基于稀疏凸非负矩阵分解的混合数据特征提取与评价研究

Research on Feature Extraction and Evaluation of Mixed Data Based on Sparse Convex Non-Negative Matrix Factorization

针对目前缺乏对在线教学和传统课堂的混合数据融合分析,提出一种改进的凸非负矩阵分解特征提取算法,可有效提取学生学习行为数据的特征群集。根据群集特征的权值大小,依次选取多级特征指标,构建评价层、群集层、特征层3个层次上的PSR评价指标体系。依据评价指标体系采用综合加权法计算学生个体的质量评价值,对个体进行分级,分级结果与学生期末考试成绩分级分布基本一致,且符合正态分布,证明了特征提取方法及评价分级模型的有效性。

Aiming at the lack of fusion analysis on the mixed data of online teaching and traditional classroom,the improved sparse convex non-negative matrix factorization algorithm was used to effectively extracting the feature clusters of student learning behavior data.Based on the weights of cluster features,multi-level feature indicators were selected in turn to construct a PSR evaluation index system on three levels of evaluation layer,cluster layer and feature layer.According to the evaluation index system,the quality value of individuals was calculated based on the comprehensive weighting method,and students were graded.The grading results were 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.