NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features
One of the most critical functions of modern intelligent teaching technology is cognitive diagnostics. Traditional cognitive diagnostic models (CDMs) usually use designed functions to deal with the linear interaction between students and exercises, but it is difficult to adequately deal with the com...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9806 |
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| author | Guangquan Li Yuqing Hu Junkai Shuai Tonghua Yang Yonghong Zhang Shiming Dai Naixue Xiong |
| author_facet | Guangquan Li Yuqing Hu Junkai Shuai Tonghua Yang Yonghong Zhang Shiming Dai Naixue Xiong |
| author_sort | Guangquan Li |
| collection | DOAJ |
| description | One of the most critical functions of modern intelligent teaching technology is cognitive diagnostics. Traditional cognitive diagnostic models (CDMs) usually use designed functions to deal with the linear interaction between students and exercises, but it is difficult to adequately deal with the complex relationship of non-linear interaction between students and exercises; moreover, existing cognitive diagnostic models often lack the integrated consideration of multiple features of exercises. To address these issues, this paper proposes a neural network cognitive diagnosis model (NeuralNCD) that incorporates multiple features. The model obtains more accurate diagnostic results by using neural networks to handle the nonlinear interaction between students and exercises. First, the student vector and the exercise vector are obtained through the Q-matrix; second, the multi-dimensional features of the exercises (e.g., difficulty, discrimination, guess and slip) are obtained using the neural network; finally, item response theory and a neural network are employed to characterize the interaction between the student and the exercise in order to determine the student’s cognitive state. At the same time, monotonicity assumptions and data preprocessing mechanisms are introduced into the neural network to improve the accuracy of the diagnostic results. Extensive experimental results on real world datasets present the effectiveness of NeuralNCD with regard to both accuracy and interpretability for diagnosing students’ cognitive states. The prediction accuracy (ACC), root mean square error (RMSE), and area under the curve (AUC) were 0.734, 0.425, and 0.776, respectively, which were about 2–10% higher than the related works in these evaluation metrics. |
| first_indexed | 2024-03-09T22:02:25Z |
| format | Article |
| id | doaj.art-3e0cc410c3704cdf9fa12a78bc5c2548 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:02:25Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3e0cc410c3704cdf9fa12a78bc5c25482023-11-23T19:46:15ZengMDPI AGApplied Sciences2076-34172022-09-011219980610.3390/app12199806NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional FeaturesGuangquan Li0Yuqing Hu1Junkai Shuai2Tonghua Yang3Yonghong Zhang4Shiming Dai5Naixue Xiong6School of Computer and Information Engineering, Jiangxi Agriculture University, Nanchang 330045, ChinaSchool of Computer and Information Engineering, Jiangxi Agriculture University, Nanchang 330045, ChinaSchool of Computer and Information Engineering, Jiangxi Agriculture University, Nanchang 330045, ChinaSchool of Vocational Teachers, Jiangxi Agriculture University, Nanchang 330045, ChinaSchool of Information Engineering, Jiangxi Vocational College of Mechanical & Electrical Technology, Nanchang 330013, ChinaSchool of Computer and Information Engineering, Jiangxi Agriculture University, Nanchang 330045, ChinaDepartment of Computer, Mathematical and Physical Sciences, Sul Ross State University, Alpine, TX 79830, USAOne of the most critical functions of modern intelligent teaching technology is cognitive diagnostics. Traditional cognitive diagnostic models (CDMs) usually use designed functions to deal with the linear interaction between students and exercises, but it is difficult to adequately deal with the complex relationship of non-linear interaction between students and exercises; moreover, existing cognitive diagnostic models often lack the integrated consideration of multiple features of exercises. To address these issues, this paper proposes a neural network cognitive diagnosis model (NeuralNCD) that incorporates multiple features. The model obtains more accurate diagnostic results by using neural networks to handle the nonlinear interaction between students and exercises. First, the student vector and the exercise vector are obtained through the Q-matrix; second, the multi-dimensional features of the exercises (e.g., difficulty, discrimination, guess and slip) are obtained using the neural network; finally, item response theory and a neural network are employed to characterize the interaction between the student and the exercise in order to determine the student’s cognitive state. At the same time, monotonicity assumptions and data preprocessing mechanisms are introduced into the neural network to improve the accuracy of the diagnostic results. Extensive experimental results on real world datasets present the effectiveness of NeuralNCD with regard to both accuracy and interpretability for diagnosing students’ cognitive states. The prediction accuracy (ACC), root mean square error (RMSE), and area under the curve (AUC) were 0.734, 0.425, and 0.776, respectively, which were about 2–10% higher than the related works in these evaluation metrics.https://www.mdpi.com/2076-3417/12/19/9806cognitive diagnosisneural networkdeep learningintelligent educational system |
| spellingShingle | Guangquan Li Yuqing Hu Junkai Shuai Tonghua Yang Yonghong Zhang Shiming Dai Naixue Xiong NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features Applied Sciences cognitive diagnosis neural network deep learning intelligent educational system |
| title | NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features |
| title_full | NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features |
| title_fullStr | NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features |
| title_full_unstemmed | NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features |
| title_short | NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features |
| title_sort | neuralncd a neural network cognitive diagnosis model based on multi dimensional features |
| topic | cognitive diagnosis neural network deep learning intelligent educational system |
| url | https://www.mdpi.com/2076-3417/12/19/9806 |
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