Education and Information Technologies最新文献

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.

Self-regulated learning (SRL) is a prerequisite for successful learning. However, studies have reported that many students struggle with self-regulation in online learning, indicating the need to provide students with additional support for SRL. This study adopted a design-based research methodology to iteratively design, implement, and evaluate different SRL recommendation designs. The main study consisted of three cycles, which were conducted in a fully online graduate course. Eighty-one students who enrolled in this course over three semesters participated in the study. The qualitative results generated from student interviews were assessed immediately after each cycle, and students’ perceptions were adopted to refine the design of next cycle. The quantitative results were analyzed after the last cycle of the study to examine the effects of different SRL recommendation designs on students’ SRL skills and learning performance. The results showed that the different recommendations designs had significant effects on students’ SRL skills. The refined system adopted in the last cycle showed the greatest potential for promoting students’ SRL skills, although no significant effect was found on their learning performance. These findings resulted in a set of design guidelines that can shed light on future literature, including: (a) incorporate personalization into the design rationale, (b) ensuring the ease of use of the system, (c) enriching users’ learning experience, and (d) sustaining users’ motivation.

In the dynamic educational context of Malaysia, this study examines the impact of integrating Unplugged Activities (UA) with Block-Based Programming (BBP) on improving the computational thinking (CT) skills of secondary students in full boarding schools in Northern Peninsular Malaysia. Using a quasi-experimental design and mixed-methods analysis, the research evaluates the impact of these teaching methods on students’ CT skills and attitudes toward programming. This research compares the results between a group that uses only BBP and another that combines both UA and BBP. The results indicate that CT skills improved in both groups, while students in the UA + BBP group showed more significant gains in confidence and a more positive attitude toward programming. These results provide valuable insights into pedagogical strategies within digital education and highlight the benefits of an integrated approach that combines tactile learning experiences with digital technologies. By combining hands-on activities with technology-based instruction, this approach not only deepens students’ understanding of CT concepts but also positively changes their perception and engagement with programming.

With the development of information and communication technology, project-based learning (PBL) has become an important pedagogical approach. Group leaders are critical in PBL, and prestige influences learner leadership. Regulation affects learners’ prestige, but research on their relationship is lacking. Through content analysis and epistemic network analysis, we examine the regulatory patterns of 21 learners engaged in multi-layered online PBL through online collaborative learning activities over 14 weeks. The analysis results show that: (1) High-prestige learners engaged significantly in “socially shared regulation (U = 24.0, Z = -2.183, p = 0.029)”, “monitoring (U = 26.5, Z = -2.008, P = 0.043)”, “task understanding (U = 15.0, Z = -2.829, p = 0.004)”, and “organizing O (U = 20.5, Z = 0.015, p = 0.013)”. (2) The regulatory patterns during PBL stages show that high-prestige learners focus on task dimensions in intra-group discussions. (3) High-prestige learners display positive emotions in inter-group assessments and intra-group refinements. In contrast, low-prestige learners exhibit higher negative emotional engagement. (4) There is a strong correlation between socially shared regulation (GRG = 0.780), content monitoring (GRG = 0.728), and learners’ prestige. Socially shared regulation (p = 0.001), self-regulation (p = 0.001), monitoring (p = 0.006), evaluation (p = 0.019), content monitoring (p = 0.000), and process monitoring (p = 0.018) all significantly positively impact learners’ prestige. The findings suggest that providing self-regulation and socially shared regulation scaffolding for PBL and utilizing various other methods to enhance learner regulation of learning are likely to increase learners’ prestige and PBL effectiveness.

In learnersoucing, automatic evaluation of student-generated content (SGC) is significant as it streamlines the evaluation process, provides timely feedback, and enhances the objectivity of grading, ultimately supporting more effective and efficient learning outcomes. However, the methods of aggregating students’ evaluations of SGC face the problems of inefficiency and cold start. The methods of combining feature engineering and deep learning suffer from the problems of insufficient accuracy and low scalability. This study introduced an automated SGC quality evaluation method based on a large language model (LLM). The method made a comprehensive evaluation by allowing LLM to simulate the cognitive process of human evaluation through the Reason-Act-Evaluate (RAE) prompt and integrating an assisted model to analyze the external features of SGCs. The study utilized the SGCs in a learnersourcing platform to experiment with the feasibility of the method. The results showed that LLM is able to achieve high agreement with experts on the quality evaluation of SGC through RAE prompt, and better results can be achieved with the help of assisted models.