Education and Information Technologies最新文献

The rapid development of generative artificial intelligence (GenAI), including large language models (LLM), has merged to support students in their academic writing process. Keeping pace with the technical and educational landscape requires careful consideration of the opportunities and challenges that GenAI-assisted systems create within education. This serves as a useful and necessary starting point for fully leveraging its potential for learning and teaching. Hence, it is crucial to gather insights from diverse perspectives and use cases from actual users, particularly the unique voices and needs of student-users. Therefore, this study explored and examined students' perceptions and experiences about GenAI-assisted academic writing by conducting in-depth interviews with 20 Chinese students in higher education after completing academic writing tasks using a ChatGPT4-embedded writing system developed by the research team. The study found that students expected AI to serve multiple roles, including multi-tasking writing assistant, virtual tutor, and digital peer to support multifaceted writing processes and performance. Students perceived that GenAI-assisted writing could benefit them in three areas including the writing process, performance, and their affective domain. Meanwhile, they also identified AI-related, student-related, and task-related challenges that were experienced during the GenAI-assisted writing activity. These findings contribute to a more nuanced understanding of GenAI's impact on academic writing that is inclusive of student perspectives, offering implications for educational AI design and instructional design.

Blended learning (BL) combines traditional classroom activities with online learning resources, enabling students to obtain higher academic performance through well-defined interactive learning strategies. However, lacking the capacity to self-regulate their learning, many students might fail to comprehensively study the learning materials after face-to-face learning. In this study, a learning analytics dashboard (LAD)-based self-regulated learning (SRL) approach is proposed to enhance the students’ practices of SRL in an e-book-based BL environment. The proposed approach aims to support students to precisely reflect on their face-to-face e-book reading activities, effectively review the e-book learning materials after the face-to-face learning sessions, and, finally, set new goals for their next face-to-face learning session by using a LAD. To evaluate the effects of the proposed approach, a quasi-experimental design was deployed in a university-level course that adopted a BL model. The experimental group learned through the proposed approach using an e-book and the LAD, whereas the control group learned using the conventional BL approach using only the e-book. The results of the one-way analysis of covariance (ANCOVA) and Mann–Whitney U test demonstrate a statistically significant difference (p-value less than 0.01) between both groups in terms of students’ learning outcomes, awareness of SRL, self-efficacy (SE), and e-book reading engagements. This provides educators with evidence of the effectiveness of an explicit SRL approach in BL, which not only improves student learning outcomes from the given course and awareness of self-regulation and SE but also increases course engagement compared to students who learn with conventional BL approaches.

Grounded in the activity theory, we adopted a sequential explanatory mixed-methods approach to explore the impact of automated written corrective feedback (AWCF) on English as a foreign language (EFL) learners’ academic writing skills (i.e. task achievement, coherence and cohesion, lexicon, and grammatical range and accuracy). To this end, two intact classes were selected and randomly assigned to an electronic class (30 EFL learners), receiving AWCF electronically, and a non-electronic class (26 EFL learners), receiving written corrective feedback (WCF) non-electronically. Both groups of learners engaged in interactive writing activities guided by the principles of the activity theory, which capitalised on the roles of writing collaboration, social environment, and the mediation of electronic/nonelectronic artefacts to develop the writing skills. The required quantitative and qualitative data were collected via IELTS academic writing Task 1 and Task 2, a stimulated recall technique, and an individual semi-structured interview. The results of one-way ANCOVA indicated that the electronic learners outperformed their non-electronic counterparts in writing performance, task achievement, and grammatical range and accuracy, whilst no significant differences were found between the two groups’ coherence and cohesion and lexicon. The stimulated recall technique, conducted with seven electronic EFL learners, confirmed the electronic learners’ behavioural, cognitive, and affective engagement with the AWCF. The individual semi-structured interview, conducted with the same electronic learners, also indicated the electronic learners’ positive and negative attitudes and perceptions towards the AWCF, further corroborating the findings. Pedagogical implications are discussed within the framework of the activity theory to clarify how instructional procedures and learning environments can be designed to more effectively contribute to EFL learners’ interactive writing activities and, hence, their writing skills development.

Though online peer assessment is recognized as a critical factor in enhancing learning performance, pedagogical strategies and analysis of students’ peer assessment at the group level, rather than the individual level, are underexplored. Online group assessment (OGA) focuses on assessing peer-group work in an online environment. A total of 64 student teachers participated in this study, where they were divided into multiple groups of four. Each group was required to collaborate on completing an instructional design and engage in OGA activities. We utilized the Technological Pedagogical Content Knowledge (TPACK) scale to assess the instructional designs of student teachers, evaluating their ability to integrate technology, pedagogy, and content knowledge. In this research, we consider the TPACK scores of each group’s instructional design as their learning performance. The correlations between providing, receiving, and responding to comments and group learning performance were explored by adopting a mixed methods approach. The results indicated that OGA enhanced group learning performance. Providing comments was more associated with improved group learning performance than receiving and responding to them. Furthermore, providing informative comments was more associated with group learning performance than providing other types of comments. In addition, innovative responses were positively associated with group learning performance, while uptake responses were negatively associated with group learning performance. Finally, the discussion and suggestions of intervention for different stages of OGA are provided to help design and implement OGA activities in the future.

Digital competence is crucial for technology integration in education, with teacher educators playing a vital role in preparing student teachers for digitalized environments. In our conceptualization of teachers’ digital competence (TDC), we emphasize its embeddedness in a professional context. The Digital Competence for Educators (DigCompEdu) framework aligns with this understanding, yet research focusing on teacher educators is limited. To address this gap, we followed a quantitative research strategy to explore different sources of validity evidence for the DigCompEdu in a small, non-representative Hungarian teacher-educator sample (N = 183) via an online questionnaire. Our study, regarding the DigCompEdu as a measure of TDC, aims to (1) establish validity evidence based on internal structure concerns via Partial Least Squares structural equation modelling to evaluate the validity and reliability of the tool, (2) compare TDC self-categorization with test results to provide validity evidence based on the consequences of testing, and (3) explore validity evidence based on relationships of TDC with other variables such as age, technological, and pedagogical competence. Our findings reveal a significant mediating effect of professional engagement on teacher educators’ ability to support student teachers’ digital competence development. Despite the sample’s limitation, this study contributes to refining the DigCompEdu framework and highlights the importance of professional engagement in fostering digital competence among teacher educators.

This study explores the level of engagement with Information and Communication Technology (ICT) supported content among students and teachers in learning sciences and basic computing at Rwandan lower secondary schools. Data were collected from ten well-equipped smart classrooms across ten schools. A sample of 394 participants included ten deputy headteachers, 40 teachers, and 344 students. Interviews, classroom observations, and surveys were used for data collection. Findings revealed a significant digital divide among students due to limited ICT literacy, time constraints, and limited access to computer devices. Also, the findings indicate that teachers faced various challenges, including underutilisation of ICT in science lessons, primarily due to inadequate digital competence. The study recommends strategies to enhance students’ digital skills through training programs and foster ICT-oriented teacher communities of practice via professional development to improve digital competence and innovative teaching methods.

Feedback is critical in providing personalized information about educational processes and supporting their performance in online collaborative learning environments. However, giving effective feedback and monitoring its effects, which is especially important in online environments, is a complex issue. Although providing feedback by analyzing online learning behaviors, it is unclear how the effectiveness of this feedback translates into online learning experiences. The current study aims to compare the behavioral patterns of online system engagement of students who receive and do not receive machine learning-based temporal learning analytics (ML-LA) feedback, to identify the differences between student groups in terms of learning performance, online engagement, and various system usage variables, and to examine the behavioral patterns change over time of students regarding online system engagement. The current study was conducted with the participation of 49 undergraduate students. The study defined three engagement levels using system usage analytics and cluster analysis. While t-test and ANCOVA were applied to pre-test and post-test scores to evaluate students’ learning performance and online engagement, lag sequential analysis was used to analyze behavioral patterns, and the Markov chain was used to examine the change of behavioral patterns over time. The group receiving ML-LA feedback showed higher behavior and cognitive engagement than the control group. In addition, the rate of completing learning tasks was higher in the experimental group. Temporal patterns of online engagement behaviors across student groups are described and compared. The results showed that both groups used all stages of the system features. However, there were some differences in the navigation rankings. The most important behavioral transitions in the experimental group are task and discussion viewing and posting, task posting updating, and group performance viewing. In the control group, the most important behavioral transitions are the relationship between viewing a discussion and making a discussion, then this is followed by the sequential relationship between viewing individual performance and viewing group performance. The results showed that students’ engagement behaviors transitioned from light to medium and intense throughout the semester, especially in the experimental group. For learning designers and researchers, this study can help develop a deep understanding of environment design.

Virtual teaching gained momentum for its ability to drive education continuity in times of disruption. As a result, the implementation of virtual teaching has piqued the attention of the higher education sector to leverage the affordances of this mode of instructional delivery, even in times of non-disruption. This study aims to conduct a review of virtual teaching in the higher education sector to reveal the key research trends of previous publications and areas of focus for future research. A bibliometric analysis is used to identify the key topics, themes, authors, sources, articles, and existing collaborations. To achieve this, papers indexed in the Scopus database between 2012 and 2023 were examined and analysed using VOSviewer. The findings of the study are provided through a quantitative analysis that gives a high-level overview of virtual teaching in the higher education sector and highlights the key performance indicators for the creation of articles and their citation through tables, graphs, and visualisation maps. The research yielded a total of 5,663 publications, of which 2,635 published articles were included in the analysis. The findings reiterate virtual teaching as a move in the direction of sustainable education as its assists in democratising knowledge. The analysis highlights the multifaceted nature of the research topic on virtual teaching, revealing six distinct yet interconnected thematic clusters. This study provides a holistic picture of virtual teaching in the higher education sector by integrating the analysis results with pertinent reviews of literature and makes recommendations for future research.

Artificial intelligence (AI) integration in education has grown significantly recently. However, the potential risks of AI have led to educators being wary of implementing AI systems. To discover whether AI systems can be effective in the classroom in the future, it is critical to understand how risk factors (e.g., perceived safety risks, perceived privacy risks, and urban/rural differences) affect pre-service teachers’ AI acceptance. Therefore, the study aimed to (1) explore the influence of perceived risks and AI trust on pre-service teachers’ intentions to use AI-based educational applications, and (2) investigate possible variations in potential determinants of their intentions to use AI based on urban–rural differences. In this study, data from 483 pre-service teachers in China (262 from rural areas) were obtained by survey and analyzed using confirmatory factor analysis (CFA) and structural equation modeling-based multi-group analysis. The study’s findings demonstrated that while AI trust influenced pre-service teachers’ AI acceptance, the effect was less pronounced than perceived ease of use and perceived usefulness. Most notably, findings showed that perceived privacy and safety risks negatively influence AI trust among pre-service teachers from rural areas, which was a trend not observed in pre-service teachers from urban areas. As a result, to integrate AI-based applications into educational settings, pre-service teachers believed that the AI system must be functionally robust, user-friendly, and transparent. In addition, urban–rural differences considerably affect pre-service teachers’ AI acceptance. This study provides further relevant recommendations for educators and policymakers.

This research analyzed the efficacy of ChatGPT as a tool for the correction and provision of feedback on primary school students' short essays written in both the English and Greek languages. The accuracy and qualitative aspects of ChatGPT-generated corrections and feedback were compared to that of educators. For the essays written in English, it was found that ChatGPT outperformed the educators both in terms of quantity and quality. It detected more mistakes, provided more detailed feedback, its focus was similar to that of educators, its orientation was more balanced, and it was more positive although more academic/formal in terms of style/tone. For the essays written in Greek, ChatGPT did not perform as well as educators did. Although it provided more detailed feedback and detected roughly the same number of mistakes, it incorrectly flagged as mistakes correctly written words and/or phrases. Moreover, compared to educators, it focused less on language mechanics and delivered less balanced feedback in terms of orientation. In terms of style/tone, there were no significant differences. When comparing ChatGPT's performance in English and Greek short essays, it was found that it performed better in the former language in both the quantitative and qualitative parameters that were examined. The implications of the above findings are also discussed.