Résumés
Abstract
This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners’ performance through ontological machine-learning techniques and, using a systematic approach, identifies the existing methodologies and tools used to forecast students’ performance. In addition, the environment for generating ontologies, as considered by academics in the field, is likewise identified. Based on the inclusion criteria and by adopting PRISMA as a research methodology, seven studies and two systematic reviews were selected. The findings reveal a scarcity of research devoted to ontologies in the prediction of learners’ achievement. However, the research outcomes suggest that building an ontological model to harness machine-learning capabilities could help accurately predict students’ academic performance. The results of this systematic review are useful for higher education institutes and curriculum planners. This is especially pertinent in online learning settings to avoid dropout or failure. Also highlighted in this study are numerous possible directions for future research.
Keywords:
- data mining,
- decision tree,
- education,
- ontology,
- Semantic Web,
- classification algorithm
Parties annexes
Bibliography
- Abdullah, S. R. A., & Al-Azawei, A. (2024). Enhancing the early prediction of learners’ performance in a virtual learning environment. In A. M. Al-Bakry, M. A. Sahib, S. O. Al-Mamory, J. A. Aldhaibani, A. N. Al-Shuwaili, H. S. Hasan, R. A. Hamid, & A. K. Idrees (Eds.), New trends in information and communications technology applications: 7th national conference, NTICT 2023, proceedings (pp. 252–266). Springer. https://doi.org/10.1007/978-3-031-62814-6_18
- Al-Azawei, A., & Al-Masoudy, M. A. A. (2020). Predicting learners’ performance in virtual learning environment (VLE) based on demographic, behavioral and engagement antecedents. International Journal of Emerging Technologies in Learning (IJET), 15(9), 60–75. https://doi.org/10.3991/ijet.v15i09.12691
- Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). The effect of universal design for learning (UDL) application on e-learning acceptance: A structural equation model. The International Review of Research in Open and Distributed Learning, 18(6), 54–87. https://doi.org/10.19173/irrodl.v18i6.2880
- Al-Azawei, A. H. S. (2017). Modelling e-learning adoption: The influence of learning style and universal learning theories [Doctoral dissertation, University of Reading]. CentAUR: Central Archive at the University of Reading. https://centaur.reading.ac.uk/77921/
- Al-Chalabi, H. K. M., & Hussein, A. M. A. (2020, June). Ontology applications in e-learning systems. In Proceedings of the 12th International Conference on Electronics, Computers and Artificial Intelligence—ECAI—2020 (pp. 1–6). IEEE. https://doi.org/10.1109/ECAI50035.2020.9223135
- Al-Masoudy, M. A. A., & Al-Azawei, A. (2023). Proposing a feature selection approach to predict learners’ performance in virtual learning environments (VLES). International Journal of Emerging Technologies in Learning (iJET), 18(11), 110–131. https://doi.org/10.3991/ijet.v18i11.35405
- 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://www.earticle.net/Article/A242009
- Aslam, N., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An improved early student’s academic performance prediction using deep learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), 108–122. https://doi.org/10.3991/ijet.v16i12.20699
- Boufardea, E., & Garofalakis, J. (2012). A predictive system for distance learning based on ontologies and data mining. In T. Bossomaier & S. Nolfi (Eds.), COGNITIVE: Proceedings of 4th International Conference on Advanced Cognitive Technologies and Applications (pp. 151–158). International Academy, Research and Industry Association. https://personales.upv.es/thinkmind/dl/conferences/cognitive/cognitive_2012/cognitive_2012_7_30_40125.pdf
- Chweya, R., Shamsuddin, S. M., Ajibade, S. S., & Moveh, S. (2020). A literature review of student performance prediction in E-learning environment. Journal of Science, Engineering, Technology and Management, 1(1), 22–36.
- Costa, L. A., Nascimento Salvador, L. D., & Amorim, R. R. (2018). Evaluation of academic performance based on learning analytics and ontology: A systematic mapping study. In J. Rhee (Chair), 2018 IEEE Frontiers in Education Conference (pp. 1–5). IEEE. https://doi.org/10.1109/FIE.2018.8658936
- Costa, L. A., Pereira Sanches, L. M., Rocha Amorim, R. J., Nascimento Salvador, L. D., & Santos Souza, M. V. D. (2020). Monitoring academic performance based on learning analytics and ontology: A systematic review. Informatics in Education, 19(3), 361–397. https://doi.org/10.15388/infedu.2020.17
- Costa, L. A., Souza, M., Salvador, L. N., Silveira, A. C., & Saibel, C. A. (2021). Students’ perceptions of academic performance in distance education evaluated by learning analytics and ontologies. In Anais do XXXII Simpósio Brasileiro de Informática na Educação—SBIE 2021 (pp. 91–102). https://doi.org/10.5753/sbie.2021.218423
- El Bolock, A., Abdennadher, S., & Herbert, C. (2021). An ontology-based framework for psychological monitoring in education during the COVID-19 pandemic. Frontiers in Psychology, 12, Article 673586. https://doi.org/10.3389/fpsyg.2021.673586
- El Aissaoui, O., & Oughdir, L. (2020). A learning style-based ontology matching to enhance learning resources recommendation. In B. Benhala, K. Mansouri, A. Raihani, M. Qbadou, & N. El Makhfi (Eds.), 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology—IRASET 2020 (pp. 1–7). IEEE. https://doi.org/10.1109/IRASET48871.2020.9092142
- El-Rady, A. A. (2020). An ontological model to predict dropout students using machine learning techniques. In 3rd ICCAIS 2020 International Conference on Computer Applications & Information Security (pp. 1–5). IEEE. https://doi.org/10.1109/ICCAIS48893.2020.9096743
- El-Rady, A. A., Shehab, M., & El Fakharany, E. (2017). Predicting learner performance using data-mining techniques and ontology. In A. E. Hassanien, K. Shaalan, T. Gaber, A. T. Azar, & M.F. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (pp. 660–669). Springer. https://doi.org/10.1007/978-3-319-48308-5_63
- George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, Article 103642. https://doi.org/10.1016/j.compedu.2019.103642
- Grivokostopoulou, F., Perikos, I., & Hatzilygeroudis, I. (2014, December). Utilizing semantic web technologies and data mining techniques to analyze students learning and predict final performance. In D. Carnegie (Chair), TALE 2014—Proceedings of IEEE International Conference on Teaching, Assessment and Learning for Engineering (pp. 488–494). IEEE. https://doi.org/10.1109/TALE.2014.7062571
- Hamim, T., Benabbou, F., & Sael, N. (2021). An ontology-based decision support system for multi-objective prediction tasks. International Journal of Advanced Computer Science and Applications, 12(12), 183–191. https://doi.org/10.14569/IJACSA.2021.0121224
- Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence & Neuroscience, 2018, Article 6347186. https://doi.org/10.1155/2018/6347186
- Icoz, K., Sanalan, V. A., Cakar, M. A., Ozdemir, E. B., & Kaya, S. (2015). Using students’ performance to improve ontologies for intelligent e-learning system. Educational Sciences: Theory and Practice, 15(4), 1039–1049. https://jestp.com/menuscript/index.php/estp/article/view/661/598
- Kara, M. (2020, May 21). Influential readers [Review of the book Distance education: A systems view of online learning, by M. G. Moore & G. Kearsley]. Educational Review, 72(6), 800. https://doi.org/10.1080/00131911.2020.1766204
- Khalilian, S. (2019). A survey on ontology evaluation methods. Digital and Smart Libraries Researches, 6(2), 25–34. https://doi.org/10.30473/mrs.2020.48615.1402
- Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Med, 6(7), Article e1000100. https://doi.org/10.1371/journal.pmed.1000100
- Liu, F., Ziden, A. A., & Liu, B. (2024). Emotional support in online teaching and learning environment: A Systematic literature Review (2014–2023). Journal of Curriculum and Teaching, 13(4), 209–218. https://doi.org/10.5430/jct.v13n4p209
- López-Zambrano, J., Lara, J. A., & Romero, C. (2022). Improving the portability of predicting students’ performance models by using ontologies. Journal of Computing in Higher Education, 34, 1–19. https://doi.org/10.1007/s12528-021-09273-3
- Mogus, A. M., Djurdjevic, I., & Suvak, N. (2012). The impact of student activity in a virtual learning environment on their final mark. Active Learning in Higher Education, 13(3), 177–189. https://doi.org/10.1177/1469787412452985
- Nafea, S., Maglaras, L. A., Siewe, F., Smith, R., & Janicke, H. (2016). Personalized students’ profile based on ontology and rule-based reasoning. EAI Endorsed Transactions on E-Learning, 3(12), Article e6. https://doi.org/10.4108/eai.2-12-2016.151720
- 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, March 29). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71
- Pelap, G. F., Zucker, C. F., & Gandon, F. (2018). Semantic models in Web-based educational system integration. In M. J. Escalona, F. D. Mayo, T. Majchrzak, & V. Monfort (Eds.), Proceedings of the 14th International Conference on Web Information Systems and Technologies—WEBIST (pp. 78–89). SciTePress. https://doi.org/10.5220/0006940000780089
- Prinsloo, P., Slade, S., & Khalil, M. (2022). Introduction: Learning analytics in open and distributed learning—Potential and challenges. In P. Prinsloo, S. Slade, & M. Khalil (Eds.), Learning analytics in open and distributed learning (pp. 1–13). Springer. https://doi.org/10.1007/978-981-19-0786-9_1
- Qiu, F., Zhang, G., Sheng, X., Jiang, L., Zhu, L., Xiang, Q., Jiang, B., & Chen, P.-K. (2022). Predicting students’ performance in e-learning using learning process and behaviour data. Nature: Scientific Reports, 12, Article 453. https://doi.org/10.1038/s41598-021-03867-8
- Raad, J., & Cruz, C. (2015). A survey on ontology evaluation methods. In A. Fred, J. Dietz, D. Aveiro, K. Liu, & J. Filipe (Eds.), Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (vol. 2, pp. 179–186). SciTePress. https://doi.org/10.5220/0005591001790186
- 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, Article 100047. https://doi.org/10.1016/j.caeai.2022.100047
- Rami, S., Bennani, S., & Idrissi, M. K. (2018). A novel ontology-based automatic method to predict learning style using Felder-Silverman model. In ITHET 2018—17th International Conference on Information Technology Based Higher Education and Training (pp. 1–5). IEEE. https://doi.org/10.1109/ITHET.2018.8424774
- Sultana, J., Rani, M. U., & Farquad, M. A. H. (2019). Student’s performance prediction using deep learning and data mining methods. International Journal of Recent Technology and Engineering (IJRTE), 8(1S4), 1018–1021.
- Wang, Y., & Wang, Y. (2021). A survey of ontologies and their applications in e-learning environments. Journal of Web Engineering, 20(6), 1675–1720. https://doi.org/10.13052/jwe1540-9589.2061
- Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9, Article 11. https://doi.org/10.1186/s40561-022-00192-z
- Zainuddin, N. M., Mohamad, M., Zakaria, N. Y., Sulaiman, N. A., Jalaludin, N. A., & Omar, H. (2024). Trends and benefits of online distance learning in the English as a second language context: A systematic literature review. Arab World English Journal, 10, 284–301. https://doi.org/10.24093/awej/call10.18
- Zeebaree, S. R. M., Al-Zebari, A., Jacksi, K., & Selamat, A. (2019). Designing an ontology of e-learning system for Duhok Polytechnic University using Protégé OWL tool. Journal of Advanced Research in Dynamical and Control Systems, 11(5), 24–37. https://jardcs.org/abstract.php?id=984