Cross-modal problem Q matrix automatic construction method based on heterogeneous graph neural network

A neural network and automatic construction technology, applied in the field of expert systems, can solve problems such as inapplicability of cross-modality and loss of accuracy, and achieve the effects of reducing computational burden, improving accuracy, and increasing connections.

Active Publication Date: 2021-10-29
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The existing Q matrix automatic construction methods are usually aimed at single-modal problems that only contain text descriptions, and they have the following defects: ①It is not suitable

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  • Cross-modal problem Q matrix automatic construction method based on heterogeneous graph neural network
  • Cross-modal problem Q matrix automatic construction method based on heterogeneous graph neural network
  • Cross-modal problem Q matrix automatic construction method based on heterogeneous graph neural network

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[0127] DETAILED DESCRIPTION

[0128] Based on the across moderative problem Q matrix automatic construction method based on heterogeneous diagram neural network, the model consists of two sub-modules: a cross-media problem and knowledge point relationship module, a link prediction module based on heterogeneous neural network . Overall model diagram figure 1 As shown, specifically as follows:

[0129] 1. Construction of relationship map of cross-media issues and knowledge points;

[0130] In the field of education, the Q matrix refers to a binary matrix that displays the relationship between problems and knowledge points, wherein the row of Q matrices represents problems, and lists the knowledge points. The process of building a Q matrix is ​​also a process of looking for a corresponding relationship between problems and knowledge points. If the problem is regarded as different nodes, the correspondence between them can construct a heterogeneous map. The correspondence between the ...

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Abstract

The invention discloses a cross-modal problem Q matrix automatic construction method based on a heterogeneous graph neural network, and the method comprises the steps: constructing a heterogeneous graph containing cross-modal problems and knowledge points at the same time, building the connection between the problems and between the knowledge points according to the similarity, and proposing a heterogeneous graph neural network used for learning the node representation of the heterogeneous graph. And learning node feature representation in the heterogeneous graph and link prediction between problem nodes and knowledge point nodes, and finding out a corresponding relation between problems and knowledge points, so that the purpose of automatically constructing a Q matrix is achieved. The association between the problem and the knowledge point is directly searched in the heterogeneous graph through link prediction in the graph neural network, so that the operation burden of a computer is reduced. Besides, similarity information among the problems is introduced into the prediction process of the knowledge points, and the relation among the problem nodes is increased, so that the accuracy of subsequent knowledge point prediction is improved.

Description

technical field [0001] The invention belongs to the technical field of expert systems, and in particular relates to a method for automatically constructing a cross-modal problem Q matrix. Background technique [0002] Cognitive diagnosis is an important function of the online education system. It can automatically evaluate students' mastery of relevant knowledge points through students' answer scores, help teachers better analyze the deficiencies and defects of students' learning, and enable teachers to understand the importance of teaching. Insufficient and improved, better realize the combination of teaching and evaluation, and promote students to grow more effectively. The Q matrix is ​​composed of the relationship between questions and knowledge points, and it is an important basis for parameter estimation and result prediction of cognitive diagnostic models. [0003] The existing Q-matrix is ​​often manually constructed by experts according to the problem itself, which...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/22
Inventor 宋凌云刘至臻尚学群张颖李战怀
Owner NORTHWESTERN POLYTECHNICAL UNIV
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