Concept–Effect Relationship Weighting Based on Frequency of Concept’s Co-Occurrence for Developing Personalized Remedial Learning Path

Notably, a personalized learning path is advantageous for students who need remedial assistance due to their struggle to master concepts and poor exam performance. Personalized learning paths produced from conceptual maps have proven highly effective. Nevertheless, in establishing personalized learning paths using conceptual maps, the manual assignment of weights that depict the degree of relationship between test items and concepts is time-consuming and ineffective. To get around the problems with giving weights by hand, we suggest an auto-generated concept relationship weighting method that is based on how often primary concepts and supporting concepts appear together in test items in this study. Moreover, we build a system that generates a personalized remedial learning path for students using the proposed weighting method and a conceptual map. To evaluate the effectiveness of the proposed approach, we carried out an experiment on high school students. As shown by the experimental results, the personalized remedial learning path generated can significantly enhance the learning achievement of underachieving students. In addition, the new auto-generated weighting method eliminated the need for experts to assign weights and resolved conflicting weight values.