Citation :

Akbayan Bekarystankyzy, Vyacheslav Khan, Aray Kassenkhan, "Recommendation Algorithms for Educational Systems," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 259-276, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P120

Abstract

This study offers an in-depth review of techniques and tools used in supporting personalized learning in e-learning environments. The techniques and tools discussed in this study include user-centric and technology-centric techniques, such as several popular models of learning styles, including the Myers-Briggs Type Indicator, Kolb’s theory of experiential learning, Dunn and Dunn’s learning style model, and Felder-Silverman’s learning style model. The technological aspects discussed in this study include an overview of the Protus system, which is used as an example of how adaptive hypermedia and semantic technologies can be used in supporting personalized learning. The combination of user modeling and recent advances in technology highlights the limitations of both techniques when used in isolation. The limitations discussed in this study include ethical concerns, data privacy, and the lack of standardization in recommendations. The analysis ends with an overview of future research possibilities, such as using natural language processing in combination with adaptive personalization techniques.

Keywords

Personalized Learning, Recommendation Systems, Adaptive Education, Machine Learning, User Modeling, Educational Data Mining, Hybrid Learning Models.

References

[1] Vivat Thongchotchat et al., “Educational Recommendation System Utilizing Learning Styles: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 8988-8999, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Bushra Alhijawi, and Yousef Kilani, “A Collaborative Filtering Recommender System using Genetic Algorithm,” Information Processing and Management, vol. 57, no. 6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Brahim Hmedna, Ali El Mezouary, and Omar Baz, “A Predictive Model for the Identification of Learning Styles in MOOC Environments,” Cluster Computing, vol. 23, no. 2, pp. 1303-1328, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Atikah Shemshack and Jonathan Michael Spector, “A Systematic Literature Review of Personalized Learning Terms,” Smart Learning Environments, vol. 7, no. 1, pp. 1-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Aditya Khamparia, and Babita Pandey, “Association of Learning Styles with Different E-Learning Problems: A Systematic Review and Classification,” Education and Information Technologies, vol. 25, no. 2, pp. 1303-1331, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Sunny Sharma, Vijay Rana, and Vivek Kumar, “Deep Learning based Semantic Personalized Recommendation System,” International Journal of Information Management Data Insights, vol. 1, no. 2, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Inma Rodríguez-Ardura, and Antoni Meseguer-Artola, “E-Learning Continuance: The Impact of Interactivity and the Mediating Role of Imagery, Presence and Flow,” Information and Management, vol. 53, no. 4, pp. 504-516, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Huong May Truong, “Integrating Learning Styles and Adaptive E-Learning System: Current Developments, Problems and Opportunities,” Computers in Human Behavior, vol. 55, pp. 1185-1193, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[9] John K. Tarus, Zhendong Niu, and Ghulam Mustafa, “Knowledge-based Recommendation: A Review of Ontology-based Recommender Systems for E-Learning,” Artificial Intelligence Review, vol. 50, no. 1, pp. 21-48, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Richard M. Felder, and Linda K. Silverman, “Learning and Teaching Styles in Engineering Education,” Engineering Education, vol. 78, no. 7, pp. 674-681, 1988.
[Google Scholar] [Publisher Link]

[11] Peter Brusilovsky, and Christoph Peylo, “Adaptive and Intelligent Web-based Educational Systems,” vol. 13, no. 2-4, pp. 156-169, 2003.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Leonard Tetzlaff, Florian Schmiedek, and Garvin Brod, “Developing Personalized Education: A Dynamic Framework,” Educational Psychology Review, vol. 33, no. 3, pp. 863-882, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Lazar Stankov, Suzanne Morony, and Yim Ping Lee, Confidence: The Best Noncognitive Predictor of Academic Achievement? 1^st ed., Noncognitive Psychological Processes and Academic Achievement, Routledge, pp. 19-38, 2016.
[Google Scholar] [Publisher Link]

[14] Omran Alharbi et al., “The use of E-Learning Tools in Education during the COVID-19 Pandemic: Literature Review,” Advances in Social Sciences Research Journal, vol. 7, no. 12, pp. 149-152, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Lixin Zhao, Wu-Yuin Hwang, and Timothy K. Shih, “Investigation of the Physical Learning Environment of Distance Learning Under COVID-19 and its Influence on Students’ Health and Learning Satisfaction,” International Journal of Distance Education Technologies (IJDET), vol. 19, no. 2, pp. 77-98, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Daniel W. Salter, Nancy J. Evans, and Deanna S. Forney, “A Longitudinal Study of Learning Style Preferences on the Myers-Briggs Type Indicator and Learning Style Inventory,” Journal of College Student Development, vol. 47, no. 2, pp. 173-184, 2006.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Alice Y. Kolb, and David A. Kolb, Learning Styles, Encyclopedia of the Sciences of Learning Springer, Boston, MA, pp. 1974-1975, 2021.
[CrossRef] [Publisher Link]

[18] Saul McLeod, and Olivia Guy-Evans, “Kolb’s Learning Styles and Experiential Learning Cycle,” Simply Psychology, vol. 5, pp. 1-21, 2017.
[Google Scholar] [Publisher Link]

[19] D. Christopher Kayes, “Internal Validity and Reliability of Kolb’s Learning Style Inventory Version 3 (1999),” Journal of Business and Psychology, vol. 20, no. 2, pp. 249-257, 2005.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Thomas Howard Morris, “Experiential Learning-A Systematic Review and Revision of Kolb’s Model,” Interactive Learning Environments, vol. 28, no. 8, pp. 1064-1077, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Rita Dunn, and Karen Burke, Learning Style: The Clue to YOU!, University of Murcia, 2005. [Online]. Available: https://webs.um.es/rhervas/miwiki/lib/exe/fetch.php%3Fmedia%3Dlscy_rimanual_v1.pdf

[22] Malgorzata S. Zywno, “A Contribution to Validation of Score Meaning for Felder Soloman's Index of Learning Styles,” Annual Conference, Nashville, Tennessee, pp. 1-17, 2003.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Richard M. Felder, and Joni Spurlin, “Applications, Reliability and Validity of the Index of Learning Styles,” International Journal of Engineering Education, vol. 21, no. 1, pp. 103-112, 2005.
[Google Scholar] [Publisher Link]

[24] Sabine Graf et al., “In-Depth Analysis of the Felder-Silverman Learning Style Dimensions,” Journal of Research on Technology in Education, vol. 40, no. 1, pp. 79-93, 2007.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Dereje Andargie Kidanemariam, “Exploring Students’ Academic Performance in some Fundamental Concepts in Chemistry through Learning Styles and Instructional Materials at Two Preparatory Schools in Ethiopia,” University of South Africa (South Africa) ProQuest Dissertations and Theses, 2013.
[Google Scholar] [Publisher Link]

[26] Maryann Kiely Lovelace, “Meta-Analysis of Experimental Research based on the Dunn and Dunn Model,” The Journal of Educational Research, vol. 98, no. 3, pp. 176-183, 2005.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Ahmad Oweini, and Carma Daouk, “Effects of the Dunn and Dunn Learning Styles Model on Reading Comprehension and Motivation: A Case Study in Innovative Learning,” International Journal for Talent Development and Creativity, vol. 4, pp. 63-70, 2016.
[Google Scholar] [Publisher Link]

[28] Marietta Papadatou-Pastou et al., “The Learning Styles Neuromyth: When the Same Term Means Different Things to Different Teachers,” European Journal of Psychology of Education, vol. 36, no. 2, pp. 511-531, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[29] Philip M. Newton, and Atharva Salvi, “How Common is Belief in the Learning Styles Neuromyth, and Does it Matter? A Pragmatic Systematic Review,” Frontiers in Education, vol. 5, pp. 1-14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[30] Aleksandra Klašnja-Milićević et al., “E-Learning Personalization based on Hybrid Recommendation Strategy and Learning Style Identification,” Computers and Education, vol. 56, no. 3, pp. 885-899, 2011.
[CrossRef] [Google Scholar] [Publisher Link]

[31] Boban Vesin et al., “Protus 2.0: Ontology-based Semantic Recommendation in Programming Tutoring System,” Expert Systems with Applications, vol. 39, no. 15, pp. 12229-12246, 2012.
[CrossRef] [Google Scholar] [Publisher Link]

[32] Boban Vesin, Aleksandra Klašnja-Milićević, and Mirjana Ivanović, “Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System,” Advances in Web-based Learning - ICWL 2016: 15^th International Conference, Rome, Italy, vol. 10013, pp. 236-245, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[33] Aleksandra Klašnja-Milićević et al., Design, Architecture and Interface of Protus 2.1 System, E-Learning Systems, Springer, Cham, vol. 112, pp. 185-212, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[34] Nikolai Scerbakov, Alexander Schukin, and Eugenia Rezedinova, “Architecture of Modern E-Learning Management System,” 2023 4^th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), Plovdiv, Bulgaria, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[35] Boban Vesin, Aleksandra Klašnja-Milićević, and Mirjana Ivanović, “Application of´ Semantic Web Technologies to Facilitate use of E-Learning System on Mobile Devices,” Smart Education and e-Learning, Springer, Cham, vol. 59, pp. 473-484, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[36] Boban Vesin, Katerina Mangaroska, and Michail Giannakos, “Learning in Smart Environments: User-Centered Design and Analytics of an Adaptive Learning System,” Smart Learning Environments, vol. 5, no. 1, pp. 1-21, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[37] Sanaz Bahargam et al., “Team Formation for Scheduling Educational Material in Massive Online Classes,” arXiv preprint, pp. 1-8, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[38] Nur W. Rahayu, Ridi Ferdiana, and Sri S. Kusumawardani, “A Systematic Review of Learning Path Recommender Systems,” Education and Information Technologies, vol. 28, no. 6, pp. 7437-7460, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[39] Michelle Deschênes, “Recommender Systems to Support Learners’ Agency in a Learning Context: A Systematic Review,” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, pp. 1-23, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[40] Hassan Khosravi, Kendra Cooper, and Kirsty Kitto, “Riple: Recommendation in Peer-Learning Environments based on Knowledge Gaps and Interests,” arXiv preprint, pp. 1-25, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[41] Aleksandra Klašnja-Milićević, Mirjana Ivanović, and Alexandros Nanopoulos, “Recommender Systems in E-Learning Environments: A Survey of the State-of-the-Art and Possible Extensions,” Artificial Intelligence Review, vol. 44, no. 4, pp. 571-604, 2015.
[CrossRef] [Google Scholar] [Publisher Link]

[42] Xola Ntlangula, and Wai Sze Leung, “A Social Profile-based Recommendation Architecture for E-Learning Systems,” Intelligent Computing: Proceedings of the 2024 Computing Conference, London, United Kingdom, vol. 1017, pp. 330-343, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[43] Birol Ciloglugil, and Mustafa Murat Inceoglu, “User Modeling for Adaptive E-Learning Systems,” Computational Science and its Applications -- ICCSA 2012: 12^th International Conference, Salvador de Bahia, Brazil, vol. 7335, pp. 550-561, 2012.
[CrossRef] [Google Scholar] [Publisher Link]

[44] Vanessa Hanin, Stéphane Colognesi, and Catherine Van Nieuwenhoven, “From Perceived Competence to Emotion Regulation: Assessment of the Effectiveness of an Intervention Among Upper Elementary Students,” European Journal of Psychology of Education, vol. 36, no. 2, pp. 287-317, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[45] Shristi Shakya Khanal et al., “A Systematic Review: Machine Learning based Recommendation Systems for E-Learning,” Education and Information Technologies, vol. 25, no. 4, pp. 2635-2664, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[46] Ilias Papadogiannis, Manolis Wallace, and Georgia Karountzou, “Educational Data Mining: A Foundational Overview,” Encyclopedia, vol. 4, no. 4, pp. 1644-1664, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[47] Mahnoor Chaudhry et al., “A Systematic Literature Review on Identifying Patterns using Unsupervised Clustering Algorithms: A Data Mining Perspective,” Symmetry, vol. 15, no. 9, pp. 1-44, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[48] Ashish Dutt et al., “Clustering Algorithms Applied in Educational Data Mining,” International Journal of Information and Electronics Engineering, vol. 5, no. 2, pp. 112-116, 2015.
[CrossRef] [Google Scholar] [Publisher Link]

[49] Rakesh Agrawal, and Ramakrishnan Srikant, Fast         Algorithms              for           Mining Association Rules, Proceedings of the 20^th: IBM Almaden Research Center, 1994. [Online]. Available: https://www.vldb.org/conf/1994/P487.PDF

[50] Mofreh A. Hogo, “Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study,” (IJCSIS) International Journal of Computer Science and Information Security, vol. 7, no. 2, pp. 131-140, 2010.
[Google Scholar] [Publisher Link]

[51] Ouafae El Aissaoui et al., “A Fuzzy Classification Approach for Learning Style Prediction based on Web Mining Technique in E-Learning Environments,” Education and Information Technologies, vol. 24, no. 3, pp. 1943-1959, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[52] Oluwatoyin Catherine Agbonifo, “Fuzzy C-Means Clustering Model for Identification of Students’ Learning Preferences in Online Environment,” International Journal of Computer Applications and Information Technology, vol. 4, no. 1, pp. 15-21, 2013.
[Google Scholar] [Publisher Link]

[53] Shrooq Algarni, and Frederick Sheldon, “Systematic     Review    of Recommendation Systems for Course Selection,” Machine Learning and Knowledge Extraction, vol. 5, no. 2, pp. 560-596, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[54] Felipe Leite da Silva et al., “A Systematic Literature Review on Educational Recommender Systems for Teaching and Learning: Research Trends, Limitations and Opportunities,” Education and Information Technologies, vol. 28, no. 3, pp. 3289-3328, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[55] 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. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[56] Zeshan Fayyaz et al., “Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities,” Applied Science, vol. 10, no. 21, pp. 1-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[57] Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro, Content-based Recommender Systems: State of the Art and Trends, Recommender Systems Handbook, Springer, Boston, MA, pp. 73-105, 2010.
[CrossRef] [Google Scholar] [Publisher Link]

[58] Juan Feldman, Ariel Monteserin, and Amandi, “Detecting Students’ Perception Style by using Games,” Computers and Education, vol. 71, pp. 14-22, 2014.
[CrossRef] [Google Scholar] [Publisher Link]