Education and Information Technologies最新文献

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.

Artificial Intelligence (AI) is increasingly being integrated into educational settings, with Intelligent Personal Assistants (IPAs) playing a significant role. However, the psychological impact of these AI assistants on fostering active learning behaviors needs to be better understood. This research study addresses this gap by proposing a theoretical model to outline and predict active learning dynamics. Data was collected from 237 validated questionnaires and analyzed using partial least squares structural equation modeling. Our results confirm most hypotheses advanced in our model, and information redundancy has an unexpected negative and indirect influence on active learning, while perceived familiarity and system quality are positive drivers. Crucial mediators such as perceived usefulness, ease of use, and convenience significantly positively influence active learning outcomes. Interestingly, the relationship between perceived ease of use, perceived convenience, and active learning is positively moderated by AI experience. The most striking and unexpected finding of this study is the preference of university students for familiar systems over high-tech learning methods. This result challenges the common belief that the younger generation is always eager to adopt the latest technology. Instead, our findings suggest that students value convenience and familiarity over novelty in learning systems. This preference is reflected in their systematic evaluation, where convenience and familiarity are considered top priorities. This study provides valuable insights into the potential of AI to enrich the learning experience, thus making it especially relevant to professionals interested in artificial intelligence in international business education.

International large-scale assessments provide cross-national data on students’ cognitive and non-cognitive characteristics. A critical methodological issue that often arises in comparing data from cross-national studies is ensuring measurement invariance, indicating that the construct under investigation is the same across the compared groups. This study addresses the measurement invariance of students’ practices regarding online information (ICTINFO) questionnaire across countries in the PISA 2022 cycle. Some methodological complexities have arisen when testing the measurement invariance across the presence of many groups. For testing measurement invariance, the multiple group confirmatory factor analysis (MGCFA), which is a traditional procedure, was employed first, and then a novel approach, the alignment method, was performed. This study comprised 29 OECD countries, with a total sample size of 187.614 15-year-old students. The MGCFA results revealed that metric invariance was achieved across countries, indicating comparable factor loadings while not the same for factor means. Consistent with MGCFA results, the alignment method identified noninvariant parameters exceeding the 25% cut-off criteria across countries. Monte Carlo simulation validated the reliability of the alignment results. This study contributes to international assessments by providing a detailed examination of measurement invariance and comparing the findings from various methodologies for improving assessment accuracy. The results provide evidence-based recommendations for policymakers to ensure fair and equitable evaluations of student performance across different countries, thereby contributing to more reliable and valid international assessments.

The present study aimed to analyse if self-control, self-esteem and self-efficacy are related to the use of artificial intelligence tools. These tools are being incorporated to educational practices, but there is a lack of empirical evidence about the relation between artificial intelligence use by students and their personal and psychological characteristics. Drawing a profile of students concerning their use of artificial intelligence is imperative in order to design effective learning strategies. This was a cross-sectional study including 1 761 undergraduate students enrolled in different degrees related to education and psychology. Data collection was conducted using validated self-reports that showed appropriate psychometric properties. According to linear regression analyses, low levels of self-control were related to a higher frequency of artificial intelligence use. Logistic regression analyses showed that self-control and self-efficacy were associated with using artificial intelligence to solve daily doubts, due to the need of interacting with someone and to do academic tasks instead of the student. Moreover, higher scores in self-esteem decreased the odds of using artificial intelligence due to the need of interacting with someone. Educators should take into account these findings when implementing the use of artificial intelligence in their educational strategies with university students.

In the wake of the COVID-19 pandemic, the demand for online learning has surged, driving rapid developments in online education. This technological advancement aligns with the global push to achieve the United Nations Sustainable Development Agenda for 2030. Despite extensive research on online learning efficacy, there is a gap in understanding the sustainability of transitions from offline to online modes. This study employs Structural Equation Model (SEM) and questionnaire surveys to explore factors influencing college students’ shift from offline to online learning. Our findings reveal that switching intention is positively impacted by perceived usefulness of online platforms, perceived ease of use of online platforms, and computer self-efficacy, while negatively affected by perceived risk of online platforms. Interestingly, student satisfaction and relationship inertia weaken the link between switching intention and switching behavior. The study offers strategic recommendations for enhancing sustainable online education, providing crucial insights for educational institutions and stakeholders.

In this study, we investigated the relationship between perceived learner control and student engagement in a blended course. Data were collected from 110 s-year bachelor students through weekly questionnaires to gather information about how they perceived their learner control and engagement in various study activities, including reading literature, watching knowledge clips, doing assignments, attending workgroups, and attending lectures. Most students perceived the knowledge clips and workgroups positively because of their clear structure and interactive elements, respectively. In addition, perceived learner control, behavioral engagement, and emotional engagement varied across different activities, whereas cognitive engagement had a similar moderate score across the activities. No significant positive relationships were found between students' perceived learner control and engagement. However, negative relationships between perceived learner control and cognitive and behavioral engagement were found for reading literature, and a negative relationship between perceived learner control and cognitive engagement was identified for attending lectures. We conclude that, in general, perceived learner control is not a significant factor for student engagement in blended learning. However, for particular activities, student engagement may increase as their perceived learner control decreases. The results extend the understanding of the relationship between perceived learner and student engagement, which varied at an activity level. Additionally, the findings suggest that teachers could consider enhancing student engagement by assigning different levels of learner control to students based on their needs.

With the prevalence of Large Language Model-based chatbots, middle school students are increasingly likely to engage with these tools to complete their assignments, raising concerns about its potential to harm students’ learning motivation and learning outcomes. However, we know little about its real impact. Through quasi-experiment research with 127 Chinese middle school students, we examined the impact of completing assignments with a Large Language Model-based chatbot, wisdomBot, on middle school students’ assignment performance, learning outcomes, learning motivation, learning satisfaction, and learning experiences; we also summarized teachers’ reflections on learning design. Compared to control groups, the Large Language Model chatbot-assisted group demonstrated significantly higher assignment submission rates, word counts, and scores in assignment performance. However, they gained significantly lower scores on materials refinement and knowledge tests. No significant differences have been observed in learning motivation, satisfaction, enjoyment, and students’ ability to migrate their knowledge. The majority of students expressed satisfaction and willingness to continue using the tool. We also identified three key gaps in learning designs, including providing scaffolds for the potential prompts, suggesting group collaboration mode, and relinquishing the authoritarian of the teacher. Our findings provide insights regarding with Large Language Model-based chatbots we could better design assignment assessment tools, facilitate students’ autonomous learning, provide emotional support, propose guidelines and instructions about applying Large Language Model-based chatbots in K-12, as well as design specialized educational Large Language Model-based chatbots.