Year: 2025
Type: conference-paper
Abstract: In a rapid changing education landscape, the assessment of teaching satisfaction plays a crucial role in improving educational quality. This paper elaborates the design and implementation of a data-driven teaching satisfaction evaluation system aiming at providing a comprehensive assessment of teaching quality by collecting and analyzing the student feedback data with machine learning algorithms. The system covers most of the real teaching scenarios from satisfaction surveys to data processing and from result analysis to report generation. By testing in a vocational training institution with more than 1,2500 students more than one academic year, the system proposed could effectively reflects student satisfaction level satisfaction improvement and provides valuable references for teaching improvements. Moreover, this research offers educational administrators an efficient tool for assessing teaching quality, which is of great significance for enhancing educational standards.
Authors: Xu Yang, Hui Liu
Institutions: Harbin Normal University, Shanghai Civil Aviation College
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4411744582