Knowledge gap extraction based on the learner click behavior in interaction with videos using the association rule algorithm

Learner's Knowledge gap detection is one of the important issues in learner's knowledge assessment. The knowledge gap is the gap between actual knowledge of the educational concepts and that received by the learner from them. This paper presents a new method to calculate the knowledge gap of each concept of instructional videos based on the learner click behavior. Many studies have analyzed learner behavior based on click behavior., but one of the main issues in event analysis is to identify the amount of knowledge learned by the learner and communicated between the actual concept and that perceived by the learner. One of the main goals of knowledge gap extraction is to detect students at risk and help them to be on the right path of the learning process. In this paper., rules are proposed based on click behavior of learners using Apriori Algorithm. Furthermore., the knowledge gap for each group of learners is calculated based on the behavioral classification. The test project is done on 52 students in the microprocessor course at the e-learning center., University of Tehran. The proposed method is evaluated and then a number of rules are extracted in this study.