Empowering Learning through Intelligent Data-Driven Systems

Abstract

The evolution of educational systems is closely tied to technological advancements, particularly the emergence of machine learning. This technology offers a sophisticated system capable of predicting, explaining, and influencing behavior. Many efforts have aimed to integrate machine learning into education, focusing on specific cases using ad-hoc models. This paper introduces an intelligent educational system that relies on data-driven student models, aiming to surpass the limitations of these ad-hoc systems. The approach outlined in this endeavor adopts a comprehensive and methodical modeling methodology centered on machine learning techniques. By employing Long Short-Term Memory (LSTM), the proposed approach enables predictive student models based on historical educational data. The effectiveness of this method was tested through experimentation on an intelligent tutoring system using 5-fold cross-validation, revealing that the smart educational system achieved a remarkable 96% accuracy rate. Furthermore, a comparison between the importance scores of features with and without the student models demonstrated the practicality and effectiveness of the proposed methodology.