International Journal of Educational Technology in Higher Education最新文献

This paper combines social fiction and academic analysis to envision hopeful futures for higher education. At the heart of the exploration is Phoebe Wagner’s speculative fiction piece, University, Speaking, which personifies a university grappling with environmental, political, and social change. Phoebe Wagner’s first-person narrative highlights the power of collective voice, the importance of centering community, and the urgent need to cultivate resilience and adaptability. Through analysis of key themes, this paper connects Phoebe Wagner’s fictional vision to contemporary research on the multi-faceted and complex challenges facing universities today. By integrating artistic and academic perspectives, this paper discusses new possibilities for universities navigating disruption and change.

Recently, the number of online courses provided for university students around the world has increased substantially. Instruction is delivered most commonly through either asynchronous video lectures (pre-recorded videos with no real-time communication) or synchronous video lectures (live-streamed videos with real-time communication). From a learning perspective, it is important to capture the interest of learners based on specific situational aspects of both asynchronous and synchronous video lectures, and situational interest of learners may vary depending on the overall intrinsic qualities of each modality. Additionally, there may be variations in regards to the way in which self-efficacy interacts with situational interest depending on which modality is used. This study examines survey responses of university students (n = 93) in South Korea to determine if students perceive differences in situational interest between two different conditions: asynchronous video lectures and synchronous video lectures. Additionally, the difference between situational interest levels of learners with low self-efficacy and learners with high self-efficacy within each condition was examined. The main results showed no overall difference in situational interest between the two conditions and no difference in situational interest between learners with low self-efficacy and learners with high self-efficacy in the synchronous video lecture condition. However, there was a significant difference in situational interest found between learners with low self-efficacy and learners with high self-efficacy in the asynchronous video lecture condition. Results are explained through the differing effects of self-efficacy on situational interest based on the amount of autonomy provided in online learning environments.

Wayfinding, which is a part of learning in connectivist learning, involves consolidating a wide variety of resources and information and building connections among them. However, learners often encounter difficulties in wayfinding, and are lost without technological support in connectivist learning. This study examined the wayfinding processes occurring within a network of learners in a personal social knowledge network (PSKN), explored differences in behavior patterns between high and low performers in PSKN. The results reveal the diversity and complexity of wayfinding in a PSKN, including finding and connecting nodes, forming cognitive maps, finding and filtering information, and creating new nodes. Moreover, the characteristics of wayfinding in the PSKN differed across participants, and high- and low-performing participants demonstrated different and unique wayfinding behavioral patterns, which provided a basis for comprehensive analyses of wayfinding. These findings can be used to provide instructional support and network navigation in connectivist learning for learners at various performance levels. The proposed PSKN shows promise in facilitate wayfinding including finding nodes and connecting nodes, as well as relations between knowledge nodes and the course base demonstrated by PSKN, providing great convenience for learners to form cognitive maps based on the node sequence. Compared with current studies, this research focuses on diversified interaction data and resource behavior rather than teaching videos and quizzes or exercises as the main resources and considering that course and technological factors influence the ways in which learners access resources in connectivist learning.

The increasing prevalence of Fourth Industrial Revolution (4IR) technologies has led to a surge in the popularity of AI application tools, particularly chatbots, in various fields, including education. This research explores the factors influencing undergraduate students' inclination to embrace AI application tools, specifically chatbots, for educational purposes. Using an expanded diffusion theory of innovation framework, the study investigates the relationship between relative advantages, compatibility, trialability, perceived trust, perceived usefulness, perceived ease of use, and behavioral intention. Using a 7-point scale, a questionnaire was given to 842 undergraduate students to collect data. The analysis, conducted using SmartPLS 4.0.9.2 software with a covariance-based structural equation model, produced significant findings. The study confirms hypotheses related to the relative advantages, compatibility, trialability, perceived usefulness, and perceived trust associated with chatbots. Notably, students who perceive the benefits of chatbots show a strong intention to use them for academic purposes. The perception of compatibility between students and chatbots positively influences adoption intention, highlighting the importance of compatibility. Additionally, students who have the opportunity to trial chatbots are more likely to use them, emphasizing the significance of trialability. Interestingly, the study did not establish direct relationships between perceived usefulness, perceived ease of use, and behavioral intention. This suggests the presence of other influential factors or dynamics in the adoption of chatbots for educational purposes. These findings offer practical insights for students and contribute to the theoretical understanding of the diffusion theory of innovation. Future research can further explore these insights to unravel the complexities of chatbot adoption and facilitate the broader adoption of AI tools in educational settings.

Abstract

The influence of Artificial Intelligence on higher education is increasing. As important drivers for student retention and learning success, generative AI-tools like translators, paraphrasers and most lately chatbots can support students in their learning processes. The perceptions and expectations of first-years students related to AI-tools have not yet been researched in-depth. The same can be stated about necessary requirements and skills for the purposeful use of AI-tools. The research work examines the relationship between first-year students’ knowledge, skills and attitudes and their use of AI-tools for their learning processes. Analysing the data of 634 first-year students revealed that attitudes towards AI significantly explains the intended use of AI tools. Additionally, the perceived benefits of AI-technology are predictors for students’ perception of AI-robots as cooperation partners for humans. Educators in higher education must facilitate students’ AI competencies and integrate AI-tools into instructional designs. As a result, students learning processes will be improved.

Abstract

The increased availability of technology in higher education has led to the growth of online learning platforms. However, a significant concern exists regarding dropout rates in online higher education (OHE). In this ever-evolving landscape, student attrition poses a complex challenge that demands careful investigation. This systematic literature review presents a comprehensive analysis of the literature to uncover the reasons behind dropout rates in virtual learning environments. Following the PRISMA guidelines, this study systematically identifies and elucidates the risk factors associated with dropout in online higher education. The selection process encompassed articles published between 2013 and June 2023, resulting in the inclusion of 110 relevant articles that significantly contribute to the discourse in this field. We examine demographic, course-related, technology-related, motivational, and support-related aspects that shape students’ decisions in online learning programs. The review highlights key contributors to dropout like the quality of the course, academic preparation, student satisfaction, learner motivation, system attributes, and support services. Conversely, health concerns, financial limitations, technological issues, screen fatigue, isolation, and academic workload, emerge as significant limitations reported by online learners. These insights offer a holistic understanding of dropout dynamics, guiding the development of targeted interventions and strategies to enhance the quality and effectiveness of online education.

Integrating digital technologies to benefit teaching and learning has long been driving higher education. The uptake of technology has been supported by teacher training focused on developing teachers’ capabilities to design for learning. However, in this paper, we raise the point of moving towards teaching-sensitive technology as a clear alternative to current strategies focusing on teachers’ mental processes. To develop this point, the paper offers a qualitative study that explores teaching to identify critical features of technology supporting teachers’ work. Analysing teaching from a hermeneutic perspective, we arrive at six fundamental dynamics within which teachers operate. Based on the factors identified, we present three principles to guide future design of technologies for teaching and two approaches to designing technology sensitive to teachers’ values.

Abstract

In the evolving landscape of higher education, challenges such as the COVID-19 pandemic have underscored the necessity for innovative teaching methodologies. These challenges have catalyzed the integration of technology into education, particularly in blended learning environments, to bolster self-regulated learning (SRL) and higher-order thinking skills (HOTS). However, increased autonomy in blended learning can lead to learning disruptions if issues are not promptly addressed. In this context, OpenAI's ChatGPT, known for its extensive knowledge base and immediate feedback capability, emerges as a significant educational resource. Nonetheless, there are concerns that students might become excessively dependent on such tools, potentially hindering their development of HOTS. To address these concerns, this study introduces the Guidance-based ChatGPT-assisted Learning Aid (GCLA). This approach modifies the use of ChatGPT in educational settings by encouraging students to attempt problem-solving independently before seeking ChatGPT assistance. When engaged, the GCLA provides guidance through hints rather than direct answers, fostering an environment conducive to the development of SRL and HOTS. A randomized controlled trial (RCT) was employed to examine the impact of the GCLA compared to traditional ChatGPT use in a foundational chemistry course within a blended learning setting. This study involved 61 undergraduate students from a university in Taiwan. The findings reveal that the GCLA enhances SRL, HOTS, and knowledge construction compared to traditional ChatGPT use. These results directly align with the research objective to improve learning outcomes through providing guidance rather than answers by ChatGPT. In conclusion, the introduction of the GCLA has not only facilitated more effective learning experiences in blended learning environments but also ensured that students engage more actively in their educational journey. The implications of this study highlight the potential of ChatGPT-based tools in enhancing the quality of higher education, particularly in fostering essential skills such as self-regulation and HOTS. Furthermore, this research offers insights regarding the more effective use of ChatGPT in education.

The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.