Collaborative Prediction of Examinee Performance based on Fuzzy Cognitive Diagnosis via cloud model

The prediction of examinee performance via cognitive diagnosis models might provide an important decision-making support for personalized learning instruction in an e-learning system. Aiming at the uncertainty of learners' skill proficiency caused by the complexity of skills, and the large-scale volume of score profiles, a collaborative prediction approach of examinee performance is proposed based on a new fuzzy cloud cognitive diagnosis model. In this approach, the normal cloud models are used to measure the uncertainty of the skill proficiency from three aspects (i.e., expectation, variation degree, and variation frequency), and an e-learner’s skill proficiency is characterized with a fuzzy interval number. Based on a collaborative parameter estimation method, the predicted scores on every test item could be obtained for learners. Finally, the experiments demonstrate that this approach provides good accuracy and less execution time for predicting examinee performance than other approaches.