Performance Prediction Based on Analysis of Learning Behavior

Abstract

With the rise of large-scale online open courses, the era of MOOC has come and it has created opportunities and challenges for higher education at home and abroad. Many colleges and universities use the combination of MOOC and classroom teaching. Through online and offline synchronization courses, the mixed teaching based on MOOC is formed. A series of learning behavior data has been generated when students participate in the online MOOC system. The resulting data facilitates learning analysis to improve teaching quality and improve learning behavior. This paper collects the learning behavior data from MOOC, uses the Pearson coefficient to select the learning behavior characteristics related to the learning effect, and establishes a learning performance classification model based on the Support Vector Machine (SVM), and predicts the learning performance according to the learning behavior data. The accuracy of the performance forecast was 95.26%.