Empowering Learning through Intelligent Data-Driven Systems
Received: 26 November 2023 | Revised: 5 December 2023 | Accepted: 25 December 2023 | Online: 8 February 2024
Corresponding author: Khalid Abdullah Aldriwish
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.
Keywords:
machine learning, educational systems, CNN, historical dataDownloads
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