Exploring the relationship between learning achievement of remote exam student-problem chart integrated with convolutional neural network and webcam eye-tracking trajectories

The COVID-19 pandemic has accelerated the development of remote assessment. This study has developed a "Remote Examination Platform" utilizing webcam eye-tracking technology to investigate respondents' hot zones and gaze trajectories during examinations. Moreover, convolutional neural network (CNN) are employed to identify image features related to respondents' behaviors, enabling the analysis of whether high and low achievers exhibit distinct hot zones and trajectories. Subsequently, a Student-Problem Chart (SP chart) is used to analyze the Caution Index for Problems (CP) to diagnose students' exam performance and detect item reactions. The results indicate that through CNN models identifying respondents' responses and eye-tracking trajectories, high-achieving students in items with abnormal attention indices demonstrate longer average dwell times and more frequent fixations compared to low achievers. Additionally, the CNN model achieves an accuracy rate of 96%. This study effectively predicts respondents' comprehension behaviors and serves as Taiwan's first artificial intelligence-based remote proctoring system (AIPS) for higher education institutions. It can assist educational institutions more efficiently in remote examinations, ensuring fairness and equity in assessments.