A Systematic Review of Machine Learning Models for Learners’ Classification in Personalized Learning Environment
Esha Bansal, Amit Verma
Abstract
Classical e-learning platforms follow “one-size-fits-all” strategy that causes a large number of drawbacks to online
medium of learning such as generic and static resources, limited access to teaching support, limited face-to-face
interaction, isolation, low motivation and technical difficulties. Since the various learners have various
backgrounds, areas, languages, education-level, and socio-demographic factors. It becomes a challenging task for
the learners to read and grasp the same learning material despite of their mental and knowledge deficiencies and
limitations. To overcome such a situation concept of personalized e-learning has been floated by the education
experts. E-learning systems establish personalized learning through individual learner characteristics to improve
both student involvement and academic achievements. Machine learning techniques complete essential work to
categorize learners through assessment of learning profile combinations with performance records and individual
interaction behaviour. This research examines and rates Decision Tree along with Random Forest as well as Neural
network and Support Vector Machine (SVM) to determine their success in learner classification tasks. The
experimental findings show Random Forest and Neural Networks produce better models compared to others while
Neural Networks yield the highest recorded F1-score which reaches 0.89. The developed framework employs AI-
ML technology to perform learner classification which delivers adaptive learning experiences to the users. The
research findings showcase AI-based optimization capabilities for e-learning systems which create opportunities
for intelligent personalized learning system development.
References
1. A. Chanaa; An Analysis of Learners’ Affective and Cognitive Traits in Context-Aware Recommender
Systems (CARS) using Feature Interactions and Factorization Machines (FMs). Journal of King Saud
University-Computer and Information Sciences, 34(8), 4796-4809 (2022).
2. D. Shawky and A. Badawi (2018); A Reinforcement Learning Based Adaptive Learning System. The
International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018),
221-231.
3. D.F. Mujtaba and N.R. Mahapatra (2020); Artificial Intelligence in Computerized Adaptive Testing. 2020
International Conference on Computational Science and Computational Intelligence (CSCI), 649-654.
4. H. Peng; S. Ma and J.M. Spector; Personalized Adaptive Learning: An Emerging Pedagogical Approach
Enabled by a Smart Learning Environment. Smart Learning Environments, 6(1), 1-14 (2019).
5. I. Krechetov and V. Romanenko; Implementing the Adaptive Learning Techniques. Educational Issues, 2,
252-277 (2020).
6. I. Zliobaite; A. Bifet; M. Gaber; B. Gabrys; J. Gama; L. Minku and K. Musial; Next Challenges for Adaptive
Learning Systems. ACM SIGKDD Explorations Newsletter, 14(1), 48-55 (2012).
7. J. Liang; R. Hare; T. Chang; F. Xu; Y. Tang; F.Y. Wang and M. Lei; Student Modeling and Analysis in
Adaptive Instructional Systems. IEEE Access, 10, 59359-59372 (2022).
8. J. Xu; K.H. Moon and M.V.D. Schaar; A Machine Learning Approach for Tracking and Predicting Student
Performance in Degree Programs. IEEE Journal of Selected Topics in Signal Processing, 11(5), 742-753
(2017).
9. J.R. Rico-Juan; C. Cachero and H. Macià; Study Regarding the Influence of a Student’s Personality and an
LMS Usage Profile on Learning Performance using Machine Learning Techniques. Applied Intelligence,
54, 6175–6197 (2024).
10. K. Hazeena; An Adaptive Personalized E-Learning Platform with Cognitive Varwk Adaptation and
Gamification. Journal of Namibian Studies: History Politics Culture, 33, 2103-2116 (2023).
11. K. Pliakos; S.H. Joo; J.Y. Park; F. Cornillie; C. Vens and W. Van den Noortgate; Integrating Machine
Learning into Item Response Theory for Addressing the Cold Start Problem in Adaptive Learning Systems.
Computers & Education, 137, 91-103 (2019).
12. M. Aleid; T.H.H. Aldhyani; O.I. Khalaf and S. Algburi; Modelling and Predicting Student Flexibility in
Online Learning Using Machine Learning: Students’ Academic Performance. International Journal of
Computing and Digital Systems, 15(1), 1-20 (2024).
13. M. Ezz and A. Elshenawy; Adaptive Recommendation System using Machine Learning Algorithms for
Predicting Student’s Best Academic Program. Education and Information Technologies, 25(4), 2733-2746
(2020).
14. M. Jebbari; B. Cherradi; S. Hamida and A. Raihani; Identifying Learning Styles in MOOCs Environment
through Machine Learning Predictive Modeling. Education and Information Technologies, 29(16), 20977 -
21014 (2024).
15. M. Taherisadr; M.A. Al-Faruque and S. Elmalaki; Erudite: Human-in-the-loop IOT for an Adaptive
Personalized Learning System. IEEE Internet of Things Journal, 11(8), 14532-14550 (2024).
16. M.A. Hussein; H.A. Hassan and M. Nassef; A Trait-based Deep Learning Automated Essay Scoring System
with Adaptive Feedback. International Journal of Advanced Computer Science and Applications, 11(5), 287-
293 (2020).
17. M.H. Suzan; N.A. Samrin; A.A. Biswas and A. Pramanik (2021); Students' Adaptability Level Prediction in
Online Education using Machine Learning Approaches. 2021 12th International Conference on Computing
Communication and Networking Technologies (ICCCNT), 1-7.
18. N. Nouman; Z.A. Shaikh and S. Wasi; A Novel Personalized Learning Framework with Interactive E-
Mentoring. IEEE Access, 12, 10428-10458 (2024).
19. P. Liao; Y. Sun; S. Ye; X. Li; G. Su and Y. Sun (2017); Predicting Learners' Multi-question Performance
Based on Neural Networks, 2017 International Conference on Behavioral, Economic, Socio-cultural
Computing (BESC), 1-6.
20. P. Ramírez-Correa; J. Alfaro-Pérez and M. Gallardo; Identifying Engineering Undergraduates’ Learning
Style Profiles using Machine Learning Techniques. Applied Sciences, 11(22), p. 10505 (2021).
21. P.J. Standen; D.J. Brown; M. Taheri; M.J.G. Trigo; H. Boulton; A. Burton and E. Hortal; An Evaluation of
an Adaptive Learning System based on Multimodal Affect Recognition for Learners with Intellectual
Disabilities. British Journal of Educational Technology, 51(5), 1748-1765 (2020).
22. R. Bajaj and V. Sharma; Smart Education with Artificial Intelligence-based Determination of Learning
Styles. Procedia Computer Science, 132, 834-842 (2018).
23. R. Tsoni; C.Τ. Panagiotakopoulos; V.S. Verykios; Revealing Latent Traits in the Social Behavior of Distance
Learning Students. Education and Information Technologies, 27(3), 3529-3565 (2022).
24. T. Hussain; L. Yu; M. Asim; A. Ahmed and M.A. Wani; Enhancing E-Learning Adaptability with
Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for
Smart Education, Information, 15(5), p. 277 (2024).
25. T.S.S. Kumar and R. Thandeeswaran; An Improved Adaptive Personalization Model for Instructional Video-
based E-learning Environments. Journal of Computers in Education, 12(1), 1-47 (2024).
26. U. Markowska-Kaczmar; H. Kwasnicka and M. Paradowski; Intelligent Techniques in Personalization of
Learning in E-Learning Systems. In: Xhafa, F., Caballé, S., Abraham, A., Daradoumis, T., Juan Perez, A.A.
(eds) Computational Intelligence for Technology Enhanced Learning. Studies in Computational Intelligence,
Vol 273. Springer, Berlin, Heidelberg, 1-23 (2010).
27. V.M. Idmilao; Factors Affecting Students’ Academic Performance Through Online Learning. Journal for
Educators, Teachers and Trainers, 14(3), 768-780 (2023).
28. Veritasium; The Biggest Myth in Education, YouTube Video, 03:20 (2021).
29. W.S. Sayed; A.M. Noeman; A. Abdellatif; M. Abdelrazek; M.G. Badawy; A. Hamed and S. El-Tantawy;
AI-based Adaptive Personalized Content Presentation and Exercises Navigation for an Effective and
Engaging E-learning Platform. Multimedia Tools and Applications, 82(3), 3303-3333 (2023).
30. X. Li; H. Xu; J. Zhang and H. H. Chang; Deep Reinforcement Learning for Adaptive Learning Systems.
Journal of Educational and Behavioral Statistics, 48(2), 220-243 (2023).
31. Y. Bellarhmouch; A. Jeghal; H. Tairi and N. Benjelloun; A proposed architectural learner model for a
personalized learning environment. Education and Information Technologies, 28(4), 4243-4263 (2023).
32. Y. Chen; X. Li; J. Liu and Z. Ying; Recommendation System for Adaptive Learning. Applied Psychological
Measurement, 42(1), 24-41 (2018).
33. Y. Choi and C. McClenen; Development of Adaptive Formative Assessment System using Computerized
Adaptive Testing and Dynamic Bayesian Networks. Applied Sciences, 10(22), p. 8196 (2020).
Trion Studio
