Proceedings of the 2017 5th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2017)

Research on Independent College Enrollment Expansion and Teaching Quality Assessment Model Based on PSO Algorithm

Authors
Xiuming Yu, Yunfei Yu
Corresponding Author
Xiuming Yu
Available Online January 2018.
DOI
10.2991/ieesasm-17.2018.7How to use a DOI?
Keywords
Enrollment expansion; PSO algorithm; Teaching assessment
Abstract

Teaching evaluation plays an important role in college teaching activity, and how to ensure high teaching quality while the teaching scale is expanding gradually is a core issue in independent colleges. This paper presents an intelligent optimization model based on PSO algorithm for the scale of enrollment expansion and teaching evaluation in independent college, to help conduct teaching quality evaluation in the case of limited resources.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 5th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
January 2018
ISBN
978-94-6252-452-1
ISSN
2352-5398
DOI
10.2991/ieesasm-17.2018.7How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiuming Yu
AU  - Yunfei Yu
PY  - 2018/01
DA  - 2018/01
TI  - Research on Independent College Enrollment Expansion and Teaching Quality Assessment Model Based on PSO Algorithm
BT  - Proceedings of the 2017 5th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2017)
PB  - Atlantis Press
SP  - 29
EP  - 33
SN  - 2352-5398
UR  - https://doi.org/10.2991/ieesasm-17.2018.7
DO  - 10.2991/ieesasm-17.2018.7
ID  - Yu2018/01
ER  -