Cognitive diagnosis method based on meta-knowledge dictionary learning

A technology of dictionary learning and diagnostic methods, applied in the field of cognitive diagnosis, can solve problems such as inability to distinguish topics, high overlap of knowledge points, and small number of abstract concepts, so as to achieve good generalization ability, improve the effect of clustering, and improve learning The effect of skills

Active Publication Date: 2020-09-15
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] However, the number of abstract concepts proposed in this literature is very small, and there is a problem that the granularity of knowledge points is not detailed enough, resulting in a high degree of overlap of knowledge points, so that it is impossible to distinguish the topics and students cannot be grouped when clustering students. distinguish

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  • Cognitive diagnosis method based on meta-knowledge dictionary learning
  • Cognitive diagnosis method based on meta-knowledge dictionary learning
  • Cognitive diagnosis method based on meta-knowledge dictionary learning

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Embodiment

[0104] The data used in this embodiment are two public data sets in the field of education: (reference Wu, Runze, et.al."Cognitive modeling for predicting examinee performance."Twenty-FourthInternational Joint Conference on Artificial Intelligence.2015.) and collection The results of the final exam of C language in the international class of a university in 2018.

[0105] 1. Establish the student answer matrix Y based on the known data, and decompose the matrix Y according to the method of step 1:

[0106] Y=sign(DX+W)

[0107] 2. Establish the objective function according to step 2;

[0108] 3. Solve the objective function according to the solution method in step 3, where the scaling factor α is set to 10, and the number of meta-knowledge points is set to 500. When solving formula (12), this optimization problem is decomposed into Q sub-optimization problems according to the number of questions Q, and each sub-problem is a continuously derivable function, and the derivative...

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Abstract

The invention discloses a cognitive diagnosis method based on meta-knowledge dictionary learning. The cognitive diagnosis method includes the steps of constructing an over-complete meta-knowledge dictionary is constructed, and expressing the answering situation of students on the dictionary; supposing that knowledge points mastered by students and knowledge points involved in questions are linearly composed of meta-knowledge points, and sparsely representing knowledge structure states of the students through a meta-knowledge dictionary; under the condition that expert knowledge points are known, learning and obtaining a relation matrix of the expert knowledge points and meta-knowledge points; and obtaining the mastering condition of each student for the expert knowledge points by utilizingthe product of the sparse representation matrix of the students and the meta-knowledge points and the relation matrix of the expert knowledge points and the meta-knowledge points. Besides, the students with the same knowledge structure are clustered into one class by using the sparse representation matrix of the students so that the students can be better grouped and the student clustering effectcan be enhanced.

Description

technical field [0001] The invention belongs to the field of knowledge mining, and in particular relates to a cognitive diagnosis method. Background technique [0002] The document "Sparse Factor Analysis for Learning and Content Analytics, the Journal of Machine Learning Research, 15(1), 1959-2008." discloses a method based on matrix factorization, which can automatically learn knowledge concepts from data, so that the Students answer right and wrong data to diagnose students' mastery of knowledge points. Abstract knowledge concepts are set in the literature, and 5 abstract concepts with the best effect are selected from them. At the same time, it is assumed that whether students can answer the questions correctly is related to whether students have mastered the knowledge points appearing in the questions. Therefore, the student's answer matrix Y is decomposed into the product of the topic knowledge point relationship matrix W and the knowledge point student relationship ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/20G06N5/02G06F16/906
CPCG06Q50/205G06N5/022G06N5/027G06F16/906Y02A90/10
Inventor 代欢张育培云岳刘树慧尚学群崔嘉琪安蕊
Owner NORTHWESTERN POLYTECHNICAL UNIV
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