Knowledge Gap Extraction Based on Learner Interaction with Training Videos

Abstract

In recent years, with the advancement of information technology in education, e-learning quality promotion has received increased attention. Numerous criteria exist for promoting learning quality, such as fitness for purpose, which refers to the extent to which service fits its intended purpose. Multiple purposes are considered in e-learning. One is reducing the knowledge gap between the learner's perception of educational concepts and what should be understood of training concepts. Identifying and calculating the learner's knowledge gap is the first step in reducing the knowledge gap. Consequently, this paper presents a new method for calculating the learner's knowledge gap concerning each concept in the training video content based on the learner's click behavior. The association between the learner's knowledge gap and click behavior was determined by categorizing the learner's click behaviors. Similarly, the Apriori algorithm extracted rules for each behavioral category. The results demonstrated that learning outcome correlated with the learner's click behavior. Therefore, four behavioral rules regarding the compatibility between the knowledge gap and learner's click behavior are presented. Experiments were performed by 52 students enrolled in the micro-processing course at Tehran University's e-Learning Center.