Advancing Educational Recommender Systems: An AI-Based Model for Personalized Learning Resource Recommendation
Advancing Educational Recommender Systems: An AI-Based Model for Personalized Learning Resource Recommendation |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-5 |
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| Year of Publication : 2025 | ||
| Author : Neeti Pal, Omdev Dahiya, Mrinalini Rana | ||
| DOI : 10.14445/22315381/IJETT-V73I5P126 | ||
How to Cite?
Neeti Pal, Omdev Dahiya, Mrinalini Rana, "Advancing Educational Recommender Systems: An AI-Based Model for Personalized Learning Resource Recommendation," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.304-327, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P126
Abstract
A Recommender System can assist and guide the learners in selecting the learning resource that is coordinated to users’ needs and preferences. Advanced learning methods provide various tools that increase learners’ engagement and improve learning outcomes. This research work designs a collaborative filtering and content-based filtering recommender system for an educational environment. The personalized recommendations are made based on learners’ interests in particular resources. By analyzing the Open University learning analytics dataset (OULAD), the relevant resources are suggested to learners by evaluating the number of clicks on the resources. A personalized recommender system contributes to understanding the concept of explainable Artificial Intelligence to make models more interpretable and rationalize their decisions. The evaluation results with an average accuracy of 0.9973 and Root Mean Squared Error (RMSE) of 0.0606 provide more appropriate recommendations compared to similar studies. Additionally, the model showed recall and precision values of 1.0, outperforming other existing methods. This recommendation model is highly adaptable and capable of integrating with various Learning Management Systems (LMS) in educational domains, thereby enriching students’ learning experiences with finely tuned personalized suggestions.
Keywords
Collaborative filtering, Educational Recommender System (ERS), Higher Education, Resource Recommendation, Educational Data Mining, E-Learning, Learning Outcomes, Artificial Intelligence (AI), AI in Education (AIED), Interpretable Machine.
References
[1] Julián Monsalve-Pulido et al., “Autonomous Recommender System Architecture for Virtual Learning Environments,” Applied Computing and Informatics, vol. 20, no. 1/2, pp. 69-88, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Wangmei Chen et al., “Applying Machine Learning Algorithm to Optimize Personalized Education Recommendation System,” Journal of Theory and Practice of Engineering Science, vol. 4, no. 1, pp. 101-108, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Kerstin Wagner et al., “Personalized and Explainable Course Recommendations for Students at Risk of Dropping Out,” Proceedings of the 15th International Conference on Educational Data Mining, 2022.
[Google Scholar] [Publisher Link]
[4] Samina Amin et al., “An Adaptable and Personalized Framework for Top-N Course Recommendations in Online Learning,” Scientific Reports, vol. 14, no. 1, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Hadis Ahmadian Yazdi, Seyyed Javad Seyyed Mahdavi, and Hooman Ahmadian Yazdi, “Dynamic Educational Recommender System Based on Improved LSTM Neural Network,” Scientific Reports, vol. 14, no. 1, pp. 1-119, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Shadi Atalla et al., “An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling,” Mathematics, vol. 11, no. 5, pp. 1-25, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Jinnie Shin, and Okan Bulut, “Building an Intelligent Recommendation System for Personalized Test Scheduling in Computerized Assessments: A Reinforcement Learning Approach,” Behavior Research Methods, vol. 54, no. 1, pp. 216-232, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Samina Amin et al., “Developing a Personalized e-Learning and MOOC Recommender System in IoT-Enabled Smart Education,” IEEE Access, vol. 11, pp. 136437-136455, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Shuai Zhang et al., “Deep Learning Based Recommender System: A Survey and New Perspectives,” ACM Computing Surveys, vol. 52, no. 1, pp. 1-38, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] John K. Tarus, Zhendong Niu, and Abdallah Yousif, “A Hybrid Knowledge-Based Recommender System for E-Learning Based on Ontology and Sequential Pattern Mining,” Future Generation Computer Systems, vol. 72, pp. 37-48, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Paul Covington, Jay Adams, and Emre Sargin, “Deep Neural Networks for YouTube Recommendations,” Proceedings of the 10th ACM Conference on Recommender Systems, Boston Massachusetts, USA, pp. 191-198, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Vusumuzi Maphosa, and Mfowabo Maphosa, “Fifteen Years of Recommender Systems Research in Higher Education: Current Trends and Future Direction,” Applied Artificial Intelligence, vol. 37, no. 1, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Boxuan Ma et al., “CourseQ: The Impact of Visual and Interactive Course Recommendation in University Environments,” Research and Practice Technology Enhanced Learning, vol. 16, no. 1, pp. 1-24, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Hsiao-Chien Tseng et al., “Building an Online Adaptive Learning and Recommendation Platform,” Emerging Technologies for Education: First International Symposium, Rome, Italy, pp. 428-432, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Stanislav Kuznetsov et al., “Reducing Cold Start Problems in Educational Recommender Systems,” 2016 International Joint Conference on Neural Networks, Vancouver, BC, Canada, pp. 3143-3149, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Sajid Ali et al., “Explainable Artificial Intelligence (XAI): What We Know and What is Left to Attain Trustworthy Artificial Intelligence,” Information Fusion, vol. 99, PP. 1-52, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Andrew Saxe, Stephanie Nelli, and Christopher Summerfield, “If Deep Learning is the Answer, What is the Question?,” Nature Reviews Neuroscience, vol. 22, no. 1, pp. 55-67, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Zewen Li et al., “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999-7019, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Giulia Vilone, and Luca Longo, “Notions of Explainability and Evaluation Approaches for Explainable Artificial Intelligence,” Information Fusion, vol. 76, pp. 89-106, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Finale Doshi-Velez, and Been Kim, “Towards a Rigorous Science of Interpretable Machine Learning,” Arxiv, pp. 1-13, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Alonso José Maria, “Explainable Fuzzy Systems-Paving the Way from Interpretable Fuzzy Systems to Explainable AI Systems,” Studies in Computational Intelligence, vol. 970, pp. 1-253, 2021.
[Google Scholar] [Publisher Link]
[22] Judea Pearl, and Dana Mackenzie, The Book of Why: The New Science of Cause and Effect, Penguin Books Limited, pp. 1-432, 2018.
[Google Scholar] [Publisher Link]
[23] Hassan Khosravi et al., “Explainable Artificial Intelligence in Education,” Computer and Education: Artificial Intelligence, vol. 3, pp. 1-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Tim Miller, “Explanation in Artificial Intelligence: Insights from the Social Sciences,” Artificial Intelligence, vol. 267, pp. 1-38, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ernest L. Boyer, Scholarship Reconsidered: Priorities of the Professoriate, Carnegie Foundation for the Advancement of Teaching, pp. 1-147, 1990.
[Google Scholar] [Publisher Link]
[26] Carolin Kreber, “Reflection on Teaching and the Scholarship of Teaching: Focus on Science Instructors,” Higher Education, vol. 50, pp. 323-359, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Margaret Price et al., “Feedback: All That Effort, But What is the Effect?,” Assessment and Evaluation in Higher Education, vol. 35, no. 3, pp. 277-289, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ashima Kukkar et al., “A Novel Methodology using RNN+ LSTM+ ML for Predicting Students’ Academic Performance,” Education and Information Technology, vol. 29, pp. 14365-14401, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Veena Grover et al., “Proposed Hybrid Model in Online Education,” EAI Endorsed Transactions on Internet of Things, vol. 10, pp. 1-7, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Qionghao Huang, and Yan Zeng, “Improving Academic Performance Predictions with Dual Graph Neural Networks,” Complex and Intelligent Systems, vol. 10, pp. 3557-3575, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Hao Luo et al., “Object-Oriented Online Course Recommendation Systems Based on Deep Neural Networks,” Journal of Theoretical and Applied Information Technology, vol. 102, no. 3, pp. 1276-1287, 2024.
[Google Scholar] [Publisher Link]
[32] Yan Zhu, “Personalized Recommendation of Educational Resource Information Based on Adaptive Genetic Algorithm,” International Journal of Reliability, Quality and Safety Engineering, vol. 30, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Hadi Ezaldeen et al., “Semantics Aware Intelligent Framework for Content-Based E-Learning Recommendation,” Natural Language Processing Journal, vol. 3, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Dudla Anil Kumar, and M. Ezhilarasan, “Shufflenetv2: An Effective Technique for Recommendation System in e-Learning by User Preferences,” Multi-disciplinary Trends in Artificial Intelligence: 16th International Conference, Hyderabad, India, pp. 179-191, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Dina Fitria Murad et al., “Personalization of Study Material based on Predicted Final Grades using Multi-Criteria User-Collaborative Filtering Recommender System,” Education and Information Technologies, vol. 25, no. 6, pp. 5655-5668, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Jing Li, and Zhou Ye, “Course Recommendations in Online Education based on Collaborative Filtering Recommendation Algorithm,” Complexity, vol. 2020, no. 1, pp. 1-10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Gongwen Xu et al., “Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering,” Computational Intelligence Neuroscience, vol. 2021, no. 1, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Hadis Ahmadian Yazdi, Seyyed Javad Seyyed Mahdavi Chabok, and Maryam Kheirabadi, “Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique,” Applied Artificial Intelligence, vol. 36, no. 1, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Jamal Mawane, Abdelwahab Naji, and Mohamed Ramdani, “Unsupervised Deep Collaborative Filtering Recommender System for e-Learning Platforms,” Smart Applications and Data Analysis: Third International Conference, Marrakesh, Morocco, pp. 146-161, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Ming Gao, Yonghan Luo, and Xiaonan Hu, “Online Course Recommendation using Deep Convolutional Neural Network with Negative Sequence Mining,” Wireless Communications and Mobile Computing, vol. 2022, no. 1, pp. 1-7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Xu Zhu, and Zhaofa Zhang, “Precise Recommendation Algorithm for Online Sports Video Teaching Resources,” EAI Endorsed Transactions on Scalable Information Systems, vol. 10, no. 2, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Jun Xiao et al., “A Personalized Recommendation System with Combinational Algorithm for Online Learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 3, pp. 667-677, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Kumar Abhinav et al., “LeCoRe: A Framework for Modeling Learner's Preference,” 11th International Conference on Educational Data Mining, Buffalo, New York, US, 2018.
[Google Scholar] [Publisher Link]
[44] Soulef Benhamdi, Abdesselam Babouri, and Raja Chiky, “Personalized Recommender System for e-Learning Environment,” Education and Information Technologies, vol. 22, pp. 1455-1477, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Sunita B Aher, and L.M.R.J. Lobo, “A Framework for Recommendation of Courses in e-Learning System,” International Journal of Computer Applications, vol. 35, no. 4, pp. 21-28, 2011.
[Google Scholar] [Publisher Link]
[46] Jakub Kuzilek, Martin Hlosta, and Zdenek Zdrahal, “Open University Learning Analytics Dataset,” Scientific Data, vol. 4, no. 1, pp. 1-8, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Ahmed Rashad Sayed et al., “Predict Student Learning Styles and Suitable Assessment Methods Using Clickstream,” Egyptian Informatics Journal, vol. 26, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Wala Bagunaid, Naveen Chilamkurti, and Prakash Veeraraghavan, “Aisar: Artificial Intelligence-Based Student Assessment and Recommendation System for e-Learning in Big Data,” Sustainability, vol. 14, no. 17, pp. 1-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Ying Zhang, “Developing an Adaptive Learning Recommendation Algorithm and System for MOOCs,” Journal of Machine and Computing, vol. 4, no. 4, pp. 962-970, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Nacim Yanes et al., “A Machine Learning-Based Recommender System for Improving Students Learning Experiences,” IEEE Access, vol. 8, pp. 201218-201235, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Sunil, and M.N. Doja, “An Improved Recommender System for e-Learning Environments to Enhance Learning Capabilities of Learners,” Proceedings of ICETIT 2019: Emerging Trends in Information Technology, pp. 604-612, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Ipseeta Nanda et al., “ALBERT-Based Personalized Educational Recommender System: Enhancing Students’ Learning Outcomes in Online Learning,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 10, pp. 2190-2201, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Mustafa Bin Tariq, and Hafiz Adnan Habib, “A Reinforcement Learning Based Recommendation System to Improve Performance of Students in Outcome Based Education Model,” IEEE Access, vol. 12, pp. 36586-36605, 2024.
[CrossRef] [Google Scholar] [Publisher Link]