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.