Abstract:

The utilization of eye-tracking technology in real-life classroom environments represents a recent innovative practice. This technology has begun to be employed for the analysis of teachers' attentional patterns in authentic classroom settings. Studies have revealed distinct gaze behaviors between experienced and novice teachers, with experienced educators demonstrating a more even distribution of attention among students compared to their less experienced counterparts. However, while these studies have made significant contributions to the literature, there remains a gap indicating the necessity for further research to comprehensively understand the dynamics of higher education classrooms. Moreover, research on assessing and improving faculty members' classroom management skills, which is crucial for enhancing the quality of teaching in higher education and supporting their professional development, is also limited. Therefore, to address these gaps, this study aims to examine faculty members' gaze distributions in the classroom and determine attention distributions in their lectures.

This study adopts a multiple case study approach. Three different classroom situations were examined, and the gaze distributions of faculty members teaching in each situation were analyzed. Each classroom had unique features such as the level of technology use, instructors' traits, and class size, which allows for the determination of instructors’ attentional behaviors in diverse settings. The first case was a Physics course given in a large-sized classroom, the second one was a computer science course given in a middle-sized classroom, and the final case was was an educational science course given in middle sized classroom as well.

Data were collected and analyzed using wearable eye-tracking devices over four consecutive weeks. At the end of each data collection session, faculty members were informed about their attentional distribution performance using heat maps that visually mapped eye-movement data of faculty members on classroom photos. To analyze the data, faculty members’ eye movements were manually mapped, and then the Gini coefficient, also known as the inequality coefficient, was calculated for each case. The Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality. Additionally, semi-structured interviews with faculty members were conducted to gather their views on their attentional distribution performance and the use of eye-tracking technology for evaluating teaching performance in higher education

The study findings indicate varying unequal gaze distribution among participating faculty members in the classroom, which can be interpreted as faculty members distributing their attention unequally among students. On the other hand, the study also revealed that their gaze distribution performance could be enhanced with feedback. Interviews with participating faculty members, who were previously unaware of the inequality in gaze distribution, led to the conclusion that such studies could increase their awareness. In conclusion, mobile eye-tracker technology can be used as a feedback tool to improve faculty members’ attentional distribution performance and provide a better teaching experience.

Keywords:

Eye-tracking, classroom ecology, gaze distribution, higher education, classroom management.