Classification of Programming Logic Understanding Level Using Mouse Tracking Heatmaps and Machine Learning Techniques

Programming skill is one of the essential basic experience that each student in the field of computer science has to acquire. To potentially train all students such a skill, teachers should know every student understanding level during the practice of a programming for individually supporting. Conducting a test is a common method to classify the understanding level of the students. However, it would be a heavy burden for teachers and the student levels are known after the test. The purpose of our study is to classify the understanding level of programming during the practice. In this study, we focus on a block coding learning platform, and we propose a classification method by using mouse tracking heatmaps and machine learning techniques. As a first step of the study, we conduct a test with 18 participants. The results had shown that using mouse click heatmap image and decision tree algorithm was observed to classify students based on their programming logic understanding level through activity on a block coding learning platform. In our future work, we will increase the accuracy of classification and develop a model that can classify the understanding levels almost in real time of during programming practice.