Ontology-Based Learner Categorization through Case Based Reasoning and Fuzzy Logic
International Association for Development of the Information Society (IADIS) International Conference on E-Learning,
Learner categorization has a pivotal role in making e-learning systems a success. However, learner characteristics exploited at abstract level of granularity by contemporary techniques cannot categorize the learners effectively. In this paper, an architecture of e-learning framework has been presented that exploits the machine learning based techniques for learner categorization taking into account the cognitive and inclinatory attributes of learners at finer level of granularity. Learner attributes are subjected to a pre-processing mechanism for taking into account the most important ones out of initial attribute set. Subsequently, couple of machine learning techniques namely Fuzzy Logic and Case Based Reasoning was employed on attributes selected for learner categorization. To best of our knowledge, these techniques have not been employed so far in learner categorization with quality of data and adaptivity while targeting semantic web. [For the complete proceedings, see ED579335.]
Sarwar, S., García-Castro, R., Qayyum, Z.U., Safyan, M. & Munir, R.F. (2017). Ontology-Based Learner Categorization through Case Based Reasoning and Fuzzy Logic. Presented at International Association for Development of the Information Society (IADIS) International Conference on E-Learning 2017.