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.
Highlights
In recent years, with the rapid development of data mining in education, the combination of data mining and machine learning methods to analyze students' behavior data has become a popular trend
A general framework is proposed to discover students' learning performance based on their behavior patterns
The multi-task learning method is applied to model students' learning performance in multiple courses simultaneously
Summary
With the rapid development of data mining in education, the combination of data mining and machine learning methods to analyze students' behavior data has become a popular trend. It is mainly devoted to the prediction of students' learning performance, the discovery of future behavior & interest and the extraction of students' individual or group characteristics [1]. It is necessary to study the correlation between communication behavior patterns and students' performance in online learning. To explore the differences of behavior patterns among different performance groups, dedicated to find the most frequent sequence of activities and the impact of the sequence of activities on emotional states such as confusion and boredom [2]. The relationship between students' behavior, mental health and academic performance is studied by using the collected behaviors of students such as movement, sleep, dialogue and learning. A learning framework is constructed to model multiple heterogeneous behavior characteristics that automatically assess students with financial difficulties, solve the time-consuming shortcomings and the hidden dangers of fairness of traditional manual selection methods
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