Towards a Personalized Adaptive Remedial e-Learning Model

Recently, demand for database programming specialists has greatly increased in Kenya. These professionals play a key role in the computing and software development industries. Although database programming skills are key fundamentals for learners in computing disciplines, skills mastery by students is still not easy. For these reasons, this study establishes an adaptive remedial learning model to assist learners in their quest of gaining skills online. The proposed solution adopts the use of fuzzy logic theory to create an appropriate learning path based on the learners' prior concepts miscomprehensions. This technique selects a suitable remedial materials for learners after constructing a learning path based on the learners' preference. After evaluation of the model through conducting several experiments, it is proposed that it can be used to offer a comprehensive and stable remedial learning environment for any LMS. Analysis of the model by learners confirm that it has achieved the effects of remedial and adaptive learning.