Title: The performance evaluation of teaching reform based on hierarchical multi-task deep learning

Authors: Jianlei Zhang

Addresses: School of Education, Anyang Normal University, Anyang 455000, Henan, China

Abstract: The research goal is to solve the problems of low accuracy and long time existing in traditional teaching reform performance evaluation methods, a performance evaluation method of teaching reform based on hierarchical multi-task deep learning is proposed. Under the principle of constructing the evaluation index system, the evaluation indicator system should be constructed. The weight of the evaluation index is calculated through the analytic hierarchy process, and the calculation result of the evaluation weight is taken as the model input sample. A hierarchical multi-task deep learning model for teaching reform performance evaluation is built, and the final teaching reform performance score is obtained. Through relevant experiments, it is proved that compared with the experimental comparison method, this method has the advantages of high evaluation accuracy and short time, and can be further applied in relevant fields.

Keywords: hierarchical multi-task deep learning; reform in education; performance evaluation; analytic hierarchy process; loss function.

DOI: 10.1504/IJITM.2024.139574

International Journal of Information Technology and Management, 2024 Vol.23 No.3/4, pp.318 - 329

Received: 19 Dec 2022
Accepted: 27 Feb 2023
Published online: 04 Jul 2024 *

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