Résumés
Abstract
As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.
Keywords:
- online learning,
- personalization,
- course recommender systems,
- course features,
- learner profiles
Parties annexes
Bibliography
- Abyaa, A., Khalidi Idrissi, M., & Bennani, S. (2019). Learner modelling: Systematic review of the literature from the last 5 years. Educational Technology Research and Development, 67(5), 1105–1143. https://doi.org/10.1007/s11423-018-09644-1
- Agarwal, A., Mishra, D. S., & Kolekar, S. V. (2022). Knowledge-based recommendation system using semantic Web rules based on learning styles for MOOCs. Cogent Engineering, 9(1), 2022568. https://doi.org/10.1080/23311916.2021.2022568
- Agrawal, D., & Deepak, G. (2022). HSIL: Hybrid semantic infused learning approach for course recommendation. In Digital Technologies and Applications: Proceedings of ICDTA ’22 (Vol. 1, pp. 417–426), Fez, Morocco. Springer. https://doi.org/10.1007/978-3-031-01942-5_42
- Ahmad, H. K., Qi, C., Wu, Z., & Muhammad, B. A. (2023). ABiNE-CRS: Course recommender system in online education using attributed bipartite network embedding. Applied Intelligence, 53(4), 4665–4684. https://doi.org/10.1007/s10489-022-03758-z
- Al-Badarenah, A., & Alsakran, J. (2016). An automated recommender system for course selection. International Journal of Advanced Computer Science and Applications, 7(3), 166–175. https://dx.doi.org/10.14569/IJACSA.2016.070323
- Asadi, S., Jafari, S., & Shokrollahi, Z. (2019). Developing a course recommender by combining clustering and fuzzy association rules. Journal of AI and Data Mining, 7(2), 249–262. https://dx.doi.org/10.22044/jadm.2018.6260.1739
- Baguley, M., Danaher, P. A., Davies, A., George-Walker, L., Jones, J. K., Matthews, K. J., Midgely, W., & Arden, C. H. (2014). Educational learning and development: Building and enhancing capacity. Springer. https://link.springer.com/book/10.1057/9781137392848
- Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational data mining and learning analytics. In Andre A. Rupp and Jacqueline P. Leighton (Eds.) The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications (pp. 379-396). Wiley. https://doi.org/10.1002/9781118956588.ch16
- Bakhshinategh, B., Spanakis, G., Zaiane, O., & ElAtia, S. (2017, April). A course recommender system based on graduating attributes. In International Conference on Computer Supported Education (Vol. 2, pp. 347–354). SCITEPRESS. https://doi.org/10.5220/0006318803470354
- Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1), 205–220. https://doi.org/10.1007/s10758-014-9223-7
- Bridges, C., Jared, J., Weissmann, J., Montanez-Garay, A., Spencer, J., & Brinton, C. G. (2018, March). Course recommendation as graphical analysis. In 52nd Annual Conference on Information Sciences and Systems, CISS 2018 (pp. 1–6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CISS.2018.8362325
- Cao, P., & Chang, D. (2020). A novel course recommendation model fusing content-based recommendation and K-means clustering for wisdom education. In LISS2019: Proceedings of the 9th International Conference on Logistics, Informatics and Service Sciences (pp. 789–809). Springer. https://doi.org/10.1007/978-981-15-5682-1_57
- Chang, P. C., Lin, C. H., & Chen, M. H. (2016). A hybrid course recommendation system by integrating collaborative filtering and artificial immune systems. Algorithms, 9(3), 47. https://doi.org/10.3390/a9030047
- Chen, W., Ma, W., Jiang, Y., & Fan, X. (2022, July). GADN: GCN-Based attentive decay network for course recommendation. In Proceedings of Knowledge Science, Engineering and Management: 15th International Conference, KSEM 2022 (Part 1, pp. 529–541), Singapore. Springer. https://doi.org/10.1007/978-3-031-10983-6_41
- Deschênes, M. (2020). Recommender systems to support learners’ agency in a learning context: A systematic review. International Journal of Educational Technology in Higher Education, 17(1), 50. https://doi.org/10.1186/s41239-020-00219-w
- Elbadrawy, A., & Karypis, G. (2016, September). Domain-aware grade prediction and top-n course recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 183–190). https://doi.org/10.1145/2959100.2959133
- Esteban, A., Zafra, A., & Romero, C. (2020). Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowledge-Based Systems, 194, 105385. https://doi.org/10.1016/j.knosys.2019.105385
- Fan, J., Jiang, Y., Liu, Y., & Zhou, Y. (2022). Interpretable MOOC recommendation: A multi-attention network for personalized learning behavior analysis. Internet Research, 32(2), 588–605. https://doi.org/10.1108/INTR-08-2020-0477
- Guo, Y., Chen, Y., Xie, Y., & Ban, X. (2022). An effective student grouping and course recommendation strategy based on big data in education. Information, 13(4), 197. https://doi.org/10.3390/info13040197
- Guruge, D. B., Kadel, R., & Halder, S. J. (2021). The state of the art in methodologies of course recommender systems—A review of recent research. Data, 6(2), 18. https://doi.org/10.3390/data6020018
- Huang, X., Tang, Y., Qu, R., Li, C., Yuan, C., Sun, S., & Xu, B. (2018, May). Course recommendation model in academic social networks based on association rules and multi-similarity. In 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE 2018 (pp. 277–282). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CSCWD.2018.8465266
- Ibrahim, M. E., Yang, Y., Ndzi, D. L., Yang, G., & Al-Maliki, M. (2018). Ontology-based personalized course recommendation framework. IEEE Access, 7, 5180–5199. https://doi.org/10.1109/ACCESS.2018.2889635
- Jiang, W., Pardos, Z. A., & Wei, Q. (2019, March). Goal-based course recommendation. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 36–45). https://doi.org/10.1145/3303772.3303814
- Jiang, X., Bai, L., Yan, X., & Wang, Y. (2022). LDA-based online intelligent courses recommendation system. Evolutionary Intelligence, 16, 1619–1625. https://doi.org/10.1007/s12065-022-00810-2
- Jing, X., & Tang, J. (2017, August). Guess you like: Course recommendation in MOOCs. In Proceedings of the International Conference on Web Intelligence (pp. 783–789). https://doi.org/10.1145/3106426.3106478
- Jung, H., Jang, Y., Kim, S., & Kim, H. (2022). KPCR: Knowledge graph enhanced personalized course recommendation. In Proceedings of Advances in Artificial Intelligence: 34th Australasian Joint Conference, AI 2021 (pp. 739–750), Sydney, Australia. Springer. https://doi.org/10.1007/978-3-030-97546-3_60
- Khalid, A., Lundqvist, K., & Yates, A. (2020). Recommender systems for MOOCs: A systematic literature survey (January 1, 2012–July 12, 2019). International Review of Research in Open and Distributed Learning, 21(4), 255–291. https://doi.org/10.19173/irrodl.v21i4.4643
- Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2020). A systematic review: Machine learning based recommendation systems for e-learning. Education and Information Technologies, 25, 2635–2664. https://doi.org/10.1007/s10639-019-10063-9
- Khorasani, E. S., Zhenge, Z., & Champaign, J. (2016, December). A Markov chain collaborative filtering model for course enrollment recommendations. In 2016 IEEE International Conference on Big Data (pp. 3484–3490). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData.2016.7841011
- Kitchenham, B. (2004). Procedures for performing systematic reviews: Joint technical report (Keele University Technical Report TR/SE-0401 and NICTA Technical Report 0400011T.1). Keele University and National ICT Australia.. https://libguides.library.arizona.edu/ld.php?content_id=49906992
- Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: A survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44, 571–604. https://doi.org/10.1007/s10462-015-9440-z
- Li, Q., & Kim, J. (2021). A deep learning-based course recommender system for sustainable development in education. Applied Sciences, 11(19), 8993. https://doi.org/10.3390/app11198993
- Li, X., Li, X., Tang, J., Wang, T., Zhang, Y., & Chen, H. (2020). Improving deep item-based collaborative filtering with Bayesian personalized ranking for MOOC course recommendation. In Proceedings of Knowledge Science, Engineering and Management: 13th International Conference, KSEM 2020 (Part I 13, pp. 247–258), Hangzhou, China. Springer. https://doi.org/10.1007/978-3-030-55130-8_22
- Ma, B., Taniguchi, Y., & Konomi, S. I. (2020). Course recommendation for university environments. In Proceedings of the 13th International Conference on Educational Data Mining, EDM 2020. https://educationaldatamining.org/files/conferences/EDM2020/papers/paper_90.pdf
- Man, M., Xu, J., Sabri, I. A. A., & Li, J. (2022). Research on students’ course selection preference based on collaborative filtering algorithm. International Journal of Advanced Computer Science and Applications, 13(5). https://dx.doi.org/10.14569/IJACSA.2022.0130583
- Morsy, S., & Karypis, G. (2019). Will this course increase or decrease your gpa? Towards grade-aware course recommendation. arXiv preprint arXiv:1904.11798. https://doi.org/10.48550/arXiv.1904.11798
- Ng, Y. K., & Linn, J. (2017, August). CrsRecs: A personalized course recommendation system for college students. In 8th International Conference on Information, Intelligence, Systems & Applications (pp. 1–6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IISA.2017.8316368
- Nguyen, V. A., Nguyen, H. H., Nguyen, D. L., & Le, M. D. (2021). A course recommendation model for students based on learning outcome. Education and Information Technologies, 26, 5389–5415. https://doi.org/10.1007/s10639-021-10524-0
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hrobjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L.A., Thomas, J., & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021, 272(71).
- Pang, Y., Liu, W., Jin, Y., Peng, H., Xia, T., & Wu, Y. (2018). Adaptive recommendation for MOOC with collaborative filtering and time series. Applications in Engineering Education, 26(6), 2071–2083. https://doi.org/10.1002/cae.21995
- Pardos, Z. A., Fan, Z., & Jiang, W. (2019). Connectionist recommendation in the wild: On the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-Adapted Interaction, 29, 487–525. https://doi.org/10.1007/s11257-019-09218-7
- Pardos, Z. A., & Jiang, W. (2020, March). Designing for serendipity in a university course recommendation system. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (pp. 350–359). https://doi.org/10.1145/3375462.3375524
- Premalatha, M., Viswanathan, V., & Čepová, L. (2022). Application of semantic analysis and LSTM-GRU in developing a personalized course recommendation system. Applied Sciences, 12(21), 10792. https://doi.org/10.3390/app122110792
- Reparaz, C., Aznárez-Sanado, M., & Mendoza, G. (2020). Self-regulation of learning and MOOC retention. Computers in Human Behavior, 111, 106423. https://doi.org/10.1016/j.chb.2020.106423
- Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
- Sakboonyarat, S., & Tantatsanawong, P. (2022). Applied big data technique and deep learning for massive open online courses (MOOCs) recommendation system. ECTI Transactions on Computer and Information Technology, 16(4), 436–447. https://doi.org/10.37936/ecti-cit.2022164.245873
- Salazar, C., Aguilar, J., Monsalve-Pulido, J., & Montoya, E. (2021). Affective recommender systems in the educational field. A systematic literature review. Computer Science Review, 40, 100377. https://doi.org/10.1016/j.cosrev.2021.100377
- Salehudin, N. B., Kahtan, H., Abdulgabber, M. A., & Al-bashiri, H. (2019). A proposed course recommender model based on collaborative filtering for course registration. International Journal of Advanced Computer Science and Applications, 10(11). https://dx.doi.org/10.14569/IJACSA.2019.0101122
- Symeonidis, P., & Malakoudis, D. (2019). Multi-modal matrix factorization with side information for recommending massive open online courses. Expert Systems with Applications, 118, 261–271. https://doi.org/10.1016/j.eswa.2018.09.053
- Tan, J., Chang, L., Liu, T., & Zhao, X. (2020, October). Attentional autoencoder for course recommendation in mooc with course relevance. In 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (pp. 190–196). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CyberC49757.2020.00038
- Tarus, J. K., Niu, Z., & Mustafa, G. (2018). Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50, 21–48. https://doi.org/10.1007/s10462-017-9539-5
- Uddin, I., Imran, A. S., Muhammad, K., Fayyaz, N., & Sajjad, M. (2021). A systematic mapping review on MOOC recommender systems. IEEE Access, 9, 118379–118405. https://doi.org/10.1109/ACCESS.2021.3101039
- Urdaneta-Ponte, M. C., Méndez-Zorrilla, A., & Oleagordia-Ruiz, I. (2021). Lifelong learning courses recommendation system to improve professional skills using ontology and machine learning. Applied Sciences, 11(9), 3839. https://doi.org/10.3390/app11093839
- Wang, X., Cui, L., Bangash, M., Bilal, M., Rosales, L., & Chaudhry, W. (2022). A machine learning-based course enrollment recommender system. In Proceedings of the 14th International Conference on Computer Supported Education (Vol. 1, pp. 436–443). https://doi.org/10.5220/0011109100003182
- Wang, Y. (2022). Research on online learner modeling and course recommendation based on emotional factors. Scientific Programming, 2022. https://doi.org/10.1155/2022/5164186
- Xia, T. (2019, August). An e-learning support middleware with MOOC course recommendation. In Proceedings of the 14th International Conference on Computer Science & Education (pp. 596–600). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCSE.2019.8845533
- Xu, G., Jia, G., Shi, L., & Zhang, Z. (2021). Personalized course recommendation system fusing with knowledge graph and collaborative filtering. Computational Intelligence and Neuroscience, 2021, 1–8. https://doi.org/10.1155/2021/9590502
- Xu, W., & Zhou, Y. (2020). Course video recommendation with multimodal information in online learning platforms: A deep learning framework. British Journal of Educational Technology, 51(5), 1734–1747. https://doi.org/10.1111/bjet.12951
- Yago, H., Clemente, J., & Rodriguez, D. (2018). Competence-based recommender systems: A systematic literature review. Behaviour & Information Technology, 37(10–11), 958–977. https://doi.org/10.1080/0144929X.2018.1496276
- Yang, Q., Yuan, P., & Zhu, X. (2018). Research of personalized course recommended algorithm based on the hybrid recommendation. In MATEC Web of Conferences (Vol. 173, p. 03067). EDP Sciences. https://doi.org/10.1051/matecconf/201817303067
- Yang, S., & Cai, X. (2022). Bilateral knowledge graph enhanced online course recommendation. Information Systems, 107, 102000. https://doi.org/10.1016/j.is.2022.102000
- Yang, X., & Jiang, W. (2019). Dynamic online course recommendation based on course network and user network. In Proceedings of Smart City and Informatization: 7th International Conference (Vol. 7, pp. 180–196), Guangzhou, China. Springer. https://doi.org/10.1007/978-981-15-1301-5_15
- Yanhui, D., Dequan, W., Yongxin, Z., & Lin, L. (2015, November). A group recommender system for online course study. In 7th International Conference on Information Technology in Medicine and Education (pp. 318–320). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITME.2015.99
- Yin, S., Yang, K., & Wang, H. (2020, May). A MOOC courses recommendation system based on learning behaviours. In Proceedings of the ACM Turing Celebration Conference: China (pp. 133–137). https://doi.org/10.1145/3393527.3393550
- Zhang, H., Huang, T., Lv, Z., Liu, S., & Yang, H. (2019). MOOCRC: A highly accurate resource recommendation model for use in MOOC environments. Mobile Networks and Applications, 24, 34–46. https://doi.org/10.1007/s11036-018-1131-y
- Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., & Sun, J. (2019, July). Hierarchical reinforcement learning for course recommendation in MOOCs. In Proceedings of the AAAI conference on Artificial Intelligence (Vol. 33, No. 01, pp. 435–442). https://doi.org/10.1609/aaai.v33i01.3301435
- Zhao, Z., Yang, Y., Li, C., & Nie, L. (2020). GuessUNeed: Recommending courses via neural attention network and course prerequisite relation embeddings. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(4), 1–17. https://doi.org/10.1145/3410441
- Zhou, J., Jiang, G., Du, W., & Han, C. (2022). Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation. Electronic Commerce Research, 23, 1–21. https://doi.org/10.1007/s10660-022-09541-z
- Zhu, Y., Lu, H., Qiu, P., Shi, K., Chambua, J., & Niu, Z. (2020). Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization. Neurocomputing, 415, 84–95. https://doi.org/10.1016/j.neucom.2020.07.064