Abstract
One of the most significant components of the teaching department’s evaluation of teaching quality is evaluating teachers’ performance. With the acceleration of educational informatization, modern information processing technology can be used effectively to evaluate teachers’ teaching quality in traditional teaching. In this context, combined with some computational intelligence algorithms, it is critical to developing a targeted teaching quality evaluation system. This paper studies teacher teaching evaluation’s characteristics and existing problems and analyzes the fundamental theories and methods of teacher teaching evaluation in colleges and universities. A novel combination of deep denoising autoencoder and support vector machine was proposed for evaluating teacher’s teaching quality. Moreover, support vector regression is used to predict the model’s output layer to achieve supervised assessment prediction. To capture the data’s key properties, the model comprises numerous hidden layers and conducts various feature transformations during unsupervised training to minimize the mean square error between the reconstructed output data and the original input data. As a result, the proposed model achieved the highest recognition accuracy of 85.23% and convergence compared to other models. Thus, the method can be employed to evaluate and forecast the quality of university teaching activity successfully.
Highlights
Mobile Information Systems are usually expressed as a set of numbers
Moraes et al [10] performed the teaching sentiment classification and compared support vector machine (SVM) against artificial neural network (ANN) algorithms. ey discovered the positive and negative sentiments using the learning support vector machines (SVM) and Naıve Bayes (NB) model. e result showed that ANN provided higher accuracy as compared to SVM and NB
SVM reported 8.26 Mean square error (MSE) and 78.52% accuracy, respectively. e lowest MSE was observed in the case of simple denoising autoencoder (DAE) with MSE 5.96 and an accuracy of 81.25%
Summary
Mobile Information Systems are usually expressed as a set of numbers. Following the students’ assessments, some specific data are used to display the students’ assessment results. Is paper uses advanced information and machine learning tools to provide a model for evaluating teachers’ teaching quality. Many teaching quality evaluation systems based on machine learning techniques have been developed. Chakrit et al [9] proposed a voting ensemble method of machine learning to reduce features in the data preprocessing stage and teach sentiment analysis. E experimental results show that the voting ensemble learning fused with chi-square feature selection exhibits higher than typical classifiers They did not apply attribute weights and sentiment analysis to enhance the efficiency of classification. E development status of the teaching quality evaluation system of physical education and the applicability of data mining technology and hidden Markov model to evaluating teaching quality in colleges was examined. According to the defects existing in the traditional evaluation model, this paper will analyze and study it, hoping to find a more reasonable index and set up a more scientific weight for it after various calculations, to establish a more systematic and comprehensive evaluation system
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