DEVELOPMENT OF AN ONTOLOGY-BASED ADAPTIVE PERSONALIZED E-LEARNING SYSTEM
Olutayo BOYINBODE
okboyinbode@futa.edu.ngThe Federal University of Technology, School of Computing, Department of Information Technology, FUTA Rd, Akure (Nigeria)
Paul OLOTU
The Federal University of Technology, School of Computing, Department of Information Technology, FUTA Rd, Akure (Nigeria)
Kolawole AKINTOLA
The Federal University of Technology, School of Computing, Department of Information Technology, FUTA Rd, Akure (Nigeria)
Abstract
E-learning has fast become an active field of research with a lot of investments towards web-based delivery of personalized learning contents to learners. Some issues of e-learning arise from the heterogeneity and interoperability of learning content adapting to learner's styles and preferences. This has brought about the development of an ontology-based personalized learning system to solve this problem. This research developed an ontology-based personalized e-learning system that presents suitable learning contents to learners based on their learning style, preferences, background knowledge, and personal profile.
Keywords:
Felder-Silverman Learning Style Model, Item response theory, Ontology, Learning ability, Difficulty levelReferences
Abu-Sayf, F.K. (1979). The Scoring of Multiple-choice Tests: A Closer Look. Educational Technology, 19(6), 5–15.
Google Scholar
Adewale, O.S. (2006). University Digital libraries: an initiative for teaching, research, and service. Adeyemo Publishing House.
Google Scholar
Agbonifo, O., & Obolo, O. (2018). Genetic Algorithm-based Curriculum Sequencing Model for Personalized E-Learning System. I.J. Modern Education and Computer Science, 5, 27–35.
DOI: https://doi.org/10.5815/ijmecs.2018.05.04
Google Scholar
Baker, F. (2001). The Basics of Item Response Theory. University of Maryland, College Park, MD: ERIC Clearinghouse on Assessment and Evaluation.
Google Scholar
Baker, F.B. (1992). Item Response Theory: Parameter estimation techniques. Marcel Dekker.
Google Scholar
Beulah, C., Latha, C.B., & Kirubakaran, E. (2013). Personalized Learning Path Delivery in Web based Educational Systems using a Graph Theory based Approach. Computer Science, 55428839.
Google Scholar
Boyinbode, O., & Akintade, F. (2015). A Cloud Based Mobile Learning Interface. Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science (pp. 353–356). San Francisco, USA.
Google Scholar
Boyinbode, O., & Bagula, A. (2012). An Interactive Mobile Learning System for Enhancing Learning in Higher Education. Proceedings of the IADIS International Mobile Learning Conference Berlin (pp. 331–334). Germany.
Google Scholar
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling, and User–Adapted Interaction, 11, 87-110.
DOI: https://doi.org/10.1023/A:1011143116306
Google Scholar
Chen, C.M., & Chung, C.J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624–647.
DOI: https://doi.org/10.1016/j.compedu.2007.06.011
Google Scholar
Chi,Y. (2009). Ontology-based Curriculum Content Sequencing System with Semantic Rules. Expert Systems with Applications, 36(4), 7838–7847. https://doi.org/10.1016/j.eswa.2008.11.048
DOI: https://doi.org/10.1016/j.eswa.2008.11.048
Google Scholar
Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart, and Winston.
Google Scholar
Dag, F., & Gecer, A. (2009). Relations between online learning and learning styles. Procedia Social and Behavioral Sciences, 1, 862–871.
DOI: https://doi.org/10.1016/j.sbspro.2009.01.155
Google Scholar
Hambleton, R.K., Swaminathan, H., & Rogers, H.J. (1991). Fundamentals of Item Response Theory. Sage Publications.
Google Scholar
Hamzeh, M. (2005). Using Distractors in Correcting for Guessing in Multiple-Choice Tests. Educational Sciences, 32(1), 192–197.
Google Scholar
Kurilovas, E., Zilinskiene, I., & Dagiene, V. (2016). Recommending Suitable Learning Paths According to Learners’ Preferences: Experimental Research Results. Computers in Human Behavior, 51, 945–951.
DOI: https://doi.org/10.1016/j.chb.2014.10.027
Google Scholar
Uhomoibhi, J.O. (2006). Implementing e-learning in Northern Ireland: prospects and challenges. Campus-Wide Information Systems, 23(1), 4–14.
DOI: https://doi.org/10.1108/10650740610639697
Google Scholar
Yarandi, M., Jahankhani, H., & Tawil., A. (2013). A Personalized Adaptive e-learning approach based on semantic web technology. Computer Science, 6257403.
Google Scholar
Authors
Olutayo BOYINBODEokboyinbode@futa.edu.ng
The Federal University of Technology, School of Computing, Department of Information Technology, FUTA Rd, Akure Nigeria
Authors
Paul OLOTUThe Federal University of Technology, School of Computing, Department of Information Technology, FUTA Rd, Akure Nigeria
Authors
Kolawole AKINTOLAThe Federal University of Technology, School of Computing, Department of Information Technology, FUTA Rd, Akure Nigeria
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