Creating Individualized Learning Paths For Self-regulated Online Learners: An Ontology-driven Approach
Abstract
This study extends the ontology-supported modeling of prior studies in learning path personalization to an ontology-driven modeling approach to create a mechanism of online learning path adaptivity. This mechanism is especially applicable for self-regulated learners such as those in flipped learning context. The proposed ontology modeling is based on a conceptualization of education being the function of the triplet of knowledge structure (guide), knowledge content (material), and instruction (teaching). In addition, this study defines cognitive learning as the mapping of the learner's personal knowledge structure to the domain knowledge structure. Furthermore, online learning is viewed as the interaction between the learning management system and the learner. With these conceptualizations, a domain ontology is constructed based on the Common Core State Standards (CCSS) for Mathematics curriculum guide; and a task ontology is constructed to model the problem of learning path adaptivity by including the teaching activity, learning material, and the learner classes. In the case experiment, the Protégé tool is used to construct the ontologies and the semantic rules. The experiment results show that the created mechanism of ontological learning path adaptivity has successfully guided the learner to pre-requisite learning activities and learning objects for remedial learning when current learning activities result in unsatisfactory assessment results. © 2014 Springer International Publishing.
Recommended Citation
Chi, Y. L., Chen, T. Y., & Tsai, W. T. (2014). Creating Individualized Learning Paths For Self-regulated Online Learners: An Ontology-driven Approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8528 LNCS, pp. 546-555. Springer.
The definitive version is available at https://doi.org/10.1007/978-3-319-07308-8_52
Department(s)
Business and Information Technology
Keywords and Phrases
e-Learning; Learning path; Ontology; Self-regulated learning
International Standard Book Number (ISBN)
978-331907307-1
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2014
