An adult learner's knowledge model based on ontologies and rule reasoning

Abstract

One of the biggest challenges of Adaptive e-learning systems is learner modelling. The learner model should represent the learner's characteristics as faithfully as possible in order to provide adaptive learning. Among these characteristics, the learner's knowledge is considered to be the core characteristic of the learner model, as adaptive e-learning systems are centered on the learner's knowledge, since acquiring « knowledge » about a specific domain or concept is considered the main goal of learning and instruction. In this paper, we propose a novel adult learner's knowledge model using ontologies and rule reasoning, by trying to define the different components that construct the learner's knowledge in an exhaustive yet a simple way. The proposed model takes into account different elements of the learner's knowledge, such as the different knowledge types and categories, the learner's prior knowledge accumulated through his/her experiences, his/her misconceptions, errors and the previously learned but forgotten knowledge.