基于大数据挖掘与文本评价的线上教学质量评估

Online teaching quality evaluation based on big data mining and text evaluation

针对线上教学质量难以精准定量评估的问题,提出一种线上教学质量评估系统。通过数据预处理、词向量训练、模型训练与测试以及特征提取四个步骤提取评价文本特征,挖掘海量评价文本特征的关联规则,以确定线上教学质量评估指标;利用PCA算法建立协方差矩阵,计算评估指标的特征值和贡献率,确定指标特征值的主成分数量和载荷,并加权平均主成分系数,建立线上教学质量评估模型。实验结果表明,该模型可利用评价文本定量评估线上教学质量,评估精度与评估召回率均高于98%,可实现线上教学质量的客观评价。

Based on the problem that online teaching quality cannot be quantitatively evaluated,an online teaching quality assessment system is proposed.Through four steps including,data preprocessing,word vector training,model training and testing,feature extraction,the evaluation text features are extracted,and the association rules of massive evaluation text features are mined to determine the online teaching quality evaluation index.The PCA algorithm is used to establish the covariance matrix,calculate the eigenvalue and contribution rate of the evaluation index,determine the principal component and load of the eigenvalue of the index,and weight the average principal component coefficient to establish the online teaching quality evaluation model.The experiment results show that the model can quantitatively evaluate the quality of online teaching by using evaluation text,and the evaluation accuracy and recall rate are higher than 98%,which can realize the objective evaluation of online teaching quality.