Computer adaptive testing method, system, electronic device and storage medium

By introducing a topic selection model based on knowledge graph coverage and chain distance loss functions, along with dynamic diagnosis using recurrent neural networks, the problem of low reliability and efficiency of test resources in computer adaptive testing is solved, enabling precise recommendations of personalized tests and learning resources.

CN115936059BActive Publication Date: 2026-03-20IFLYTEK CO LTD
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Patent Information

Application Number
CN202211698490.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-12-28
Publication Date
2026-03-20
Estimated Expiration
2042-12-28

AI Technical Summary

Technical Problem

Existing computer-adaptive testing methods are difficult to guarantee in terms of effectiveness when the initial ability estimate deviates from the actual ability value. The lack of interpretability of information leads to a decrease in the credibility of test resources, and the test efficiency is low, especially when there are few historical answer records.

Method used

A topic selection model training method is adopted, which introduces the knowledge graph coverage and knowledge point chain distance loss function, and combines a recurrent neural network model for dynamic ability diagnosis. The topic selection model is trained through an active learning mechanism to improve the credibility of test resources and test efficiency.

Benefits of technology

It enables personalized testing for different learning situations, improves the reliability and efficiency of testing resources, ensures the accuracy of test results, and supports precise recommendations of learning resources.

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Abstract

The present application relates to the technical field of computer application, and provides a computer adaptive test method and system, an electronic device and a storage medium, which first determines the current ability information of a target subject for a target subject; then inputs the historical answering information and the current ability information of the target subject for the target subject into a question selection model to obtain test resources output by the question selection model. When the basic model is trained to obtain the question selection model, a first loss function is introduced to ensure that the knowledge coverage rate of the test resources output by the question selection model is overall for the target subject, and a second loss function is introduced to ensure that the knowledge point correlation of the test resources output by the question selection model is overall small, so that the credibility of the test resources can be improved, the target subject can be comprehensively tested, the test efficiency can be improved, the subsequent test result is more accurate, and accurate recommendation of learning resources can be realized.
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