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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 overcome the drawbacks of manually assigning weights, we propose an auto-generated concept relationship weighting method based on the co-occurrence between primary concepts and supporting concepts of 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 generated personalized remedial learning path can significantly enhance the learning achievement of underachieving students. Furthermore, the new auto-generated weighting method was able to eliminate the time taken to assign weights by experts and resolve conflicting weight values.

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