Analysis of Student Learning Willingness

This paper uses the real data recorded by the micro-assisted teaching platform in the daily teaching process of the course "Computer Systems" for students majoring in Software Engineering at Information Engineering School in 2021, 2022, and 2023. This can provide better guidance and reference for future actual teaching. Based on existing research, this paper selects 10 basic indicators listed in table I: score of class attendance rates (attend_score), average score of post-class assignments (hw_score), average score of group activities or discussion (discus_score), average score of classroom question answering (ans_score) as four student participation behavioral data indicators, and whether smart classrooms are being used for teaching (smart_cr), the frequency of using gaming teaching methods such as storytelling and video viewing (s&v_num), the frequency of using group activities or discussion (discus_num), the number of post-class assignments (hw_num), the frequency of classroom questions (ans_num) and the percentage of actual teacher explanations (explan_t) as the six indicators of multimodal teaching methods. Using these 10 indicators as input features, and classifying them into low / medium / high three categories of learning willing states (0, 1, 2) as output labels, to establish a predictive model.