Generation of a learning path in e-learning environments: literature review
Abstract
Education is moving into an e-learning environment, displacing contact and face-to-face learning. However, current e-learning environments cannot still personalise when creating e-learning paths. Identifying existing solutions’ problems and limitations is critical to generate new, more advanced ideas for creating personalised e-learning paths. The literature analysis, for which 28 articles for 2018–2022 were used, describes the existing solutions used to adapt and optimise e-learning. The article provides an overview of existing research in the field of personalisation of e-learning systems and the creation of e-learning trajectories, proposes the development of a taxonomy of studied methods for recommending and forming individual learning trajectories, analysis of the practices described in the articles to identify the most commonly used of them. Limitations, problems and unresolved issues in previous studies are summarised and provide information for further work on improving the results obtained and for choosing the direction of future research, which is given in the final part of the article.
Keyword : adaptive e-learning, personalisation, e-learning path generation, optimisation, literature review
How to Cite
Ovtšarenko, O. (2023). Generation of a learning path in e-learning environments: literature review. New Trends in Computer Sciences, 1(1), 32–50. https://doi.org/10.3846/ntcs.2023.18278
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Apoki, U., Hussein, A., Al-Chalabi, H., Badica, C., & Mocanu, M. (2022). The role of pedagogical agents in personalised adaptive learning: A review. Sustainability, 14(11), 6442. https://doi.org/10.3390/su14116442
Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). Recommendation system for adaptive learning. Applied Psychological Measurement, 42(1), 24–41. https://doi.org/10.1177/0146621617697959
Cochrane. (2022). Systematic review standards. Background to systematic reviews, university libraries. Retrieved August 4, 2022, from https://www.cochranelibrary.com/about/about-cochrane-reviews
Delgado-von-Eitzen, C., Anido-Rifón, L., & Fernández-Iglesias, I. (2021). Blockchain applications in education: A systematic literature review. Applied Sciences, 11(24), 11811. https://doi.org/10.3390/app112411811
Diwan, C., Srinivasa, S., & Ram, P. (2019). automatic generation of coherent learning pathways for open educational resources. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Lecture notes in computer science: Vol. 11722. Transforming learning with meaningful technologies. EC-TEL 2019 (pp. 321–334). Springer. https://doi.org/10.1007/978-3-030-29736-7_24
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development of students’ engagement. International Journal of Educational Technology in Higher Education, 18, 53. https://doi.org/10.1186/s41239-021-00289-4
Felder, R., & Silverman, L. (1988). Learning and teaching styles in engineering education. Journal of Engineering Education, 78(7), 674–681.
Felder, R., & Soloman, B. (2022). Index of learning styles questionnaire. North Carolina State University, USA. Retrieved December 10, 2022, from https://www.webtools.ncsu.edu/learningstyles/
Gao, J., Liu, Q., & Huang, W. (2021, January). Learning path generator based on knowledge graph. In 12th International Conference on E-Education, E-Business, E-Management, and E-Learning, (pp. 27–33). https://doi.org/10.1145/3450148.3450155
Jiang, B., Li, X., Yang, S., Kong, Y., Cheng, W., Hao, C., & Lin, Q. (2022). Data-driven personalized learning path planning based on cognitive diagnostic assessments in MOOCs. Applied Sciences, 12(8), 3982. https://doi.org/10.3390/app12083982
Kalibatienė, D., & Miliauskaitė, J. (2021). Hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development. Informatica, 32(1), 85–118. https://doi.org/10.15388/21-INFOR444
Kausar, S., Xu, H., Hussain, I., Zhu, W., & Zahid, M. (2018). Integration of data mining clustering approach with the personalized e-learning system. Preprints 2018, 2018080350. https://doi.org/10.20944/preprints201808.0350.v2
Levanova, E., Berezhnaya, I., Fedorov, V., Tarasuk, N., Krivotulova, E., & Pankova, T. (2019). Individual learning path for future specialists’ development. TEM Journal, 8(4), 1384–1391.
Li, Y., Shao, Z., Wang, X., Zhao, X., & Guo, Y. (2018). Concept map-based learning paths automatic generation algorithm for adaptive learning systems. IEEE Access, 7, 245–255. https://doi.org/10.1109/ACCESS.2018.2885339
Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050. https://doi.org/10.1016/j.caeai.2022.100050
Nabizadeh, A. H., Gonçalves, D., Gama, S., Jorge, J., & Rafsanjani, H. (2020). Adaptive learning path recommender approach using auxiliary learning objects. Computers & Education, 147, 103777. https://doi.org/10.1016/j.compedu.2019.103777
Navarro, A., & Moreno-Ger, P. (2018). Comparison of clustering algorithms for learning analytics with educational datasets. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 9–16. https://doi.org/10.9781/ijimai.2018.02.003
Rahayu, R., Ferdiana, R., & Kusumawardani, S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047. https://doi.org/10.1016/j.caeai.2022.100047
Ramanauskaitė, S., & Slotkienė, A. (2019). Hierarchy-based competency structure and its application in e-evaluation. Applied Sciences, 9(17), 3478. https://doi.org/10.3390/app9173478
Ramos, D., Ramos, I. M., Gasparini, I., & Teixeira de Oliveira, E. (2021). A new learning path model for e-learning systems. International Journal of Distance Education Technologies, 19(2). https://doi.org/10.4018/IJDET.20210401.oa2
Rasheed, F., & Wahid, A. (2019). Sequence generation for learning: a transformation from past to future. International Journal of Information and Learning Technology, 36(5), 434–452. https://doi.org/10.1108/IJILT-01-2019-0014
Roth, S. & Tagge, N. (2022). Systematic review learning outcomes for researchers. Retrieved August 4, 2022, from https://guides.temple.edu/systematicreviews
Safitri, S., Setiadi, H., & Suryani, E. (2022). Educational data mining using cluster analysis methods and decision trees based on log mining. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(3). https://doi.org/10.29207/resti.v6i3.3935
Sanchez Nigenda, R., Maya Padrón, C., Martínez-Salazar, I., & Torres-Guerrero, F. (2018). Design and evaluation of planning and mathematical models for generating learning paths. Computational Intelligence, 34(3), 821–838. https://doi.org/10.1111/coin.12134
Shi, D., Wang, T., Xing, H., & Xu, H. (2020). A learning path recommendation model based on a knowledge graph framework for e-learning. Knowledge-Based Systems, 195, 105618. https://doi.org/10.1016/j.knosys.2020.105618
Son, N. T., Jaafar, J., Aziz, I. A., & Anh, B. N. (2021). Meta-heuristic algorithms for learning path recommender at MOOC. IEEE Access, 9, 20985134. https://doi.org/10.1109/ACCESS.2021.3072222
Tavakoli, M., Elias, M., Kismihók, G., & Auer, S. (2021, April 12–16). Metadata analysis of open educational resources. In 11th International Learning Analytics and Knowledge Conference (LAK21), Irvine, CA, USA. ACM. https://doi.org/10.1145/3448139.3448208
Tseng, F. S. C., Yeh, C.-T., & Chou, A. Y. H. A (2022). Collaborative framework for customized e-learning services by analytic hierarchy processing. Applied Sciences, 12, 1377. https://doi.org/10.3390/app12031377
Vagale, E. V., Niedrite, L., & Ignatjeva, S. (2020). Application of the recommended learning path in the personalized adaptive e-learning system. Baltic Journal Modern Computing, 8(4), 618–637. https://doi.org/10.22364/bjmc.2020.8.4.10
Vanitha, V., Krishnan, P., & Elakkiya, R. (2019). Collaborative optimisation algorithm for learning path construction in E-learning. Computers & Electrical Engineering, 77, 325–338. https://doi.org/10.1016/j.compeleceng.2019.06.016
Wei, X., Sun, S., Wu, D., & Zhou, L. (2021). Personalized online learning resource recommendation based on artificial intelligence and educational psychology. Frontiers in Psychology, 12, 767837. https://doi.org/10.3389/fpsyg.2021.767837
Xiao, Q., Zhang, Y., Song, Y., & Chen, M. (2022). Learning path optimization based on multi-attribute matching and variable length continuous representation [preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-1400545/v1
Zaoudi, M., & Belhadaoui, H. (2020, March). Adaptive E-learning: Adaptation of content according to the continuous evolution of the learner during his training. In NISS2020: Proceedings of the 3rd International Conference on Networking, Information Systems & Security (Article No. 71, pp. 1–6). https://doi.org/10.1145/3386723.3387890
Zhang, B., Chai, C., Yin, Z., & Shi, Y. (2021). Design and implementation of an EEG-based learning-style recognition mechanism. Brain Sciences, 11(5), 613. https://doi.org/10.3390/brainsci11050613
Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). Recommendation system for adaptive learning. Applied Psychological Measurement, 42(1), 24–41. https://doi.org/10.1177/0146621617697959
Cochrane. (2022). Systematic review standards. Background to systematic reviews, university libraries. Retrieved August 4, 2022, from https://www.cochranelibrary.com/about/about-cochrane-reviews
Delgado-von-Eitzen, C., Anido-Rifón, L., & Fernández-Iglesias, I. (2021). Blockchain applications in education: A systematic literature review. Applied Sciences, 11(24), 11811. https://doi.org/10.3390/app112411811
Diwan, C., Srinivasa, S., & Ram, P. (2019). automatic generation of coherent learning pathways for open educational resources. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Lecture notes in computer science: Vol. 11722. Transforming learning with meaningful technologies. EC-TEL 2019 (pp. 321–334). Springer. https://doi.org/10.1007/978-3-030-29736-7_24
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development of students’ engagement. International Journal of Educational Technology in Higher Education, 18, 53. https://doi.org/10.1186/s41239-021-00289-4
Felder, R., & Silverman, L. (1988). Learning and teaching styles in engineering education. Journal of Engineering Education, 78(7), 674–681.
Felder, R., & Soloman, B. (2022). Index of learning styles questionnaire. North Carolina State University, USA. Retrieved December 10, 2022, from https://www.webtools.ncsu.edu/learningstyles/
Gao, J., Liu, Q., & Huang, W. (2021, January). Learning path generator based on knowledge graph. In 12th International Conference on E-Education, E-Business, E-Management, and E-Learning, (pp. 27–33). https://doi.org/10.1145/3450148.3450155
Jiang, B., Li, X., Yang, S., Kong, Y., Cheng, W., Hao, C., & Lin, Q. (2022). Data-driven personalized learning path planning based on cognitive diagnostic assessments in MOOCs. Applied Sciences, 12(8), 3982. https://doi.org/10.3390/app12083982
Kalibatienė, D., & Miliauskaitė, J. (2021). Hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development. Informatica, 32(1), 85–118. https://doi.org/10.15388/21-INFOR444
Kausar, S., Xu, H., Hussain, I., Zhu, W., & Zahid, M. (2018). Integration of data mining clustering approach with the personalized e-learning system. Preprints 2018, 2018080350. https://doi.org/10.20944/preprints201808.0350.v2
Levanova, E., Berezhnaya, I., Fedorov, V., Tarasuk, N., Krivotulova, E., & Pankova, T. (2019). Individual learning path for future specialists’ development. TEM Journal, 8(4), 1384–1391.
Li, Y., Shao, Z., Wang, X., Zhao, X., & Guo, Y. (2018). Concept map-based learning paths automatic generation algorithm for adaptive learning systems. IEEE Access, 7, 245–255. https://doi.org/10.1109/ACCESS.2018.2885339
Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050. https://doi.org/10.1016/j.caeai.2022.100050
Nabizadeh, A. H., Gonçalves, D., Gama, S., Jorge, J., & Rafsanjani, H. (2020). Adaptive learning path recommender approach using auxiliary learning objects. Computers & Education, 147, 103777. https://doi.org/10.1016/j.compedu.2019.103777
Navarro, A., & Moreno-Ger, P. (2018). Comparison of clustering algorithms for learning analytics with educational datasets. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 9–16. https://doi.org/10.9781/ijimai.2018.02.003
Rahayu, R., Ferdiana, R., & Kusumawardani, S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047. https://doi.org/10.1016/j.caeai.2022.100047
Ramanauskaitė, S., & Slotkienė, A. (2019). Hierarchy-based competency structure and its application in e-evaluation. Applied Sciences, 9(17), 3478. https://doi.org/10.3390/app9173478
Ramos, D., Ramos, I. M., Gasparini, I., & Teixeira de Oliveira, E. (2021). A new learning path model for e-learning systems. International Journal of Distance Education Technologies, 19(2). https://doi.org/10.4018/IJDET.20210401.oa2
Rasheed, F., & Wahid, A. (2019). Sequence generation for learning: a transformation from past to future. International Journal of Information and Learning Technology, 36(5), 434–452. https://doi.org/10.1108/IJILT-01-2019-0014
Roth, S. & Tagge, N. (2022). Systematic review learning outcomes for researchers. Retrieved August 4, 2022, from https://guides.temple.edu/systematicreviews
Safitri, S., Setiadi, H., & Suryani, E. (2022). Educational data mining using cluster analysis methods and decision trees based on log mining. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(3). https://doi.org/10.29207/resti.v6i3.3935
Sanchez Nigenda, R., Maya Padrón, C., Martínez-Salazar, I., & Torres-Guerrero, F. (2018). Design and evaluation of planning and mathematical models for generating learning paths. Computational Intelligence, 34(3), 821–838. https://doi.org/10.1111/coin.12134
Shi, D., Wang, T., Xing, H., & Xu, H. (2020). A learning path recommendation model based on a knowledge graph framework for e-learning. Knowledge-Based Systems, 195, 105618. https://doi.org/10.1016/j.knosys.2020.105618
Son, N. T., Jaafar, J., Aziz, I. A., & Anh, B. N. (2021). Meta-heuristic algorithms for learning path recommender at MOOC. IEEE Access, 9, 20985134. https://doi.org/10.1109/ACCESS.2021.3072222
Tavakoli, M., Elias, M., Kismihók, G., & Auer, S. (2021, April 12–16). Metadata analysis of open educational resources. In 11th International Learning Analytics and Knowledge Conference (LAK21), Irvine, CA, USA. ACM. https://doi.org/10.1145/3448139.3448208
Tseng, F. S. C., Yeh, C.-T., & Chou, A. Y. H. A (2022). Collaborative framework for customized e-learning services by analytic hierarchy processing. Applied Sciences, 12, 1377. https://doi.org/10.3390/app12031377
Vagale, E. V., Niedrite, L., & Ignatjeva, S. (2020). Application of the recommended learning path in the personalized adaptive e-learning system. Baltic Journal Modern Computing, 8(4), 618–637. https://doi.org/10.22364/bjmc.2020.8.4.10
Vanitha, V., Krishnan, P., & Elakkiya, R. (2019). Collaborative optimisation algorithm for learning path construction in E-learning. Computers & Electrical Engineering, 77, 325–338. https://doi.org/10.1016/j.compeleceng.2019.06.016
Wei, X., Sun, S., Wu, D., & Zhou, L. (2021). Personalized online learning resource recommendation based on artificial intelligence and educational psychology. Frontiers in Psychology, 12, 767837. https://doi.org/10.3389/fpsyg.2021.767837
Xiao, Q., Zhang, Y., Song, Y., & Chen, M. (2022). Learning path optimization based on multi-attribute matching and variable length continuous representation [preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-1400545/v1
Zaoudi, M., & Belhadaoui, H. (2020, March). Adaptive E-learning: Adaptation of content according to the continuous evolution of the learner during his training. In NISS2020: Proceedings of the 3rd International Conference on Networking, Information Systems & Security (Article No. 71, pp. 1–6). https://doi.org/10.1145/3386723.3387890
Zhang, B., Chai, C., Yin, Z., & Shi, Y. (2021). Design and implementation of an EEG-based learning-style recognition mechanism. Brain Sciences, 11(5), 613. https://doi.org/10.3390/brainsci11050613