基于主成分分析的在线学习者评价模型构建研究

Construction of Online Learner Evaluation Model Based on Principal Component Analysis

在大数据技术迅速发展的今天,利用海量的学习数据对学习者的学习过程进行数据分析、建模和解读是在线教育研究的热点和难点问题。文章提出了一种使用主成分分析对实时学习数据进行分析,最终得到对学习者评价的方法。本文通过使用大数据量的样本分析,提出了包括行为数量、行为得分、考试得分等三类共计11个指标,使用主成分分析法将这些指标降维,得到5个表征学习过程的主成分,然后使用主成分回归对5个主成分进行回归分析,最终得到对学习者的评价模型。结果显示模型能准确评估学生学习效果,并已应用于实际系统。

Today,with the rapid development of big data technology,it is a hot and difficult issue of on-line education research to use massive learning data to analyze,model and interpret the learning process.This paper proposes a method of using principal component analysis to analyze real-time learning data and finally get the evaluation of learners.This paper analyzes large amounts of data samples and proposes a total of 11 indicators,including behavior quantity,behavior score and examination score,and the principal component analysis method is used to reduce the dimensions of these indicators to 5 principal components representing the learning process,then the principal component regression method is used to perform regression analysis on the 5 principal components,and finally the student’s evaluation model can be obtained.The results show that the model can assess students’learning effects accurately,and has been applied to the actual system.