other pedagogically relevant constructs. These findings are discussed with regards to their implications for researchers, practitioners, and stakeholders.

Due to advances in Artificial Intelligence (AI), computer science education has rapidly started to include topics related to AI along K-12 education. Although this development is timely and important, it is also concerning because the elaboration of the AI field for K-12 is still ongoing. Current efforts may significantly underestimate the role of data, the fundamental component of an AI system. If the goal is to enable students to understand how AI systems work, knowledge of key concepts related to data processing is a prerequisite, as data collection, preparation, and engineering are closely linked to the functionality of AI systems. To advance the field, the following research provides a comprehensive collection of key data-related concepts relevant to K-12 computer science education. These concepts were identified through a theoretical review of the AI field, aligned through a review of AI curricula for school education, evaluated through interviews with domain experts and teachers, and structured hierarchically according to the data lifecycle. Computer science educators can use the elaborated structure as a conceptual guide for designing learning arrangements that aim to enable students to understand how AI systems are created and function.

Information and communications technology (ICT) plays an important role in teaching and learning English as a Foreign Language (EFL) at school. However, there are still relevant research gaps regarding (prospective) teachers’ attitudes and approaches towards ICT-assisted EFL teaching and learning. The present study examined possible different groups of pre-service teachers based on patterns regarding their approaches to ICT-assisted EFL teaching and learning and associations with study level and value and self-efficacy beliefs based on a sample of pre-service EFL teachers from various universities in North Rhine-Westphalia (Germany). Results from a cluster analysis identified three distinct clusters of pre-service teachers who differed significantly in their transmissive and constructivist approaches to ICT-assisted EFL teaching and learning. Cluster allocation varied significantly depending on participants’ study level. Further cluster comparisons also partly indicated significant associations between participants’ transmissive and constructivist beliefs and their value beliefs regarding ICT-assisted EFL teaching and learning. Participants’ self-efficacy beliefs regarding ICT-assisted EFL teaching did not vary significantly between different clusters of pre-service teachers. Possible implications are discussed conclusively.

The adoption of intelligent tutoring systems (ITSs) worldwide has led to a considerable accumulation of process data as students interact with different learning topics within these systems. Typically, these learning topics are structured within ITSs (e.g., the fraction topic includes subtopics such as a fraction number line subtopic). However, there is a lack of methods that offer quick, data-driven insights into the content structure of ITSs, particularly through easily accessible visualizations. Here, we applied psychological network analysis to process data (230,241 students; 5,365,932 problem sets) from an ITS for learning mathematics to explore performance interdependencies between 40 different subtopics. We argue that the visualization of these content interdependencies allows for a quick empirical evaluation of the validity of the existing structuring of the respective learning content. These insights allow for deriving recommendations concerning potential changes in the ITS structure and are thus highly valuable for ITS developers. Our results are also relevant for researchers as the interdependencies illustrated through psychological network analysis can contribute towards a better understanding of the interplay between mathematical skills. Together, our results indicate that psychological network analysis represents a valuable data-driven method to evaluate and optimize ITSs.

The sharing of personally identifiable information (PII) through social media platforms poses known risks to children's online privacy and safety. While the risks of oversharing PII through a range of digital contexts are becoming better understood, limited research has documented the social media practices of educational institutions that have a fiduciary responsibility to children. This study seeks to understand the role of educational institutions in putting students’ privacy at risk by investigating their social media practices on X (formerly Twitter). This paper extends previous research (Rosenberg et al., 2022a) by exploring how often students' PII (e.g., names, images, and phone numbers) and other social identities (e.g., gender identity, religion, race, and ethnicity) are exposed on X. Additionally, we examine both images and videos of posts shared by educational institutions. Using a data set of approximately 20.6 million posts made by K-12 education institutions in the United States, we explore the extent to which students’ PII is shared with the public on X. Our analyses suggest that approximately 4 % of posts that contain images and videos (approximately 800,000 posts in the overall data set) included an identifiable face of a student or students along with their name(s) and 2.3 % ascribed students’ gender identity. Given the extent of disclosed PII and the potential privacy risks, this study provides additional insight for educational stakeholders to cultivate safer social media practices, seeking to mitigate potential risks to students' privacy and improve students’ digital rights.

There has been an explosion in uses of educational technology (EdTech) to support schools’ teaching, learning, assessment and administration. This article asks whether UK EdTech and data protection policies protect children's rights at school. It adopts a children's rights framework to explore how EdTech impacts children's rights to education, privacy and freedom from economic exploitation, taking Google Classroom as a case study. The research methods integrate legal research, interviews with UK data protection experts and education professionals working at various levels from national to local, and a socio-technical investigation of the flow of children's data through Google Classroom. The findings show that Google Classroom undermines children's privacy and data protection, potentially infringing children's other rights. However, they also show that regulation has impacted on Google's policy and practice. Specifically, we trace how various governments’ deployment of a range of legal arguments has enabled them to regulate Google's relationship with schools to improve its treatment of children's data. Although the UK government has not brought such actions, the data flow investigation shows that Google has also improved its protection of children's data in UK schools as a result of these international actions. Nonetheless, multiple problems remain, due both to Google's non-compliance with data protection regulations and schools’ practices of using Google Classroom. We conclude with a blueprint for the rights-respecting treatment of children's education data that identifies needed actions for the UK Department for Education, data protection authority, and industry, to mitigate against harmful practices and better support schools.

This paper investigates how lecturers’ connectedness with students affected their experience of online teaching during the first COVID-19 lockdown in the UK and Switzerland. We examined how this connectedness predicted lecturers’ self-efficacy in online teaching. This was in addition to other social context variables (connectedness with colleagues and perceived support from the university) and their previous experience with digital tools. The shift to online teaching in the lockdown period abruptly removed any in-person contact between lecturers and their students. Lecturers’ self-efficacy in online teaching is crucial to student motivation, achievement, and the lecturer's own teaching experience. Likewise, lecturers’ connectedness with students and colleagues has been identified as a key factor in learning. Consequently, this study explored how different forms of connectedness predicted lecturers’ self-efficacy in the new teaching environment. A total of 252 lecturers from UK and Swiss universities completed an online survey about their teaching experiences before and during the COVID-19 lockdown. Multiple regressions were used to predict lecturers’ online teaching self-efficacy. The results revealed that connectedness with students was a significant and positive predictor of online teaching self-efficacy. However, connectedness with colleagues and perceived support from the university did not. The perception that digital tools enhanced teaching prior to the lockdown was a significant predictor only for the UK lecturers, but not for the Swiss ones. These findings point towards lecturers’ connectedness with their students being a pathway to success in online teaching.