Generating Concept Effect Relationship Based On Frequency Of Concept's Co-Occurrence Using Concept-Rank Algorithms

Understanding a topic in a course mainly consists of many concepts, which is challenging for students. It is because, usually, those concepts need to be conveyed with information on the relationships between concepts to solve the problem. Students' different limits, backgrounds, and goals have made research regarding concepts become even more challenging. Researchers have applied various learning path personalization strategies employing multiple methods and techniques, including depicting the connections between topic concepts. However, the strategy has several problems to solve, one of which is the main and exciting is the process of generating connections between concepts which is seen as a weighted-directed graph. This research offers a new practical and adaptive method for constructing Concept Effect Relationship (CER), a more specific concept map, by identifying the co-occurrence frequency of concepts hidden in a set of questions. It is proven that it is possible to extract the hidden concept when it appears simultaneously in the question item. It can be used to determine the weight and direction of each concept's node to generate CERs.