Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
Cognitive diagnosis is one of the essential components in intelligent education and aims to diagnose student’s skill or knowledge mastery based on their responses. Recently, with the development of artificial intelligence, some researchers have applied neural network methods to cognitive diagnosis....
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/22/10380 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846154458966261760 |
|---|---|
| author | Jinhong Tao Wei Zhao Fengjuan Liu Xiaoqing Guo Nuo Cheng Qian Guo Xiaoqing Xu Hong Duan |
| author_facet | Jinhong Tao Wei Zhao Fengjuan Liu Xiaoqing Guo Nuo Cheng Qian Guo Xiaoqing Xu Hong Duan |
| author_sort | Jinhong Tao |
| collection | DOAJ |
| description | Cognitive diagnosis is one of the essential components in intelligent education and aims to diagnose student’s skill or knowledge mastery based on their responses. Recently, with the development of artificial intelligence, some researchers have applied neural network methods to cognitive diagnosis. Although they achieved some success, they seemed to lack a certain basis for designing network structures and could not obtain a unified method for designing network structures. We propose a neural network method for cognitive diagnosis based on Q-matrix constraints, introducing the Q-matrix from traditional cognitive diagnosis to enhance the reliability and interpretability of the network structure. Specifically, our method is highly consistent with generalized deterministic inputs, the noisy “and” gate model (GDINA), and the network structure reflects the direct contribution of skills to answering questions correctly, as well as the indirect contribution of interactions between skills to answering questions correctly. Finally, extensive experiments on both simulated and real datasets demonstrated that our method achieved high accuracy and reliability, with a particularly notable performance on low-quality datasets. As the number of questions and skills increased, our approach exhibited greater robustness compared to the classical methods, highlighting its potential for broad applicability in cognitive diagnosis tasks. |
| format | Article |
| id | doaj-art-5932a67799e740e597a8527e749c7c64 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5932a67799e740e597a8527e749c7c642024-11-26T17:48:32ZengMDPI AGApplied Sciences2076-34172024-11-0114221038010.3390/app142210380Cognitive Diagnosis Method via Q-Matrix-Embedded Neural NetworksJinhong Tao0Wei Zhao1Fengjuan Liu2Xiaoqing Guo3Nuo Cheng4Qian Guo5Xiaoqing Xu6Hong Duan7School of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, ChinaSchool of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, ChinaSchool of Educational Science, Shaanxi University of Technology, 1 Dongyi, Hanzhong 723001, ChinaSchool of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, ChinaSchool of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, ChinaSchool of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, ChinaSchool of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, ChinaTeachers College, Shihezi University/Bingtuan Education Institute, 221 North 4th Road, Shihezi 832003, ChinaCognitive diagnosis is one of the essential components in intelligent education and aims to diagnose student’s skill or knowledge mastery based on their responses. Recently, with the development of artificial intelligence, some researchers have applied neural network methods to cognitive diagnosis. Although they achieved some success, they seemed to lack a certain basis for designing network structures and could not obtain a unified method for designing network structures. We propose a neural network method for cognitive diagnosis based on Q-matrix constraints, introducing the Q-matrix from traditional cognitive diagnosis to enhance the reliability and interpretability of the network structure. Specifically, our method is highly consistent with generalized deterministic inputs, the noisy “and” gate model (GDINA), and the network structure reflects the direct contribution of skills to answering questions correctly, as well as the indirect contribution of interactions between skills to answering questions correctly. Finally, extensive experiments on both simulated and real datasets demonstrated that our method achieved high accuracy and reliability, with a particularly notable performance on low-quality datasets. As the number of questions and skills increased, our approach exhibited greater robustness compared to the classical methods, highlighting its potential for broad applicability in cognitive diagnosis tasks.https://www.mdpi.com/2076-3417/14/22/10380cognitive diagnosis assessmentdiagnostic classification modelsQ-matrixmachine learningpsychometricscognitive tests |
| spellingShingle | Jinhong Tao Wei Zhao Fengjuan Liu Xiaoqing Guo Nuo Cheng Qian Guo Xiaoqing Xu Hong Duan Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks Applied Sciences cognitive diagnosis assessment diagnostic classification models Q-matrix machine learning psychometrics cognitive tests |
| title | Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks |
| title_full | Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks |
| title_fullStr | Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks |
| title_full_unstemmed | Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks |
| title_short | Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks |
| title_sort | cognitive diagnosis method via q matrix embedded neural networks |
| topic | cognitive diagnosis assessment diagnostic classification models Q-matrix machine learning psychometrics cognitive tests |
| url | https://www.mdpi.com/2076-3417/14/22/10380 |
| work_keys_str_mv | AT jinhongtao cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT weizhao cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT fengjuanliu cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT xiaoqingguo cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT nuocheng cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT qianguo cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT xiaoqingxu cognitivediagnosismethodviaqmatrixembeddedneuralnetworks AT hongduan cognitivediagnosismethodviaqmatrixembeddedneuralnetworks |