A comprehensive evaluation model for university classroom teaching quality based on machine vision and Fermatean fuzzy sets

Abstract

Traditional course teaching evaluation models face increasing challenges in higher education due to their limitations in measuring qualitative indicators. To address this issue, this study constructs a classroom teaching quality evaluation system based on YOLOv5 object detection technology and the five major processes of course teaching management. Here, Fermatean fuzzy set (FFS) is introduced to express the uncertainty in the evaluation process. The core of this study is to establish a scientifically effective comprehensive evaluation model for university classroom teaching quality. Specifically, this model utilizes the minimum discriminative information principle and combines the order relation analysis method (G1 method) and the FFS entropy weighting method to determine attribute weights for weighted attribution. Thereafter, the proposed generalized distance measurement formula is utilized to enhance the technique for order preference by similarity to ideal solution (TOPSIS) for assessing similarity, thereby achieving the objective of evaluating classroom teaching. Eventually, the effectiveness and practicality of the evaluation model are validated through case studies and comparisons with other methods.

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“…In addition to academic performance, the flipped classroom model has also been found to enhance student participation and learning motivation. A study conducted by Raza focused on the impact of case analysis within a flipped classroom and found that it not only increased student participation but also improved students learning motivation and overall academic performance (Li et al, 2023). These findings are consistent with the feedback received from students who have experienced flipped learning, as they reported having a positive and engaging learning experience (Ying & Ayub, 2022).…”