Network & Modern Education Technique Center, Guangzhou University, Guangzhou 510006, P.R.China.

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Abstract—In this paper, we explore the characteristics of teaching quality evaluation of our university. Based on an effective dataset from teacher evaluation system, we apply data mining (DM) technique to teaching quality evaluation system and give related case analysis. The analyzed results show there exists the hidden relationship between the teacher’s attributes, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. In addition, we reveal how the course’s attributes, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. The mined rules are basically accorded with the reform of educational system made recently in our university, thus providing effective guidance to talent development, teaching management, and input of talent in higher education system.

Index Terms—Data mining, teaching evaluation, decision tree, association rule.

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Cite: Aifeng Li, Kui Liu, Zhiyou Ge, "Application of Data Mining in the Colleges’ in-Class Teaching Quality Evaluation System," Journal of Computers vol. 10, no. 3, pp. 166-175, 2015.