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Evaluation System of Tourism Psychology Teaching Quality Based on Convolutional Neural Network

Published: 22 November 2021 Publication History

Abstract

How to improve the teaching quality (TQ) of tourism psychology in higher education has become the focus of current higher education. Teaching evaluation is a key measure to improve TQ. Therefore, it is particularly important to formulate a scientific and reasonable ES for college classroom TQ. As a new technology, neural network (NN) has been widely used in various evaluation problems by virtue of its non-linear processing, adaptive learning, high fault tolerance and other characteristics. The purpose of this article is to study the ES of tourism psychology TQ based on convolutional NN. This article takes the school's internal talent training quality ES as the research object, and introduces the optimized convolutional NN theory into the college tourism psychology TQ evaluation. The mathematical model is established using the NN model structure, and the convolutional NN algorithm is introduced in detail. The realization of the optimized system is divided into two parts, one is to collect the scoring data of students, teachers and experts through the online ES. The second is to process the collected data through the ES to achieve sample maintenance, optimized NN training and NN evaluation functions to obtain the teacher's final evaluation results. Experimental results show that not only the accuracy of training and prediction is completely within the acceptable range, but the error of the test sample is more than 90% similar to the error of the test sample. It can be seen that the student evaluation model based on NN proposed in this paper is a reasonable and feasible evaluation model.

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      cover image ACM Other conferences
      ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided Education
      September 2021
      2972 pages
      ISBN:9781450390255
      DOI:10.1145/3482632
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 22 November 2021

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      • This work was supported by 2021 School-level Teaching Reform Research Project of the China University of Labor Relations

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