A teaching quality evaluation method, device and medium based on federated learning

By adopting a teaching quality evaluation method based on federal learning in medical teaching and combining a dual linear interpolation algorithm with trajectory correction, the accuracy and privacy of teaching quality evaluation in the existing technology are solved, and efficient and accurate teaching quality evaluation and improvement of young teachers' teaching ability are achieved.

CN119783954AInactive Publication Date: 2025-04-08PEKING UNIVERSITY FIRST HOSPITAL (PEKING UNIVERSITY FIRST CLINICAL MEDICAL COLLEGE)
View PDF 0 Cites 0 Cited by

Patent Information

Application Number
CN202411811561.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-04-08
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is difficult to accurately and efficiently evaluate teaching quality in medical teaching, especially in terms of data privacy and model generalization capabilities.

Method used

The teaching quality evaluation method based on federated learning is adopted, and local model training and parameter aggregation and update are carried out through the collaborative work of the central server and multiple clients to ensure data privacy, and the teaching data is expanded using the dual linear interpolation algorithm of trajectory correction.

Benefits of technology

It achieves more accurate and efficient teaching quality evaluation, ensures data privacy, improves the generalization ability of models, and improves the teaching ability of young teachers and students' acceptance.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN119783954A_ABST
    Figure CN119783954A_ABST
Patent Text Reader

Abstract

The present invention provides a method, system, device, medium and program product for constructing a teaching quality evaluation model based on federated learning, as well as a teaching quality evaluation method based on the teaching quality evaluation model, and relates to the technical field of student data processing. The method of the teaching quality evaluation model includes: the federated learning is composed of a central server and N clients; the client trains the local model through the original teaching data, and different clients use different secure communication channels to derive local model parameters from the local model and upload them to the central server; after receiving the local model parameters, the central server aggregates and updates them and sends them to the local model for updating to obtain a local update model, which will replace the local model; repeat the above steps for multiple iterations until the loss function converges or the number of training rounds reaches the preset number of rounds of the model to obtain a teaching quality evaluation model; the original teaching data is expanded based on the trajectory-corrected dual linear interpolation algorithm.
Need to check novelty before this filing date? Find Prior Art