Error Analysis Method Based on Graph Neural Network

Abstract

The present invention provides a fault analysis method based on a graph neural network, which collects possible explainable fault information in assignments or test papers to form a corresponding set of preset explainable fault information. The set of preset fault-cause solutions matched with the set of explainable fault-cause information and the corresponding corresponding fault-cause graph neural network model, according to the actual situation of the current fault-cause information, can be explained from the preset fault-cause graph neural network model Find out the explainable error information with the highest degree of correlation from the wrong cause information set, and find the matching preset error cause solution method from the preset error cause solution set, so as to realize the actual occurrence in the homework or test paper Quickly locate and find the explainable cause information and determine the appropriate explainable cause solution accurately and efficiently.

Description

technical field [0001] The invention relates to the technical field of information error analysis and solution, in particular to a method for error analysis based on a graph neural network. Background technique [0002] At present, in the teaching process, it is usually necessary to judge homework or test papers. With the increase of the number of students and teaching tasks, the amount of homework and test papers that teachers need to judge also increases. It is not possible to quickly, comprehensively and accurately review and judge all assignments or examination papers one by one, which is not only conducive to teachers' in-depth understanding of different students' mastery of different knowledge points, but also affects the teaching progress and teaching quality. It can be seen that there is an urgent need in the prior art for a method that can effectively analyze the interpretable error causes in homework or test papers and quickly propose a solution to the error causes...

AI Technical Summary

Problems solved by technology

[0002] At present, in the teaching process, it is usually necessary to judge homework or test papers, and with the increase in the number of students and teaching tasks, the amount of homework and test papers that teachers need to judge also increases, relying only on manual review and judgment by teachers, It is not possible to quickly, comprehensively and accurately review and judge all assignments or test papers one by one, which is not conducive to teachers' in-depth understanding of different students' mastery of different knowledge points, but also affects teaching progress and teaching quality

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