Abstract
—In this paper, we employ data mining (DM) technique to analyze various potential factors which impact the in-class teaching quality evaluation. Based on an effective dataset, we first exploit association rule method to mine the relationship between the teacher’s attributions, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. Then, we construct the decision tree of course’s attributions to reveal how the course’s attributions, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. Our mined rules can provide effective guidance to talent development, teaching management, and input of talent in higher education system. Index Terms—data mining, decision tree, association rule, teaching quality evaluation
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