Prediction of Students' Performance Based on Cognitive Diagnosis and XGBoost

QIN Yajie; LIU Mengchi; HU Jie; FENG Jiamei

doi:10.6054/j.jscnun.2023005

Journal of South China Normal University (Natural Science Edition) > 2023 > 55(1): 55-64. > DOI: 10.6054/j.jscnun.2023005

QIN Yajie, LIU Mengchi, HU Jie, FENG Jiamei. Prediction of Students' Performance Based on Cognitive Diagnosis and XGBoost[J]. Journal of South China Normal University (Natural Science Edition), 2023, 55(1): 55-64. DOI: 10.6054/j.jscnun.2023005

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Prediction of Students' Performance Based on Cognitive Diagnosis and XGBoost

1.
Department of Computer Science, South China Normal University, Guangzhou 510631, China
2.
Department of Computer and Information Engineering, Hubei University, Wuhan 430062, China

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Received Date: October 11, 2021
Available Online: April 11, 2023

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Abstract

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.
- cognitive diagnosis,
- matrix factorization,
- XGBoost algorithm,
- student performance prediction

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