Empowering Learning through Intelligent Data-Driven Systems


  • 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


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.


machine learning, educational systems, CNN, historical data


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M. Laaziri, S. Khoulji, K. Benmoussa, and K. M. Larbi, "Outlining an Intelligent Tutoring System for a University Cooperation Information System," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3427–3431, Oct. 2018.

F. D. Pereira et al., "Using learning analytics in the Amazonas: understanding students’ behaviour in introductory programming," British Journal of Educational Technology, vol. 51, no. 4, pp. 955–972, 2020.

I. Saric-Grgic, A. Grubisic, S. Stankov, and M. Stula, "An agent-based intelligent tutoring systems review," International Journal of Learning Technology, vol. 14, no. 2, pp. 125–140, Jan. 2019.

A. Alkhatlan and J. Kalita, "Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments," arXiv e-prints. Dec. 01, 2018.

H. Mohamed and M. Lamia, "Implementing flipped classroom that used an intelligent tutoring system into learning process," Computers & Education, vol. 124, pp. 62–76, Sep. 2018.

L. Yuan et al., "Iterative Teacher-Aware Learning," in 35th Conference on Neural Information Processing Systems, Canada, USA, Dec. 2021, pp. 1–32.

M. A. Al-Shargabi, "An Integrated Decision Support Model For Enhancing Continuous Improvement Of Academic Programs," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4835–4841, Oct. 2019.

M. A. Alsuwaiket, A. H. Blasi, and K. Altarawneh, "Refining Student Marks based on Enrolled Modules’ Assessment Methods using Data Mining Techniques," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5205–5210, Feb. 2020.

O. Osoba and P. K. Davis, "An Artificial Intelligence/Machine Learning Perspective on Social Simulation: New Data and New Challenges," in Social-Behavioral Modeling for Complex Systems, New York, NY, USA: John Wiley & Sons, 2019, pp. 443–476.

K. Hasegawa, G. Tashiro, S. Kiritani, and K. Tachikawa, "Intelligent Marine Traffic Simulator for Congested Waterways," in 7th IEEE International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, 2001, pp. 1–6.

C. Korkmaz and A.-P. Correia, "A review of research on machine learning in educational technology," Educational Media International, vol. 56, no. 3, pp. 250–267, Jul. 2019.

D. Hooshyar, M. Pedaste, and Y. Yang, "Mining Educational Data to Predict Students’ Performance through Procrastination Behavior," Entropy, vol. 22, no. 1, Jan. 2020, Art. no. 12.

K. Sharma, Z. Papamitsiou, and M. Giannakos, "Building pipelines for educational data using AI and multimodal analytics: A ‘grey-box’ approach," British Journal of Educational Technology, vol. 50, no. 6, pp. 3004–3031, 2019.

P. Dayananda, M. V. Latte, M. S. Raisinghani, and C. N. Sowmyarani, "New approach for target setting mechanism of course outcomes in higher education accreditation," Journal of Economic and Administrative Sciences, vol. 37, no. 1, pp. 79–89, Jan. 2020.

H. Wang et al., "Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective," Education and Information Technologies, vol. 28, no. 7, pp. 9113–9148, Jul. 2023.

S. K G and M. Kurni, "Educational Data Mining & Learning Analytics," in A Beginner’s Guide to Learning Analytics, S. K G and M. Kurni, Eds. New York, NY, USA: Springer, 2021, pp. 29–60.

J. Cai, J. Luo, S. Wang, and S. Yang, "Feature selection in machine learning: A new perspective," Neurocomputing, vol. 300, pp. 70–79, Jul. 2018.

T. Maguire, L. Manuel, R. A. Smedinga, and M. Biehl, "A review of feature selection and ranking methods," in Proceedings of the 19th SC@ RUG 2022, 2022, pp. 15–20.

S. M. R. Abidi, M. Hussain, Y. Xu, and W. Zhang, "Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development," Sustainability, vol. 11, no. 1, Jan. 2019, Art. no. 105.

C. Yang, F.-K. Chiang, Q. Cheng, and J. Ji, "Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems," Journal of Educational Computing Research, vol. 59, no. 6, pp. 1015–1035, Oct. 2021.

K. Hemachandran et al., "Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education," Computational Intelligence and Neuroscience, vol. 2022, May 2022, Art. no. e1410448.

S. Alshmrany, "Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model," Cluster Computing, vol. 25, no. 1, pp. 523–536, Feb. 2022.

A. Berguiga, A. Harchay, A. Massaoudi, M. B. Ayed, and H. Belmabrouk, "GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture," Computers, Materials & Continua, vol. 77, no. 1, 2023, Art. no. 379.

Y. Ma and W. Lu, "Design and Implementation of Learning System Based on T-LSTM," in 20th International Conference on Web-Based Learning, Macau, China, Nov. 2021, pp. 148–153.

S. Jagannatha, M. Niranjanamurthy, and P. Dayananda, "Algorithm Approach: Modelling and Performance Analysis of Software System," Journal of Computational and Theoretical Nanoscience, vol. 15, no. 11–12, pp. 3389–3397, Nov. 2018.

C. Molnar, Interpretable Machine Learning. Morrisville, NC, USA: Lulu.com, 2020.

D. V. Carvalho, E. M. Pereira, and J. S. Cardoso, "Machine Learning Interpretability: A Survey on Methods and Metrics," Electronics, vol. 8, no. 8, Aug. 2019, Art. no. 832.

R. Ratra and P. Gulia, "Experimental Evaluation of Open Source Data Mining Tools (WEKA and Orange)," International Journal of Engineering Trends and Technology, vol. 68, no. 8, pp. 30–35, Aug. 2020.


How to Cite

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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