Interpretable Grade Prediction Based on ECOC and Shapley Theory

In recent years, blended learning models combining online and offline instructional methods have been widely implemented in higher education institutions. Online learning platforms accumulate a large number of fine-grained records of students' learning behavior, offering an excellent opportunity to analyze students' learning behavior. Firstly, a year's worth of student behavior log data was collected from the “Java Language Programming” blended learning course at a college. Secondly, we propose an interpretable grade prediction method based on the error correcting output code (ECOC) with Shapley theory. This approach employs Recursive Feature Elimination (RFE) to select features from the original features, and creates an ECOC classification matrix to predict student performance. Finally, the Shapley values for each feature were used to conduct an explainable analysis of the prediction results. Experimental results show that the proposed method outperforms other classical classification algorithms. This study provides a visual and educational analysis of the major factors affecting academic performance, thereby offering educational administrators more intuitive decision support.