Prediction of Students' Performance Based on Cognitive Diagnosis and XGBoost

Aiming at the problem that the cognitive diagnosis method does not consider the commonality of students' answers and the matrix factorization method does not consider the individuality of students' knowledge acquisition, a student performance prediction method (PRNCD-XGBoost) combining cognitive diagnosis and XGBoost (eXtreme Gradient Boosting) is proposed. Firstly, explore the similarity between knowledge according to the co-occurrence relationship of knowledge in exercises, and the weight of each knowledge in the exercise is explored in combination with the exercise-knowledge bipartite graph to make cognitive diagnosis. Then, the students' historical score matrix is filled with the results of cognitive diagnosis. Finally, the non-negative matrix factorization method is used to extract the common features of students including cognitive diagnostic factors for score prediction. The PRNCD-XGBoost was compared with PMF, NeuralCD, PR-NCD, NMF-XGBoost, MNMF- XGBoost methods on the datasets of ASSISTments2009 and ASSISTments2017. The experimental results show that the PRNCD-XGBoost method has higher accuracy in predicting students' performance.