Research of Cluster Feature Extraction and Evaluation System Construction for Mixed Teaching Data

Abstract

At present, the mining and analysis of teaching data is mainly aimed at the online courses data, but not mixed data, which is fused by the traditional offline-classroom and online teaching data. Meanwhile, the most evaluation models are constructed by the learning data to evaluate the teaching quality of teachers, but not to evaluate and grade the individual quality of students. In fact, the evaluation and grading of students' quality can effectively provide more targeted teaching intervention for students of different levels based on the data analysis. To address these issues, the online teaching data is fused by the students' learning behavior data of traditional course to form the mixed data in this paper, and then the sparse non-negative matrix factorization (SNMF) method is adopted to extract the feature clusters of mixed learning data. According to the weights of the extracted cluster features, the multi-level feature indicators are selected in turn to construct the hierarchical evaluation index system. Finally, the comprehensive weighting method is adopted to evaluate and grade the individual students. In this paper, the mixed teaching data of computer basic course of our school is formed, and then the weights of feature clusters are calculated by SNMF and an evaluation model is established to evaluate and grade the students. The grading results are in accordance with the normal distribution and basically consistent with the grading distribution of students' final examination scores. Thus the validity of the model and method proposed in this paper is proved.