International Journal of Educational Technology in Higher Education最新文献

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.

ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners’ programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students’ programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students’ programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students’ perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT.

The shift toward electronic learning due to the COVID-19 pandemic has created many opportunities to shape Oman’s learning styles. This study explores the factors that affect students’ acceptance of blended learning (BL) in higher education institutions in developing countries, focusing on Oman. The study examines the impact of demographic and social factors, attitude, subjective norms, perceived behavioral control, self-efficacy, beliefs, behavioral intention, and actual use of BL among students. The Theory of Planned Behavior (TPB) was used as a theoretical framework to understand the decision-making processes surrounding BL adoption. Hypotheses are formulated and tested using statistical analysis of survey results. The questionnaire was distributed to students from Sultan Qaboos University in Oman. The data collected were analyzed using inferential predictive modeling methods such as multiple regression analysis and Pearson correlation. The findings indicate that students have a positive attitude toward BL and are likely to choose it in the future. The study also reveals that demographic characteristics and various dimensions, such as attitude, subjective norms, perceived behavioral control, self-efficacy, beliefs, behavioral intention, and actual usage, influence students’ acceptance and utilization of BL. The results contribute to the existing literature and provide insights into the factors that affect BL adoption in developing countries.

While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N = 165). Study 2 used a three-wave time-lagged design to collect data from university students (N = 494) to further validate the scale and test the study’s hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage on students’ levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast, students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students’ academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students’ outcomes through ChatGPT usage.

This article situates the potential for intellectual work to be renewed through an enriched engagement with the relationship between indigenous protocols and artificial intelligence (AI). It situates this through a dialectical storytelling of the contradictions that emerge from the relationships between humans and capitalist technologies, played out within higher education. It argues that these have ramifications for our conceptions of AI, and its ways of knowing, doing and being within wider ecosystems. In thinking about how technology reinforces social production inside capitalist institutions like universities, the article seeks to refocus our storytelling around mass intellectuality and generative possibilities for transcending alienating social relations. In so doing, the focus shifts to the potential for weaving new protocols, from existing material and historical experiences of technology, which unfold structurally, culturally and practically within communities. At the heart of this lies the question, what does it mean to live? In a world described against polycrisis, is it possible to tell new social science fictions, as departures towards a new mode of higher learning and intellectual work that seeks to negate, abolish and transcend the world as-is?