The Reform of Classroom Teaching Quality Evaluation Based on Analytic Hierarchy Process and Convolutional Neural Network.
Published In: International Journal of Computational Intelligence & Applications, 2025, v. 24, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhao, Jinku 3 of 3
Abstract
Classroom teaching evaluation is one of the important contents of the new round of basic education curriculum reform in China. The new curriculum reform puts forward new requirements for the construction of the teaching evaluation system: promoting the all-round development of students, promoting the continuous improvement of teachers' level, and promoting the curriculum of continuous development. However, from the current situation of the implementation of the new curriculum, the original teaching evaluation system is far from the requirements of the new curriculum reform, and does not have much practical value, and cannot provide strong support for the new curriculum reform. If it is not reformed, it will inevitably have a negative impact on the overall promotion of curriculum reform. How to improve classroom teaching evaluation under the background of the new curriculum reform, and how to establish a teaching evaluation scale and system suitable for the new curriculum reform, so as to play the role of evaluation in guiding, motivating and promoting, is an urgent problem to be solved at present. By referring to the relevant literature, the concepts of evaluation, teaching evaluation and classroom teaching evaluation are defined and discussed, and the object of classroom teaching evaluation is clarified. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Computational Intelligence & Applications. 2025/12, Vol. 24, Issue 4, p1
- Document Type:Article
- Subject Area:Education
- Publication Date:2025
- ISSN:1469-0268
- DOI:10.1142/S146902682342004X
- Accession Number:190554630
- Copyright Statement:Copyright of International Journal of Computational Intelligence & Applications is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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