Predicting Student Engagement Characteristics Using a Multi-Instance Localization Approach with a Gradient-Boosted Deep LSTM Classifier

The prediction of student engagement characteristics involves forecasting and analyzing student interaction with educational materials using engagement prediction models. This process encompasses the prediction of cognitive, behavioral, and emotional dimensions of engagement. The existing student engagement prediction models have some limitations, including poor convergence, less generalizability, complexity issues, overfitting, false errors, and limited resources. Hence, the research proposes the Multi-Instance Localization-based Gradient Boosted Long Short-Term Memory (MIL-GBLTM) model to tackle the challenge of predicting student engagement characteristics in online classes. The integration of effective MIL with a Triplet Attention mechanism focuses on the significant features that help with engagement prediction; LSTM layers capture intricate sequential patterns, and fractional gradient boosting is used for fine-tuning for accurate prediction, alongside ensemble-based learning. The LSTM layers with the Triplet Attention module refine temporal attention, and Fractional Gradient Boosting ensures the model’s adaptability and robustness. By combining these components, the proposed model is able to predict accurate student engagement with high convergence. This integrated approach enhances the capabilities of engagement prediction models in educational contexts, facilitating more effective interventions and personalized student support in online learning environments. Experimental results demonstrate that the proposed MIL-GBLTM model outperforms other existing models by achieving the highest accuracy of 96.55% with a k-fold of 10, utilizing the wacv2016 dataset.