Empowering Learning through Intelligent Data-Driven Systems

Authors

  • Khalid Abdullah Aldriwish Computer Science Department, College of Computer and Information Sciences, Majmaah University, Saudi Arabia
Volume: 14 | Issue: 1 | Pages: 12844-12849 | February 2024 | https://doi.org/10.48084/etasr.6675

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 data

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How to Cite

[1]
K. A. Aldriwish, “Empowering Learning through Intelligent Data-Driven Systems”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12844–12849, Feb. 2024.

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