Study on learning effect prediction models based on principal component analysis in MOOCs

Abstract

In recent years, the rise and development of massive open online courses (MOOCs) have promoted the boom of online education and also promoted the research on learning analysis and mining based on big data of education. However, while offering a large number of high quality courses, there is also a phenomenon that the overall learning effect is not ideal. How to make effective use of MOOCs for teaching activities poses urgent practical requirements for educators and researchers. MOOCs store massive learners’ learning behavior data, and mining these data is of great significance to learners’ learning effects. Due to the deviation caused by the correlation between many behavior indexes, the paper analyze the nine measurable performance indexes by principal component analysis (PCA), and get three principal components factors in order to reduce the computational dimension and increase the comprehensibility, and then obtain a model of learning effect prediction, namely the PCA prediction model (PCA_PM). Logistic regression algorithm has a good fitting effect on behavioral indexes. Therefore, the paper use the logistic regression method to construct another model of learning effect prediction, namely the principal component logistic regression prediction model (PCL_PM), and predictive effects of two models were compared and analyzed. The results show that it is an effective way to help learners to improve the passing rate of the course by establishing the predictive models. The prediction accuracy of both PCA_PM and PCL_PM is high, but overall, the prediction effect of PCA_PM is better.