Cargando…

Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network

The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education (IEE). Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction (CAI) system. Secondly, the CAI model is...

Descripción completa

Detalles Bibliográficos
Autor principal: Cao, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343719/
https://www.ncbi.nlm.nih.gov/pubmed/35928420
http://dx.doi.org/10.3389/fpsyg.2022.900195
_version_ 1784761051324088320
author Cao, Hong
author_facet Cao, Hong
author_sort Cao, Hong
collection PubMed
description The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education (IEE). Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction (CAI) system. Secondly, the CAI model is implemented based on the weight sharing and local perception of the Convolutional Neural Network (CNN). Finally, the performance of the CNN-based CAI model is tested. Meanwhile, it analyses students’ IEE experience under the proposed CAI model through a case study of Music Majors from Xi’an Conservatory of Music. The experimental data show that the CNN-based CAI model can respond quickly and stably when users access different functional modules, such as webpage browsing. The proposed CAI model increases students’ entrepreneurial interest, skills, and knowledge by 55.62, 57.32, and 72.12%, respectively. Students’ entrepreneurial practice ability has been improved by over 50.00%; such an increase in entrepreneurial practice ability has also shown individual differences. Thus, the proposed Music Majors-oriented IEE-infiltrated CAI model based on CNN improves students’ entrepreneurial practice ability and reflects the positive experience of Music Majors on IEE. The finding provides references for the step-by-step identification of the CNN-based CAI model and has certain guiding significance for analyzing the effect of college IEE.
format Online
Article
Text
id pubmed-9343719
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-93437192022-08-03 Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network Cao, Hong Front Psychol Psychology The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education (IEE). Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction (CAI) system. Secondly, the CAI model is implemented based on the weight sharing and local perception of the Convolutional Neural Network (CNN). Finally, the performance of the CNN-based CAI model is tested. Meanwhile, it analyses students’ IEE experience under the proposed CAI model through a case study of Music Majors from Xi’an Conservatory of Music. The experimental data show that the CNN-based CAI model can respond quickly and stably when users access different functional modules, such as webpage browsing. The proposed CAI model increases students’ entrepreneurial interest, skills, and knowledge by 55.62, 57.32, and 72.12%, respectively. Students’ entrepreneurial practice ability has been improved by over 50.00%; such an increase in entrepreneurial practice ability has also shown individual differences. Thus, the proposed Music Majors-oriented IEE-infiltrated CAI model based on CNN improves students’ entrepreneurial practice ability and reflects the positive experience of Music Majors on IEE. The finding provides references for the step-by-step identification of the CNN-based CAI model and has certain guiding significance for analyzing the effect of college IEE. Frontiers Media S.A. 2022-07-19 /pmc/articles/PMC9343719/ /pubmed/35928420 http://dx.doi.org/10.3389/fpsyg.2022.900195 Text en Copyright © 2022 Cao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Cao, Hong
Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network
title Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network
title_full Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network
title_fullStr Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network
title_full_unstemmed Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network
title_short Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network
title_sort entrepreneurship education-infiltrated computer-aided instruction system for college music majors using convolutional neural network
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343719/
https://www.ncbi.nlm.nih.gov/pubmed/35928420
http://dx.doi.org/10.3389/fpsyg.2022.900195
work_keys_str_mv AT caohong entrepreneurshipeducationinfiltratedcomputeraidedinstructionsystemforcollegemusicmajorsusingconvolutionalneuralnetwork