| Data Mining refers to the process of searching and revealing implicit, previously unknownand potentially valuable information from the large amount of data in a database by means ofsome algorithm[1]. At present, multitudinous domains are carrying on the data mining appliedresearch.Teaching appraisal of Hainan College of Software Technology (hereafter as HNSCT)primarily depends on students’assessment, that is, thescores given by thestudents. In this processof data evaluation, what our college managers do is simply to collect statistics, so what thedepartments receive are just the final scores and the data like Excellent Rate, Good Rate and ThePass Rate. But how to analyze these scores and data deeply so as to make the teaching appraisalplay a greater role is a subject worthy of study.Taking HNSCT as an example, the present study presents a cluster analysis of classevaluation behavior by using data mining techniques. By establishing the teaching evaluationmodel through the model based on the relation between teaching evaluation data and students’achievement the factors of determining the quality of teaching are revealed. The author appliesassociation rules in-depth data mining to find out the key impact factor that determines the qualityso as to find a strong theoretical and practical basis to improve the teaching quality. The paperapplies the cause and effect to deep mine the data on teaching appraisal grades to verify theassumption. Through the original data analysis, using probability weights analys is the authorrefines the teaching quality evaluation index, accomplishes the quantitative evaluation of thequality of classroom teaching evaluation and constructs the evaluation index system of the qualityof classroom teaching, which indicates this classroom teaching quality evaluation mechanismeffective. |
