A Knowledge-based Adaptive Algorithm to Recommend Interactive Learning Assessments

The popularity of m-learning has created endless possibilities for improving education. Mobile based educational applications assist to enhance knowledge, provide personalized learning experience, support interactivity and accessibility to different learning content. Among many different features in m-learning applications, competency-based adaptive delivery of content is an area that is still under study. This study presents a rule-based algorithm to dynamically recommend competencybased interactive assessments to learners. In our approach, the competency levels are calculated using constraints concerning the learner and the assessments, which are then used to determine the difficulty level of the succeeding assessment. This adaptive algorithm obtained a success rate of 86.11% from the evaluation.