Keyword: physics education

ABSTRACT: Motivation drives students' effort, persistence, and deep learning, while empathy underpins trust, inclusion, and emotional safety in diverse classrooms. In undergraduate physics education, where courses are often perceived as difficult, selective, and abstract, understanding how teachers’ and students’ motivation and empathy are related is crucial for creating supportive learning environments. This study explores how lecturers and students in undergraduate physics make sense of the interrelationships between teachers’ and students’ motivation and empathy. This study employed a qualitative exploratory design. Semi-structured interviews were conducted with physics education lecturers and undergraduate physics students. Lecturers were invited to describe their teaching motivation, their empathy towards students, and how they perceived these qualities as influencing students’ learning and well-being. Students were asked to describe their learning motivation and empathy and to reflect on how they experienced their lecturers’ motivation and empathy in everyday classroom interactions. The interview data were analysed inductively using thematic procedures to identify recurring patterns and relational dynamics between teachers’ and students’ motivation and empathy. The analysis reveals that lecturers who describe themselves as intrinsically motivated and caring tend to recount practices such as adapting explanations, providing emotional support, and giving individual attention, which students experience as motivating and affirming. Students often link their own motivation in physics to whether they perceive their lecturers as enthusiastic, approachable, and empathetic, particularly when facing conceptual difficulties. At the same time, some accounts suggest that tensions can arise when strong academic expectations and performance pressures coexist with limited emotional resources, complicating the enactment and experience of motivation and empathy. Overall, the findings illuminate how lecturers’ motivational and empathic stances are perceived to shape students’ motivation and empathy in undergraduate physics, highlighting the need for pedagogical approaches that intentionally cultivate both.

ABSTRACT: Enhancing students' academic performance in higher education is a primary goal, necessitating a systematic and adaptable approach. Traditional methods for analyzing student performance data often struggle with complexity and unpredictability, making them inadequate for handling intricate educational patterns. As a result, the development of fuzzy logic-based decision-making systems has become increasingly important. This study aims to design an accurate fuzzy logic system for predicting first-year physics students' academic achievement at Dire Dawa University. The model leverages the fuzzy library, defining membership functions for participation in experiments and discussions (categorized as low, medium, and high). It then establishes rules to map participation levels to predicted grades (poor, average, good, and excellent). The analysis involved applying the fuzzy logic system to a dataset and validating the predictions against actual grades. The findings revealed that the model accurately predicted grades for medium participation (e.g., 50.00 for 50% participation) but tended to overestimate high participation levels (e.g., predicting 85.00 when the actual score was 67.51). This research contributes to educational technology by providing a flexible predictive tool that can be expanded to other STEM disciplines, enhancing data-driven decision-making in academic settings. These results demonstrate the effectiveness of fuzzy logic in managing educational uncertainties, though refinements are needed to address overestimation and incorporate additional variables such as study habits and prior knowledge.