Large Language Models (LLMs), such as ChatGPT, Claude, and Gemini, have been widely adopted, significantly influencing various domains of social life, particularly education. This work highlights the importance of evaluating students' cognitive processes of learning-such as information gathering, decision-making, and assumption formation-when interacting with LLMs. We propose a BERT-based method using Bloom's Taxonomy to analyze students' task prompts and gain insights into their reasoning and problem-solving. Analysis of 48 students over a 16-week Python Programming course at a national university in northern Taiwan reveals a preference for higher-order cognitive skills. Application prompts comprise 32% of total prompts, compared to 12% for Knowledge. DeBERTa achieved the highest accuracy (75%) and F1 score (0.729)., though error rates were higher for complex tasks like Synthesis (0.44) than for Knowledge (0.13). These findings demonstrate the potential of LLMs to enhance critical thinking and support targeted educational interventions. Our findings indicate that LLMs facilitate content mastery and enhance awareness of how students approach educational tasks. By incorporating data from LLM interactions into assessment practices., educators can better understand learners” competencies., enabling targeted interventions to develop critical thinking skills. This approach provides a foundation for more robust pedagogical frameworks., ensuring students' growth through dynamic and reflective learning experiences.
Pedagogical Conversational Agents (PCAs) are increasingly used to provide personalized support in digital learning. This study examines how different levels of learner control that a virtual teaching assistant (TA) offered impact students' self-efficacy, learning effectiveness, and satisfaction, given the moderating effects of student characteristics. A between-subjects study with 215 students across three learner control conditions revealed that the moderate control (i.e., Assisted Inquiry) led to higher perceived learning effectiveness than the high control. Agreeableness, Extraversion, and Openness were found to significantly influence students' learning. These findings underscore the need for adaptive PCA systems that tailor instructional support with scaffolding and balanced learning interventions while accounting for learners' personality traits to enhance student engagement and perceived learning effectiveness.
In recent years, namely due to its association with positive academic outcomes, the concept of ‘student engagement’ has received a growing interest from researchers, practitioners, and policymakers. As student engagement received strong attention due to its association with positive academic outcomes, research on the costs and benefits of technology use has also become increasingly popular. This paper presents an exploratory literature review aiming to map how student engagement is being assessed in TEL environments, highlighting the interplay between self-reported experiences, learning analytics, and ethical considerations. The potential benefits and risks associated with engagement measurement in TEL are also addressed. The review identifies key gaps in current research and suggests directions for future exploration, particularly at the intersection of student engagement, TEL, digital well-being, and ethics.
In this study, we examined the effects of a 1.5-day microelectronics training program on students' self-efficacy. Using a quasi-experimental design (N = 27), we analyzed the data through a paired t-test complemented by qualitative insights. The findings revealed that: (1) a short-term microelectronics training program, designed based on the Attention, Relevance, Confidence, and Satisfaction (ARCS) motivational model, can positively influence learners' self-efficacy toward STEM education; and (2) incorporating diverse instructional strategies-such as guest talks from industry and academic experts, along with hands-on, real-world experiences-is essential for making abstract textbook concepts more concrete and inspiring students to pursue STEM-related careers.
This study investigates iVRLab, an immersive virtual reality (VR) microfabrication training system, and its influence on college students' self-efficacy and interest in microelectronics during a one-hour workshop held within a 1.5-day training camp. iVRLab simulates photolithography cleanroom operations, providing hands-on virtual experiences that merge theory with practice. A pre-post analysis revealed a significant rise in self-efficacy (M = 4.11 to 4.37, p = 0.0066), demonstrating the system's effectiveness in building confidence. Although interest only showed slight gains, high baseline scores (3.6-3.7 on a 4-point scale) indicate a ceiling effect. Correlations among engagement, embodiment (r = 0.91, p < 0.001), and immersion (r = 0.60, p < 0.01) underscore the value of active, embodied VR learning. Qualitative feedback further highlighted iVRLab's immersive realism and its capacity to spark curiosity about microfabrication careers. These findings suggest that VR training can effectively boost self-efficacy in microelectronics, reinforcing students' enthusiasm in an informal learning setting and indicating the potential broader adoption across STEM fields.
