Article Sidebar

Download
PDF
Statistic
Read Counter : 964 Download : 911

Downloads

Download data is not yet available.

Main Article Content

Abstract

As online learning resources increase exponentially on the World Wide Web, online students have difficulty choosing the most suitable and relevant learning material that meets their learning needs due to information overload. Online learning recommendation system is used to predict the preferences or ranking of learners' targets on learning objects for the purpose of generating recommendations. However, the Recommendation system is considered to lack the ability to resolve semantic interoperability issues with heterogeneous sources of information. The purpose of this study is to discuss the role of ontology in the development of recommendation systems in the online learning domain. There are four electronic journal databases selected as references, namely IEEE, Science Direct, Springer Link, and ACM Digital Library. This study obtained 9 articles that were synthesized to answer research questions. This study shows that the involvement of ontology for knowledge representation in the recommendation process can improve the accuracy and quality of recommendations and at the same time help to overcome the weaknesses associated with conventional recommendations.

Keywords

Online Learning Ontology Recommender System

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. M. Ferreira-satler, F. P. Romero, V. H. Menendez, A. Zapata, and M. E. Prieto, “A fuzzy ontology approach to represent user profiles in e-learning environments,” in International Conference on Fuzzy Systems, 2010, pp. 1–8.
  2. Z. Gulzar, “Towards Recommending Courses in a Learner Centered System Using Query Classification Approach,” in International Conference on Advanced Computing and Communication Systems, 2017.
  3. H. Jeong, C. Choi, and Y. Song, “Expert Systems with Applications Personalized Learning Course Planner with E-learning DSS using user profile,” Expert Syst. Appl., vol. 39, no. 3, pp. 2567–2577, 2012.
  4. J. K. Tarus, Z. Niu, and A. Yousif, “A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining,” Futur. Gener. Comput. Syst., vol. 72, pp. 37–48, 2017.
  5. J. K. Tarus, Z. Niu, and G. Mustafa, “Knowledge-based recommendation : a review of ontology-based recommender systems for e-learning,” Artif. Intell. Rev., vol. 50, no. 1, pp. 21–48, 2018.
  6. B. Simeonov et al., “Semantic integration of web data for international investment decision support,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9934 LNCS, 2016.
  7. K. Bagherifard, M. Rahmani, M. Nilashi, and V. Rafe, “Telematics and Informatics Performance improvement for recommender systems using ontology,” Telemat. Informatics, vol. 34, no. 8, pp. 1772–1792, 2017.
  8. I. A. Wulandari, D. I. Sensuse, A. A. Krisnadhi, I. F. Akmaliah, and P. Rahayu, “Ontologies for Decision Support System: The Study of Focus and Techniques,” in International Conference on Information Technology and Electrical Engineering, 2018, pp. 609–614.
  9. M. Rospocher and L. Serafini, “Ontology-centric decision support,” Proc. Int. Work. Semant. Technol. meet Recomm. Syst. Big Data, vol. 919, pp. 61–67, 2012.
  10. R. Mohemad, A. R. Hamdan, N. M. M. Noor, and Z. A. Othman, “Ontological-based for supporting multi criteria decision-making,” ICIME 2010 - 2010 2nd IEEE Int. Conf. Inf. Manag. Eng., vol. 1, pp. 214–217, 2010.
  11. Blomqvist and Eva, “The use of Semantic Web technologies for decision support --a survey,” Semant. Web, vol. 5, no. 3, pp. 177–201, 2014.
  12. G. George and A. M. Lal, “Computers & Education Review of ontology-based recommender systems in e-learning,” Comput. Educ., vol. 142, no. July 2018, p. 103642, 2019.
  13. J. Shao, “Research on Fuzzy Ontology E-learning Based on User Profile,” in Association for Computing Machinery, 2017, pp. 49–52.
  14. B. Cheng, Y. Zhang, and D. Shi, “Ontology-based personalized learning path recommendation for course learning,” in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 2018, pp. 531–535.
  15. M. E. Ibrahim, Y. Yang, D. L. Ndzi, G. Yang, and M. Al-maliki, “Ontology-Based Personalized Course Recommendation Framework,” IEEE Access, vol. 7, pp. 5180–5199, 2019.
  16. B. Kitchenham, “Procedures for performing systematic reviews,” Keele, UK, Keele Univ., vol. 33, no. TR/SE-0401, p. 28, 2004.
  17. B. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature reviews in Software Engineering Version 2.3,” Engineering, vol. 45, no. 4ve, p. 1051, 2007.
  18. B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering - A systematic literature review,” Inf. Softw. Technol., vol. 51, no. 1, pp. 7–15, 2009.
  19. S. Staab and R. Studer, “Handbook on Ontologies,” Decis. Support Syst., p. 654, 2007.
  20. A. Huertas, M. Gil, F. J. García, and G. Martínez, “Design of a recommender system based on users ’ behavior and collaborative location and tracking,” J. Comput. Sci., vol. 12, pp. 83–94, 2016.
  21. A. Karpus, I. Vagliano, K. Goczyła, and M. Morisio, “An Ontology-based Contextual Pre-filtering Technique for Recommender Systems,” 2016 Fed. Conf. Comput. Sci. Inf. Syst., vol. 8, pp. 411–420, 2016.
  22. T. R. Gruber, “A translation approach to portable ontology specifications,” Knowl. Acquis., vol. 5, no. 2, pp. 199–220, 1993.
  23. I. F. Akmaliah, A. A. Krisnadhi, D. I. Sensuse, P. Rahayu, and I. A. Wulandari, “Role of Ontology and Machine Learning in Recommender Systems,” 2018 Electr. Power, Electron. Commun. Control. Informatics Semin., pp. 371–376, 2018.