Student Behavior Analysis and Performance Prediction Based on Blended Learning Data

Blended teaching has the characteristics of small scale, strong controllability, definite learning tasks and consideration of both online and offline teaching. The quantitative evaluation indicators of learners' blended learning behavior enthusiasm and stability are proposed, and then used for learning behavior analysis and performance prediction. It analyzes the distribution, correlation, consistency and effectiveness of online and offline learning behavior indicators, and it is found that there is a high correlation between learning behavior indicators and the final grade. The prediction is carried on the data set composed of learning behavior indicators, students' basic information, online and offline learning data. The improved forest optimization algorithm is applied to select features. The naive B ayes, decision tree and random forest classifier are used to predict the final performance. The experiments show that the learning behavior indicators can effectively reduce the scale of feature set and improve the performance prediction effect.