Evaluation of Teaching Reform Effect of Computer Vision Specialty Based on Deep Learning

Yajing Chen

2023

Abstract

Most of the conventional evaluation methods for teaching reform effect are designed based on the principle of K-modes algorithm, with limited scope of application, large deviation of evaluation results, and unable to obtain more accurate teaching reform effect.This paper introduces the principle of deep learning method, and takes the computer vision specialty as an example, puts forward the evaluation research on the teaching reform effect of computer vision specialty based on deep learning.First, select the data set required for evaluation, and preprocess the data set to reduce the repeatability and redundancy of data.Secondly, establish the evaluation index system of the teaching reform effect of computer vision specialty, analyze and find out the potential internal links in the data, as the theoretical basis for the evaluation of the teaching reform quality effect.On this basis, hierarchical nesting is used to characterize the affiliation of deep learning and evaluate the effect of teaching reform.It can be seen from the application test results that after the application of the new evaluation method, the average score evaluation result of the computer culture basic applicationskills test is closer to the actual score, with small deviation and significant advantages in evaluation effect.

Paper Citation

in Harvard Style

Chen Y. (2023). Evaluation of Teaching Reform Effect of Computer Vision Specialty Based on Deep Learning. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 208-213. DOI: 10.5220/0012277700003807

in Bibtex Style

@conference{anit23,
author={Yajing Chen},
title={Evaluation of Teaching Reform Effect of Computer Vision Specialty Based on Deep Learning},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={208-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012277700003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Evaluation of Teaching Reform Effect of Computer Vision Specialty Based on Deep Learning
SN - 978-989-758-677-4
AU - Chen Y.
PY - 2023
SP - 208
EP - 213
DO - 10.5220/0012277700003807
PB - SciTePress