Deep learning-based model for predicting student learning behavior: A pathway to early intervention and enhanced outcomes

Abstract

In the realm of educational data science, the utilization of advanced analytical techniques is increasingly pivotal for optimizing pedagogical methods and implementing early interventions. This study introduces a predictive model employing deep learning technology for the analysis of student learning behavior. The model’s primary objective is to detect potential learning impediments at nascent stages, thereby facilitating timely and effective educational strategies to augment learning outcomes. Despite strides in behavioral prediction, challenges persist due to the dynamic and complex nature of educational data. Addressing these challenges, the proposed model integrates sequence pattern recognition with time-series analysis. The application of the PrefixSpan algorithm initiates the process, identifying sequential patterns in student learning behaviors and elucidating their temporal progression. Subsequently, an advanced ordered funnel analysis algorithm is employed, unveiling directional associations among diverse learning patterns. The final phase involves applying independent component analysis (ICA) to enhance the multi-layer long short-term memory (Multi-LSTM) network’s structure, thus enabling precise predictions of student outcomes in the context of early interventions. The results underscore the efficacy of deep learning in deciphering intricate behavioral patterns and underscore its potential in personalized educational interventions. This comprehensive approach demonstrates the model’s capacity to harness the intricacies of educational data, thereby contributing significantly to the field of personalized education and precise teaching methodologies. The innovative aspect of this study lies in the comprehensive application of the PrefixSpan algorithm, the ordered funnel analysis algorithm, and the Multi-LSTM network architecture. A holistic model for predicting student learning behavior is proposed, coupled with early interventions, which enables educators to better understand student learning conditions and implement effective measures to enhance student learning outcomes.