International Journal of Educational Technology in Higher Education最新文献

This study investigates how engagement (E), foreign language enjoyment (FLE), and ambiguity tolerance (AT) exert mediation/moderation in metaverse-based foreign language learning (FLL). Featuring augment/simulation-based experiences and self-fulfillment/external-control-oriented mechanics, metaverse provides virtualized interactive circumstances involving individuals’ embodied presence/behaviors, aligning with FLL that emphasizes social interaction. Based on the quantified survey data, partial least squares structural equation modeling (PLS-SEM) analyses investigate the significance and positivity of the mediation/moderation relations. According to the results, E exerts fully positive mediation in the effect of intrinsic motivation (IM) on learning effectiveness (LE), FLE exerts partially positive mediation in the effect of classroom social climate (CSC) on E, and AT exerts negative moderation in the positive effect of E on LE. Notably, FLE exerts insignificant mediation in the effect of growth mindset (GM) on E. Therefore, efficient metaverse-based FLL requires synergies of affective factors, i.e., intrinsic motivation, perceptions of classroom social climate, moderate degrees of ambiguity tolerance, and engagement, for sustainable and long-term language learning progress in virtual interactive experiences. At the theoretical level, the findings extend the FLL-related models and advance the understanding of FLL. At the practical level, the findings provide references for more efficient metaverse implementations in FLL.

The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used generative AI tools, ChatGPT, Bing and Bard. Each AI tool was prompted with an essay question from undergraduate (UG) level 4 (year 1), level 5 (year 2), level 6 (year 3) and postgraduate (PG) level 7 biomedical sciences courses. Anonymised AI generated output was then evaluated by four independent markers, according to specified marking criteria and matched to the Frameworks for Higher Education Qualifications (FHEQ) of UK level descriptors. Percentage scores and ordinal grades were given for each marking criteria across AI generated papers, inter-rater reliability was calculated using Kendall’s coefficient of concordance and generative AI performance ranked. Across all UG and PG levels, ChatGPT performed better than Bing or Bard in areas of scientific accuracy, scientific detail and context. All AI tools performed consistently well at PG level compared to UG level, although only ChatGPT consistently met levels of high attainment at all UG levels. ChatGPT and Bing did not provide adequate references, while Bing falsified references. In conclusion, generative AI tools are useful for providing scientific information consistent with the academic standards required of students in written assignments. These findings have broad implications for the design, implementation and grading of written assessments in higher education.

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity concerns. Results show significant reductions in detector accuracy (17.4%) when faced with simple techniques to manipulate the AI generated content. The varying performances of GenAI tools and detectors indicate they cannot currently be recommended for determining academic integrity violations due to accuracy limitations and the potential for false accusation which undermines inclusive and fair assessment practices. However, these tools may support learning and academic integrity when used non-punitively. This study aims to guide educators and institutions in the critical implementation of AI text detectors in higher education, highlighting the importance of exploring alternatives to maintain inclusivity in the face of emerging technologies.

While test anxiety is a problem in asynchronous online courses, few studies have systematically investigated learning factors influencing test anxiety in asynchronous online courses. Additionally, emotional engagement has been identified as a mediator between learning factors and test anxiety. Therefore, this study clarified the mediating role of emotional engagement between learning factors (i.e., self-efficacy, instructor-learner interaction, learner-learner interaction, perceived ease-of-use, and perceived usefulness) and test anxiety in college-level asynchronous online courses. Overall, 316 college students participated in this study. Structural equation modeling analysis examined the relationships between learning factors and test anxiety. Self-efficacy, instructor-learner interaction, and perceived ease of use had direct and significant negative influences on test anxiety. Self-efficacy, instructor-learner interaction, learner-learner interaction and perceived usefulness had indirect negative effects on test anxiety mediated by emotional engagement. The current findings indicated that instructors should consider self-efficacy, instructor-learner interaction, learner-learner interaction, perceived ease of use, and perceived usefulness when designing and conducting asynchronous online courses to reduce college students’ test anxiety.

This research explores using blogs as an innovative assessment tool to enhance authentic learning and professional skill development in students. Unlike traditional methods, blogs foster active knowledge building and peer interaction, making learning more engaging and aligned with industry contexts. The study involved third-year planning students, in the course Governance and Planning, and utilised questionnaires, focus groups, and blog post analyses. Results indicated that blogs promoted reflective practice, facilitated peer review, and improved learning efficiencies. Despite some initial resistance and concerns about academic rigor, students found that blogging enhanced their understanding of course content and professional practices. Teacher support and peer feedback played a crucial role in this process. The literature supports blogs’ effectiveness in motivating students and aligning learning activities with real-world applications. However, assumptions about students’ familiarity with blogging were challenged, highlighting the need for thorough induction and support. Overall, blog-based assessments proved beneficial in creating authentic learning experiences and preparing students for their future careers. Future research should consider long-term studies on graduate outcomes and further explore peer review mechanisms.

Educational chatbots (EC) have shown their promise in providing instructional support. However, limited studies directly explored the impact of EC on learners’ emotional responses. This study investigated the induced emotions from interacting with micro-learning EC and how they impact learning motivation. In this context, the EC interactions encompassed greetings, biology learning content delivery, self-evaluation, and feedback. This study employed a between-subject experimental design involving 62 college students. Participants were randomly assigned to either the Metacognitive EC group, receiving metacognitive feedback, or the Neutral EC group, receiving neutral feedback. The results of T-tests demonstrated significant differences in specific induced emotions between the two groups while some similarities exist. Importantly, it unveiled that both Metacognitive EC and Neutral EC interactions evoked a spectrum of positive, negative, and ambivalent emotions, in which positive emotions surpassed the induced negative emotions. In general, metacognitive feedback induced fewer negative emotions than neutral feedback. PLS analysis supported the relationships between induced emotions and intrinsic motivation, with positive emotion, ambivalent emotions, and negative emotions influencing interest motivation, which, in turn, shaped other motivational components, including perceived competence, perceived value, and perceived pressure. However, the influence of positive emotion on interest was weaker in the Metacognitive than in the Neutral EC. In conclusion, the study revealed how induced emotions impact motivations and showed that the presence of metacognitive feedback reduced negative emotions and promoted motivation. These findings highlight the need for positive emotion element design and appropriate feedback that will impact learning motivations during educational chatbot interactions.

As technology continues to advance, the integration of generative artificial intelligence tools in various sectors, including education, has gained momentum. ChatGPT, an extensively recognized language model created by OpenAI, has gained significant importance, particularly in education. This study investigates the awareness, acceptance, and adoption of ChatGPT, a state-of-the-art language model developed by OpenAI, in higher education institutions across China. This study applies the partial least squares structural equation modeling (PLS-SEM) method for examining data collected from 320 Chinese university students. The study’s conceptual framework integrates key determinants from the Technology Acceptance Model (TAM) and extends it by incorporating perceived intelligence as a critical factor in the adoption process. The study findings reveal that ChatGPT awareness significantly influences the intention to adopt ChatGPT. Perceived ease of use, usefulness, and intelligence significantly mediate the association between ChatGPT awareness and adoption intention of ChatGPT. Additionally, perceived trust significantly moderates the relationship between ChatGPT awareness and perceived ease of use, usefulness, and intelligence. Moving forward, in order to maintain students’ critical thinking skills and inventiveness in their assessment writing, assessments must promote the safe use of ChatGPT. Therefore, educators will be crucial in ensuring that artificial intelligence tools are used in assessments ethically and suitably by providing clear guidelines and instructions.

This study explored the dynamics of students' knowledge co-construction in an asynchronous gamified environment in higher education, focusing on peer discussions in college business courses. Utilizing epistemic network analysis, sequence pattern mining, and automated coding, we analyzed the interactions of 1,319 business students. Our findings revealed that externalization and epistemic activity were prevalent, demonstrating a strong link between problem-solving and conceptual understanding. Three primary discussion types were observed: argumentative, epistemic, and social, each with unique patterns of engagement and idea integration. Effective knowledge co-construction patterns included open-ended questions with an epistemic focus, debates serving as intense knowledge co-construction arenas, and social interactions fostering a supportive and collaborative learning environment. The introduction of gamification elements led to increased student engagement and participation. Our findings emphasize the significance of structured analysis, collaboration, and argumentation in promoting effective knowledge co-construction in peer learning settings. This study offers insights into the temporal interplay of discourse dimensions and their potential for collaborative learning, enhancing our understanding of how learning analytics can be employed to discover ways in which students co-construct knowledge in asynchronous gamified environments.

This study aims to understand the dynamics and impact of emotional presence in a collaborative learning environment and its effects on the learning process and outcomes. Emotional presence, defined as the experience of emotion arising from cognitive appraisals in learner-environment interactions, encompasses four dimensions: interest-curiosity, emotional regulation, expression management, and emotional awareness. Using a mixed-methods approach, we surveyed 33 Japanese college students engaged in collaborative learning activities and conducted in-depth interviews with 10 participants to gather qualitative insights. The study revealed that emotional presence evolves throughout the collaborative process, with increased emotional regulation in response to complex tasks, reflecting learners’ adaptations to varying task demands. It highlighted the importance of emotional regulation in shaping the learning process and improving outcomes in collaborative learning. Our findings suggest that heightened emotional presence, with increased emotional awareness and regulation, fosters cognitive development and learner well-being, supporting more effective and fulfilling collaborative learning experiences. The study also emphasizes the crucial role of cognitive appraisal in shaping emotional experiences, significantly influencing outcomes in technology-enhanced environments. The study concludes by proposing future research directions, such as examining the longitudinal impacts of emotional presence and exploring interventions to enhance emotional regulation skills in diverse learning contexts.

In 2022, China’s Educational Ministry, for the first time, released an industry standard for teachers’ digital literacy. This standard provides a holistic framework for teachers’ digital literacy (TDL) in five dimensions. Since few studies have investigated EFL teachers’ digital literacy, the relationships among the five dimensions proposed for EFL teachers’ digital literacy remain unknown. Therefore, this research applied a quantitative method using a five-point Likert questionnaire designed based on TDL. Ninety-two EFL teachers from two universities and one higher vocational college in China participated in this questionnaire research. Partial least squares structural equation modeling was used to assess the relationships between these five dimensions. The relationships between the five TDL dimensions were highlighted by the findings, which supported the proposed model. In addition, implications for enhancing EFL teachers’ future digital literacy have been provided to facilitate and favor EFL teachers’ digital literacy development.