Using AI-based NiCATS System to Evaluate Student Comprehension in Introductory Computer Programming Courses

This Research to Practice Full Paper presents the use of data collected by our Non-Intrusive Classroom Attention Tracking System (NiCATS) to evaluate student comprehension. Quantifying students' cognitive processes in classrooms in a non-intrusive way is challenging. By analyzing various aspects of the eye metrics against defined regions of interest (ROI), instructors can better understand students’ cognitive processes as they acquire new knowledge. Eye-tracking studies primarily define ROIs based on commonly used metrics (source code complexity, significant fixation durations, etc.). While helpful, these metrics, when used independently, do not accurately represent their comprehension patterns. This paper contributes an alternative, multilayered approach for calculating gaze metrics against automatically defined ROIs. The work utilizes the AI-based Non-Intrusive Classroom Attention Tracking System (NiCATS - developed by the researchers), collecting raw-gaze data in real-time as information is presented on a computer screen. This paper reports the results of a study in which undergraduate students in a CS programming course were asked to identify defects seeded in Java programs. Each JAVA program included its own unique sets of ROIS defined using two different granularities: lexer-based and line-based. The ROI sets were then used to calculate relevant eye metrics in the context of each ROI layout. The results of the eye metric analysis at specific ROIs w.r.t their code review task provide insights into the cognitive processes students undergo when trying to comprehend new material. Subdividing this region into lexer-based regions, we determined “content topics” students struggled with (e.g., using complex data types) in a specific area. This feedback is valuable to the instructor as it enables the ability to identify hard-to-comprehend content topics post-hoc and gives the ability to validate student learning in the classroom. While this experiment focused on students in introductory programming courses, we intend to conduct experiments in other learning settings where students are expected to read material on a computer screen or solve actual problems. To summarize, the analysis of these eye metrics using more fine-grained ROIs (lexer-based, line-based) as an extension of complexity-based ROIs provides instructors with deeper insights into the cognitive processes used by students when compared to the current state-of-the-art techniques.