Exercise Recommendation Based on Cognitive Diagnosis and Neutrosophic Set
Hua Ma, Zhuoxuan Huang, Wensheng Tang, Xuxiang Zhang
Abstract
It is a fundamental function of a personalized elearning system to recommend suitable exercises to learners for improving their learning efficiencies and qualities. These exercises relevant to the current learning progress and the skill proficiency of learners could be selected by analyzing their score profiles in the past exams. Aiming at the limitations of existing research, a new exercise recommendation approach is proposed based on cognitive diagnosis and Neutrosophic set. In it, the learners' cognitive status is measured from multiple perspectives comprehensively by introducing the Neutrosophic set theory. The similarity between the learners is calculated with a Neutrosophic set method. The learner's performance on the new exercises could be predicted by collaborative filtering algorithm, and the exercises suitable to learners are recommended to them according to their preferences. The experiments show that the accuracy of proposed approach is higher than the existing approaches.