PREDICTING STUDENT MOTIVATION AND ENGAGEMENT THROUGH MACHINE LEARNING MODELS

Abstract

Student motivation and engagement are pivotal yet latent constructs that benefit from timely, data-driven prediction to inform proactive support in higher education. This paper presents a concise synthesis of machine learning approaches for predicting multi-dimensional engagement (behavioral, emotional, cognitive) and academic motivation (intrinsic, extrinsic), bridging theory with deployable practice. We outline common data sources—learning management system (LMS) interaction logs, assessment trajectories, attendance and academic records, and psychometric instruments—and emphasize feature engineering for temporal dynamics, interaction patterns, effort proxies, and context transfer across courses. The modeling landscape spans interpretable classifiers (logistic regression, decision trees, random forests, gradient boosting), kernel methods (support vector machines), and deep learning architectures for sequential signals (RNN/LSTM, temporal CNN, transformers), with growing interest in multimodal fusion and representation learning. Evidence indicates that temporal and interaction features substantially improve early-warning performance, while generalization benefits from course-agnostic features, calibration, and domain adaptation. It is believed that this paper will help future researchers to gain insight about the said domain.