Curriculum-Aware Contrastive Learning Bi-Directional Gated Recurrent Unit for Student Learning Course Recommendation and Classification
Received: 28 August 2025 | Revised: 14 October 2025 and 5 November 2025 | Accepted: 6 November 2025 | Online: 9 February 2026
Corresponding author: Joshi Vinay Kumar
Abstract
Student learning course recommendation is a system that recommends the appropriate next course for students based on their interests and performance in previous courses. In a course recommendation system, after a student completes a suggested course, the classification task involves predicting whether the student would recommend the course to others. However, traditional course recommendation models rely on collaborative filtering or static rules, and graph-based attention models frequently ignore curriculum structure and sequential dependencies between courses, which leads to irrelevant or misaligned recommendations that affect student engagement and academic progression and fail to reflect a student's learning path. To overcome these challenges, a Curriculum-Aware Contrastive Learning–Bi-directional Gated Recurrent Unit (CACL-Bi-GRU) is proposed for student learning course recommendation and classification to improve model performance by capturing personalized learning patterns and curriculum dependencies. The proposed CACL-Bi-GRU is utilized to capture both future and past dependencies in sequential course history and to predict the next course based on sequential student enrolment. The proposed model integrates contrastive learning to differentiate semantically relevant courses, whereas the Bi-GRU model learns forward and backward dynamics. The proposed model achieves high values for the Hit Ratio of top-K items (HR@K) at HR @10, Normalized Discounted Cumulative Gain of top-K items (NDCG@K) at NDCg@10, and Mean Reciprocal Rank (MRR) values of 91.85, 65.87, and 56.97, respectively, compared to the Contrastive Learning and Graph convolutional-based Attention Decay network (CLGADN). The proposed model output ensures that associating curriculum-aware context and sequential modeling improves both the relevance and accuracy of course recommendations.
Keywords:
Bi-directional Gated Recurrent Unit (Bi-GRU), course history, curriculum-aware contrastive learning, student learning, recommendationDownloads
References
M. Afzaal, A. Zia, J. Nouri, and U. Fors, "Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation," Technology, Knowledge and Learning, vol. 29, no. 1, pp. 331–354, Mar. 2024. DOI: https://doi.org/10.1007/s10758-023-09650-0
S. Bhaskaran and R. Marappan, "Design and analysis of an efficient machine learning based hybrid recommendation system with enhanced density-based spatial clustering for digital e-learning applications," Complex & Intelligent Systems, vol. 9, no. 4, pp. 3517–3533, Aug. 2023. DOI: https://doi.org/10.1007/s40747-021-00509-4
J.-W. Tzeng, N.-F. Huang, A.-C. Chuang, T.-W. Huang, and H.-Y. Chang, "Massive open online course recommendation system based on a reinforcement learning algorithm," Neural Computing and Applications, vol. 37, no. 18, pp. 11607–11618, June 2025. DOI: https://doi.org/10.1007/s00521-023-08686-8
W. Deng, P. Zhu, H. Chen, T. Yuan, and J. Wu, "Knowledge-aware sequence modelling with deep learning for online course recommendation," Information Processing & Management, vol. 60, no. 4, July 2023, Art. no. 103377. DOI: https://doi.org/10.1016/j.ipm.2023.103377
H. Zhang, X. Shen, B. Yi, W. Wang, and Y. Feng, "KGAN: Knowledge Grouping Aggregation Network for course recommendation in MOOCs," Expert Systems with Applications, vol. 211, Jan. 2023, Art. no. 118344. DOI: https://doi.org/10.1016/j.eswa.2022.118344
J. Chinnadurai et al., "Enhancing online education recommendations through clustering-driven deep learning," Biomedical Signal Processing and Control, vol. 97, Nov. 2024, Art. no. 106669. DOI: https://doi.org/10.1016/j.bspc.2024.106669
M. Praseptiawan, N. M. Putri, M. F. Damar Muchtarom, M. H. Zakaria, and A. N. Che Pee, "Application of Collaborative Filtering and Explainable AI Methods in Recommendation System Modeling to Predict MOOC Course Preferences," in 2024 2nd International Symposium on Information Technology and Digital Innovation, Bukittinggi, Indonesia, 2024, pp. 228–233. DOI: https://doi.org/10.1109/ISITDI62380.2024.10797073
C. Troussas, F. Giannakas, C. Sgouropoulou, and I. Voyiatzis, "Collaborative activities recommendation based on students’ collaborative learning styles using ANN and WSM," Interactive Learning Environments, vol. 31, no. 1, pp. 54–67, Jan. 2023. DOI: https://doi.org/10.1080/10494820.2020.1761835
A. B. Rashid, R. R. R. Ikram, Y. Thamilarasan, L. Salahuddin, N. F. A. Yusof, and Z. B. Rashid, "A Student Learning Style Auto-Detection Model in a Learning Management System," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 11000–11005, June 2023. DOI: https://doi.org/10.48084/etasr.5751
T. Nguyen et al., "H-BERT4Rec: Enhancing Sequential Recommendation System on MOOCs Based on Heterogeneous Information Networks," IEEE Access, vol. 12, pp. 155789–155803, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3462830
X. Xia, "Learning behavior mining and decision recommendation based on association rules in interactive learning environment," Interactive Learning Environments, vol. 31, no. 2, pp. 593–608, Feb. 2023. DOI: https://doi.org/10.1080/10494820.2020.1799028
N. S. M. Yusop, M. M. Rosli, N. F. Farid, N. A. S. M. Nazri, N. Jamil, and M. I. Ramli, "Development of a Collaborative Intelligent Individual Education Program System using a Prototyping Approach," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14666–14676, June 2024. DOI: https://doi.org/10.48084/etasr.7352
P. Hao, Y. Li, and C. Bai, "Meta-relationship for course recommendation in MOOCs," Multimedia Systems, vol. 29, no. 1, pp. 235–246, Feb. 2023. DOI: https://doi.org/10.1007/s00530-022-00989-5
X. Wang, L. Jia, L. Guo, and F. Liu, "Multi-aspect heterogeneous information network for MOOC knowledge concept recommendation," Applied Intelligence, vol. 53, no. 10, pp. 11951–11965, May 2023. DOI: https://doi.org/10.1007/s10489-022-04025-x
C. C. Y. Yang and H. Ogata, "Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning," Education and Information Technologies, vol. 28, no. 3, pp. 2509–2528, Mar. 2023. DOI: https://doi.org/10.1007/s10639-022-11291-2
N. S. Raj and V. G. Renumol, "An improved adaptive learning path recommendation model driven by real-time learning analytics," Journal of Computers in Education, vol. 11, no. 1, pp. 121–148, Mar. 2024. DOI: https://doi.org/10.1007/s40692-022-00250-y
S. Hussain and M. Q. Khan, "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, vol. 10, no. 3, pp. 637–655, June 2023. DOI: https://doi.org/10.1007/s40745-021-00341-0
W. Ma, W. Chen, L. Lu, and X. Fan, "Integrating learners’ knowledge background to improve course recommendation fairness: A multi-graph recommendation method based on contrastive learning," Information Processing & Management, vol. 61, no. 4, July 2024, Art. no. 103750. DOI: https://doi.org/10.1016/j.ipm.2024.103750
M. Li, Z. Li, C. Huang, Y. Jiang, and X. Wu, "EduGraph: Learning Path-Based Hypergraph Neural Networks for MOOC Course Recommendation," IEEE Transactions on Big Data, vol. 10, no. 6, pp. 706–719, Dec. 2024. DOI: https://doi.org/10.1109/TBDATA.2024.3453757
X. Tang, H. Zhang, N. Zhang, H. Yan, F. Tang, and W. Zhang, "Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network," Mobile Information Systems, vol. 2022, no. 1, May 2022, Art. no. 8255965. DOI: https://doi.org/10.1155/2022/8255965
C. Jin, "MOOC student dropout prediction model based on learning behavior features and parameter optimization," Interactive Learning Environments, vol. 31, no. 2, pp. 714–732, Feb. 2023. DOI: https://doi.org/10.1080/10494820.2020.1802300
X. Yu, Q. Mao, X. Wang, Q. Yin, X. Che, and X. Zheng, "CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view," Expert Systems with Applications, vol. 249, no. C, Sept. 2024, Art. no. 123777. DOI: https://doi.org/10.1016/j.eswa.2024.123777
S. S. Kusumawardani and S. A. I. Alfarozi, "Transformer Encoder Model for Sequential Prediction of Student Performance Based on Their Log Activities," IEEE Access, vol. 11, pp. 18960–18971, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3246122
J. Zhou, G. Jiang, W. Du, and C. Han, "Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation," Electronic Commerce Research, vol. 23, no. 4, pp. 2357–2377, Dec. 2023. DOI: https://doi.org/10.1007/s10660-022-09541-z
L. Chaw, "Dataset related to MOOCs." Mendeley Data, 2022.
S. Amin, M. I. Uddin, W. K. Mashwani, A. A. Alarood, A. Alzahrani, and A. O. Alzahrani, "Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education," IEEE Access, vol. 11, pp. 136437–136455, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3336676
Downloads
How to Cite
License
Copyright (c) 2025 Joshi Vinay Kumar, Dahlia Sam
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
