A Data Mining Method for Students' Behavior Understanding

Abstract

To model students' behavior and describe their behavior characteristics accurately and comprehensively, a framework for predicting students' learning performance based on behavioral model is proposed, which extracts features from multiple perspectives to describe behaviors more comprehensively, including statistical features and association features. In addition, a multi-task model is designed for fine-grained prediction of students' learning performance in the curriculum. A framework for predicting mastery based on online learning behavior is also put forward. Additional context information is added to the collaborative filtering algorithm, including student-knowledge-point mastery and class-knowledge-point, and students' mastery is predicted according to the learning path excavated. Considering the time-varying of mastery, the approximate curve of students' mastery of knowledge points is fitted according to the Ebinhaus forgetting curve. The experiments show that the proposed framework has a high recall rate for the prediction of learning performance, and also shows a certain practicability for early warning. Further, based on the model, the correlation between student behavior patterns and learning performance is discussed. The addition of additional information has improved the prediction efficiency, especially the operational efficiency. At the same time, the proposed framework can not only dynamically assess students' master of knowledge, but also facilitate the system to review feedback or adjust the learning order, and provide personalized learning services.