Abstract
In colleges and universities, teaching quality evaluation is an integral part of the teaching management process. Many factors influence it, and the relationship between its evaluation index and instructional quality is complicated, abstract, and nonlinear. However, existing evaluation methods and models have flaws such as excessive subjectivity and randomness, difficulty determining the weight of indicators, easy over‐fitting, slow convergence speed, and limited computing power, to name a few. Furthermore, the evaluation index system focuses primarily on teaching attitude, material, and methods, rarely taking into account preparation prior to teaching or the teaching situation throughout the teaching process, resulting in an incomplete evaluation. As a result, learning how to construct a model for objectively, truly, thoroughly, and accurately assessing the teaching quality of colleges and universities is beneficial not only to improving teaching quality but also to promoting scientific decision‐making in education. This paper develops a teaching assessment model using a deep convolutional neural network and the weighted Naive Bayes algorithm. Based on the degree of influence of different characteristics on the assessment outcomes, a method to estimate the weight of each evaluation characteristic by employing the related probability of class attributes is proposed, and the corresponding weight is assigned for each evaluation index, resulting in a classification model ideal for teaching assessment that promotes standardization and intelligibility.
Highlights
With the continued development of higher education [1,2,3,4], determining how to fairly evaluate the teaching quality [5,6,7] of colleges and universities, promote the perfection of teaching objectives, and improve the teaching quality of colleges and universities is the key to furthering educational reform [8, 9], and it is an urgent problem that needs to be solved
Artificial neural network [11,12,13,14] is a nonlinear system [15] composed of many computational neurons which can be adjusted in different ways from layer to layer
The main contributions of this paper are as follows: (1) To solve the problem of evaluating teaching quality in colleges and universities, promote continuous improvement of teaching goals, and promote scientific decision-making in education, this paper proposes a method based on a deep convolutional neural network and a weighted Bayesian model, all of which can help to improve teaching quality
Summary
With the continued development of higher education [1,2,3,4], determining how to fairly evaluate the teaching quality [5,6,7] of colleges and universities, promote the perfection of teaching objectives, and improve the teaching quality of colleges and universities is the key to furthering educational reform [8, 9], and it is an urgent problem that needs to be solved now. Artificial neural network [11,12,13,14] is a nonlinear system [15] composed of many computational neurons which can be adjusted in different ways from layer to layer. It has the advantages of nonlinear ability, self-organization and selflearning ability, large-scale parallel processing, and so on. Many new theories and algorithms of artificial neural networks have been proposed successively as a result of a large number of scholars joining the research, such as the perceptron model [17], back-propagation algorithm [18], Boltzmann machine [19, 20], unsupervised learning [21], and supervised learning [22,23,24], and their theoretical research and information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
