Enhancing Student Academic Success Prediction Through Ensemble Learning and Image-Based Behavioral Data Transformation

Predicting student academic success is a significant task in the field of educational data analysis, offering insights for personalized learning interventions. However, the existing research faces challenges such as imbalanced datasets, inefficient feature transformation methods, and limited exploration data integration. This research introduces an innovative method for predicting student performance by transforming one-dimensional student online learning behavior data into two-dimensional images using four distinct text-to-image encoding methods: Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF). We evaluated the transformed images using CNN and FCN individually as well as an ensemble network, EnCF. Additionally, traditional machine learning methods, such as Random Forest, Naive Bayes, AdaBoost, Decision Tree, SVM, Logistic Regression, Extra Trees, K-Nearest Neighbors, Gradient Boosting, and Stochastic Gradient Descent, were employed on the raw, untransformed data with the SMOTE method for comparison. The experimental results demonstrated that the Recurrence Plot (RP) method outperformed other transformation techniques when using CNN and achieved the highest classification accuracy of 0.9528 under the EnCF ensemble framework. Furthermore, the deep learning approaches consistently achieved better results than traditional machine learning, underscoring the advantages of image-based data transformation combined with advanced ensemble learning approaches.