Abstract
Due to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching quality of college teachers. To solve the above problems, a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network is proposed. Firstly, face detection is realized by adding three Haar-like feature blocks and improving the AdaBoost algorithm of a weak classifier connection. Secondly, in order to effectively improve the image imbalance, the MSR algorithm is used to enhance the face data image, which is effectively suitable for network training. Then, by readjusting the connection mode, the DenseNet is improved to fully reflect the local detail feature information of the low level. Finally, quantum particle swarm optimization (QPSO) is used to optimize the DenseNet structure, which makes the optimization of network structure more automatic and solves the uncertainty of manual selection. The experimental results show that the proposed method has a good detection effect and prove the effectiveness and correctness of the proposed method.
Highlights
With the rapid development of Educational Informatics, it has been widely used in many aspects of the higher education field and achieved good results, but research and practice have lagged behind in measuring teaching quality, such as the teaching quality evaluation system and evaluation model; it is well known that the evaluation of teaching quality analysis is a very complex nonlinear process [1,2,3,4]; there are multiple influencing factors and dynamic variables involved, so that the traditional model of the teaching quality has become less fully competent in work addressing this ambiguity
In order to solve the above problems, this study proposed a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network. e proposed method improves DenseNet [19] by reducing the number of redundant connections, so as to fully reflect low-level local detail feature information
In order to improve the accuracy of student fatigue state evaluation, this paper proposes a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network
Summary
With the rapid development of Educational Informatics, it has been widely used in many aspects of the higher education field and achieved good results, but research and practice have lagged behind in measuring teaching quality, such as the teaching quality evaluation system and evaluation model; it is well known that the evaluation of teaching quality analysis is a very complex nonlinear process [1,2,3,4]; there are multiple influencing factors and dynamic variables involved, so that the traditional model of the teaching quality has become less fully competent in work addressing this ambiguity. E traditional teaching quality evaluation method needs to monitor each student’s sitting posture, facial expression, and other information in the video manually to judge whether each student has fatigue. How to find the fatigue state of students in time and effectively by means of automation in practical application, so as to effectively prevent the occurrence of students’ inattention in class, has a strong practical significance and helps to ensure the teaching quality of information-based courses. Many researchers have applied deep learning to fatigue state detection tasks and achieved good recognition accuracy, among which the most representative is the artificial neural network. In order to solve the above problems, this study proposed a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network. Experimental results show that, on CAS_PEAL and self-built datasets, the accuracy of the QPSO-DenseNet algorithm is higher than that of the optimal DenseNet structure selected manually
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