Abstract
Learning path recommender systems are emerging. Given the popularity of ontology/knowledge-based systems in adaptive learning, this work reviews learning path in ontology-based recommender systems. The review covers recommendation trends, ontology use, recommendation process, recommendation technique, contributing factors, and recommender evaluations. A total of 12,972 articles published between 2010 and 2020 were identified in the initial search across five major databases, and 9 of them are considered in this work. Currently, student model, learning objects, learning activities, and external environment are contributing factors for recommending learning object sequence. We also found that the current trend for LP recommendations process is semi-dynamic and dynamic. Semi-dynamic learning path are started by a pre-set path, while dynamic learning path is flexible from the first step and intended for personal use. The recommendation process itself has four phases: predelivery of the first learning object, current learning object delivery, learning object postdelivery, and predelivery of the next learning object. The current recommendation technique collaborates ontology and several techniques, such as Bayesian networks, data mining, and other artificial intelligence technique. To evaluate performance, learning path recommender systems use real students, control groups in parallel or sequential experiments, and student satisfaction surveys. Ontology could work with knowledge representation instruments, educational psychology, and evolutionary computation to create a future dynamic learning path in adaptive learning environment.
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Data availability
The data file for this systematic review will be made available upon reasonable academic request by the corresponding author upon acceptance.
Abbreviations
- ACO:
-
Ant Colony Optimization
- ADL:
-
Advanced Distributed Learning
- AR:
-
Augmented Reality
- CAI:
-
Computer-Aided Instruction
- CDT:
-
Context Dimension Tree
- GA:
-
Genetic Algorithm
- ITS:
-
Intelligent Tutoring System
- LMS:
-
Learning Management System
- LP:
-
Learning Path
- LO:
-
Learning Object
- PRISMA:
-
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
- VLE:
-
Virtual Learning Environment
References
Abdullatif, H., & Velázquez-Iturbide, J. (2020). Relationship between motivations, personality traits and intention to continue using MOOCs. Education and Information Technologies, 25, 4417–4435. https://doi.org/10.1007/s10639-020-10161-z
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99
Aggarwal, C. C. (2016). Recommender systems. Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-29659-3
Al-Muhaideb, S., & Menai, M. E. B. (2011). Evolutionary computation approaches to the curriculum sequencing problem. Natural Computing, 10(2), 891–920. https://doi.org/10.1007/s11047-010-9246-5
Al-Yahya, M., George, R., & Alfaries, A. (2015). Ontologies in e-learning: Review of the literature. International Journal of Software Engineering and Its Applications, 9(2), 67–84. https://doi.org/10.14257/ijseia.2015.9.2.07
Bouhdidi, J. E., Ghailani, M., & Fennan, A. (2013). An intelligent architecture for generating evolutionary personalized learning paths based on learner profiles. Journal of Theoretical and Applied Information Technology, 57(2), 294–304.
Boyce, S., & Pahl, C. (2007). Developing domain ontologies for course content. Journal of Educational Technology & Society, 10(3), 275–288. https://www.jstor.org/stable/jeductechsoci.10.3.275. Accessed 24 June 2019
Bremgartner, V. (2015). Adaptation resources in virtual learning environments under constructivist approach: A systematic review. Proceedings of the 2015 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/FIE.2015.7344316
Buitrago, M., & Chiappe, A. (2019). Representation of knowledge in digital educational environments: A systematic review of literature. Australasian Journal of Educational Technology, 35(4), 46–62. https://doi.org/10.14742/ajet.404162
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12, 331–370. https://doi.org/10.1023/A:1021240730564
Capuano, N., & Toti, D. (2019). Experimentation of a smart learning system for law based on knowledge discovery and cognitive computing. Computers in Human Behavior, 92, 459–467. https://doi.org/10.1016/j.chb.2018.03.034
Clemente, J., Ramírez, J., & de Antonio, A. (2014). Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction. Expert Systems with Applications, 41(2), 508–520. https://doi.org/10.1016/j.eswa.2013.07.077
Colace, F., & Santo, M. (2010). Ontology for e-learning: A Bayesian approach. IEEE Transactions on Education, 53(2), 223–233. https://doi.org/10.1109/TE.2009.2012537
Colace, F., de Santo, M., Lombardi, M., Mosca, R., & Santaniello, D. (2020). A multilayer approach for recommending contextual learning paths. Journal of Internet Services and Information Security (JISIS), 2(May), 91–102. https://doi.org/10.22667/JISIS.2020.05.31.091
Cortinovis, R., Mikroyannidis, A., Domingue, J., Mulholland, P., & Farrow, R. (2019). Supporting the discoverability of open educational resources. Education and Information Technologies, 24, 3129–3161. https://doi.org/10.1007/s10639-019-09921-3
Dahman, M. R., & Dahman, S. (2020). Decision support model to help language teachers grouping adult learners in a classroom. Education and Information Technologies, 25, 4329–4352. https://doi.org/10.1007/s10639-020-10153-z
Devedzic, V. (2006). Semantic web and education (12 vol.). Springer Science & Business Media. https://doi.org/10.1007/978-0-387-35417-0
Fidalgo-Blanco, A., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2015). Methodological approach and technological framework to break the current limitations of MOOC model. Journal of Universal Computer Science, 21, 712–734.
George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 1–18. https://doi.org/10.1016/j.compedu.2019.103642
Grubišić, A., Stankov, S., & Peraić, I. (2013). Ontology based approach to Bayesian student model design. Expert Systems with Applications, 40(13), 5363–5371. https://doi.org/10.1016/j.eswa.2013.03.041
Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2018). “Let’s set up some subgoals”: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11(1), 54–66. https://doi.org/10.1109/TLT.2017.2756629
Hnida, M., Idrissi, M. K., & Bennani, S. (2014). A formalism of the competency-based approach in adaptive learning systems. WSEAS Transactions on Information Science and Applications, 11, 83–93.
Hsieh, T. C., & Wang, T. I. (2010). A mining-based approach on discovering courses pattern for constructing suitable learning path. Expert Systems with Applications, 37(6), 4156–4167. https://doi.org/10.1016/j.eswa.2009.11.007
Huang, M. J., Huang, H. S., & Chen, M. Y. (2007). Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33(3), 551–564. https://doi.org/10.1016/j.eswa.2006.05.019
Huang, R., Spector, J. M., & Yang, J. (2019). Lecture notes in educational technology. Springer Nature Singapore. https://doi.org/10.1007/978-981-13-6643-7
Iatrellis, O., Kameas, A., & Fitsilis, P. (2019). EDUC8 ontology: Semantic modeling of multi-facet learning pathways. Education and Information Technologies, 24, 2371–2390. https://doi.org/10.1007/s10639-019-09877-4
Iglesias, A., Martínez, P., Aler, R., & Fernández, F. (2004). Learning content sequencing in an educational environment according to student needs. Algorithmic learning theory. ALT 2004. Lecture notes in computer science (3244 vol., pp. 454–463). Springer-Verlag. https://doi.org/10.1007/978-3-540-30215-5_34.
ISO/IEC/IEEE (2017). ISO/IEC/IEEE International Standard - Systems and software engineering–Vocabulary. In ISO/IEC/IEEE 24765:2017(E). https://doi.org/10.1109/IEEESTD.2017.8016712
Jeng, Y., & Huang, Y. M. (2019). Dynamic learning paths framework based on collective intelligence from learners. Computers in Human Behavior, 100(September 2018), 242–251. https://doi.org/10.1016/j.chb.2018.09.012
Jevremovic, A., Shimic, G., Veinovic, M., & Ristic, N. (2017). IP addressing: Problem-based learning approach on computer networks. IEEE Transactions on Learning Technologies, 10(3), 367–378. https://doi.org/10.1109/TLT.2016.2583432
Kardan, A. A., Aziz, M., & Shahpasand, M. (2015). Adaptive systems: A content analysis on technical side for e-learning environments. Artificial Intelligence Review, 44(3), 365–391. https://doi.org/10.1007/s10462-015-9430-1
Katuk, N., Kim, J., & Ryu, H. (2013). Experience beyond knowledge: Pragmatic e-learning systems design with learning experience. Computers in Human Behavior, 29(3), 747–758. https://doi.org/10.1016/j.chb.2012.12.014
Klašnja-Milicevic, A., Ivanovic, 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
Kurilovas, E., & Juskeviciene, A. (2015). Creation of web 2. 0 tools ontology to improve learning. Computers in Human Behavior, 51, 1380–1386. https://doi.org/10.1016/j.chb.2014.10.026
Kurilovas, E., Kubilinskiene, S., & Dagiene, V. (2014). Web 3.0 – Based personalisation of learning objects in virtual learning environments. Computers in Human Behavior, 30, 654–662. https://doi.org/10.1016/j.chb.2013.07.039
Labib, A. E., Canós, J. H., & Penadés, M. C. (2017). On the way to learning style models integration: A learner’s characteristics ontology. Computers in Human Behavior, 73, 433–445. https://doi.org/10.1016/j.chb.2017.03.054
Laudon, K. C., & Laudon, J. P. (2012). In B. Horan (Ed.), Management information systems: Managing the digital firm (12th ed.). Pearson Education, Inc.
Leyendecker, R. (2012). Curriculum and learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp.896–900). Springer Science + Business Media. https://doi.org/10.1007/978-1-4419-1428-6_1617
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32. https://doi.org/10.1016/j.dss.2015.03.008
Machado, M., de Bravo, O. C., Martins, N. F. S., Bernardino, A. F., Barrere, H. S., & de Souza, J. F. (2021). Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artificial Intelligence Review, 54(1), 711–754. https://doi.org/10.1007/s10462-020-09864-z
Magnisalis, I., Demetriadis, S., & Karakostas, A. (2011). Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies, 4(1), 5–20. https://doi.org/10.1109/TLT.2011.2
Manouselis, N., Drachsler, H., Verbert, K., & Duval, E. (2013). Survey and analysis of TEL recommender systems. In Recommender systems for learning (pp. 37–61). https://doi.org/10.1007/978-1-4614-4361-2_3
Mariño, B. D. R., Rodríguez-fórtiz, M. J., Torres, M. V. H., & Haddad, H. M. (2018). Accessibility and activity-centered design for ICT users: ACCESIBILITIC ontology. IEEE Access: Practical Innovations, Open Solutions, 6, 60655–60665. https://doi.org/10.1109/ACCESS.2018.2875869
Middleton, S. E., de Roure, D., & Shadbolt, N. R. (2009). Ontology-based recommender systems. In S. Staab & R. Studer (Eds.), Handbook on ontologies, international handbooks on information systems (pp. 1648–1686). Springer-Verlag. https://doi.org/10.1007/978-3-540-92673-3_35
Monti, D., Rizzo, G., Morisio, M., & Monti, D. (2020). A systematic literature review of multicriteria recommender systems. In Artificial intelligence review (Issue 0123456789). Springer Netherlands. https://doi.org/10.1007/s10462-020-09851-4
Moreno-Marcos, P. M., Alario-Hoyos, C., Munoz-Merino, P. J., & Kloos, C. D. (2019). Prediction in MOOCs: A review and future research directions. IEEE Transactions on Learning Technologies, 12(3), 384–401. https://doi.org/10.1109/TLT.2018.2856808
Muhammad, A. H., Zhou, Q., Beydoun, G., Xu, D., & Shen, J. (2016). Learning path adaptation in online learning systems. 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 421–426. https://doi.org/10.1109/CSCWD.2016.7566026
Nabizadeh, A. H., Leal, J. P., Rafsanjani, H. N., & Shah, R. R. (2020). Learning path personalization and recommendation methods: A survey of the state-of-the-art. Expert Systems with Applications, 159, 1–20. https://doi.org/10.1016/j.eswa.2020.113596
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., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71
Pepin, B., & Kock, Z. (2021). Students’ use of resources in a challenge-based learning context involving mathematics. International Journal of Research in Undergraduate Mathematics Education, 7(2), 306–327. https://doi.org/10.1007/s40753-021-00136-x
Porcel, C., Ching-López, A., Lefranc, G., Loia, V., & Herrera-Viedma, E. (2018). Sharing notes: An academic social network based on a personalized fuzzy linguistic recommender system. Engineering Applications of Artificial Intelligence, 75, 1–10. https://doi.org/10.1016/j.engappai.2018.07.007
Premlatha, K. R., & Geetha, T. V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4), 443–465. https://doi.org/10.1007/s10462-015-9432-z
Rahayu, N. W., Ferdiana R., & Kusumawardani, S. S. (2021). Model of nonlinear learning path using heutagogy. Proceeding of International Conference on Teaching, Assessment, and Learning for Engineering (TALE 2021), 1–6. https://doi.org/10.1109/TALE52509.2021.9678642
Rahayu, N. W., Ferdiana, R., & Kusumawardani, S. S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100047
Rasheed, F., & Wahid, A. (2019). Sequence generation for learning: a transformation from past to future. The International Journal of Information and Learning Technology, 36(5), 434–452. https://doi.org/10.1108/IJILT-01-2019-0014
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58. https://doi.org/10.1145/245108.245121
Robberecht, R. (2007). Interactive nonlinear learning environments. The Electronic Journal of E-Learning, 5(1), 59–68.
Romero, L., Saucedo, C., Caliusco, M. L., & Gutiérrez, M. (2019). Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths. International Journal of Educational Technology in Higher Education, 16(1), 16. https://doi.org/10.1186/s41239-019-0146-1
Salehi, M., Kamalabadi, I. N., & Ghoushchi, M. B. G. (2013). An effective recommendation framework for personal learning environments using a learner preference tree and a GA. IEEE Transactions on Learning Technologies, 6(4), 350–363. https://doi.org/10.1109/TLT.2013.28
Siren, A., & Tzerpos, V. (2022). Automatic learning path creation using OER: A systematic literature mapping. IEEE Transactions on Learning Technologies, 15(4), 493–507. https://doi.org/10.1109/TLT.2022.3193751
Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1–2), 161–197. https://doi.org/10.1016/S0169-023X(97)00056-6
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(1), 21–48. https://doi.org/10.1007/s10462-017-9539-5
Tibaná-herrera, G., Fernández-bajón, M. T., Moya-anegón, F., & De (2018). Categorization of E-learning as an emerging discipline in the world publication system: a bibliometric study in. International Journal of Educational Technology in Higher Education, 15(21). https://doi.org/10.1186/s41239-018-0103-4.
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014
Vesin, B., Klasnja-Milicevic, A., Mirjana, I., & Budimac, Z. (2013). Applying recommender systems and adaptive hypermedia for e-learning personalization. Computing and Informatics, 32, 629–659.
Yago, H., Clemente, J., Rodriguez, D., & Fernandez-de-Cordoba, P. (2018). ON-SMMILE: Ontology network-based student model for multiple learning environments. Data & Knowledge Engineering, 115, 48–67. https://doi.org/10.1016/j.datak.2018.02.002
Zapata-Ros, M. (2006). Sequencing of contents and learning objects: Part II. Revista de Educación a Distancia, V(14), 1–15. http://www.um.es/ead/red/14/. Accessed 3 Oct 2021
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Rahayu, N.W., Ferdiana, R. & Kusumawardani, S.S. A systematic review of learning path recommender systems. Educ Inf Technol 28, 7437–7460 (2023). https://doi.org/10.1007/s10639-022-11460-3
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DOI: https://doi.org/10.1007/s10639-022-11460-3