Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)

A Computer-aided Analysis of the Causes of College Students’ Learning Disability

Authors
Xiyuan Wu, Meng Ni, Songjie Feng, Chao Liu, Xinjie He, Borong Ma
Corresponding Author
Xiyuan Wu
Available Online October 2019.
DOI
10.2991/mbdasm-19.2019.17How to use a DOI?
Keywords
learning disability; behavior logs; linear regression
Abstract

The problem of college students’ learning disability is one of the problems that need to be solved in universities. This paper aims at the college students’ learning disability and applies linear regression model to analyze the relationship between learning behavior and achievements of students. It concludes that learners’ activeness, duration of study, self-discipline, etc., have a significant relationship with learning disability.

Copyright
© 2019, 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/).

Download article (PDF)

Volume Title
Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)
Series
Advances in Computer Science Research
Publication Date
October 2019
ISBN
10.2991/mbdasm-19.2019.17
ISSN
2352-538X
DOI
10.2991/mbdasm-19.2019.17How to use a DOI?
Copyright
© 2019, 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  - Xiyuan Wu
AU  - Meng Ni
AU  - Songjie Feng
AU  - Chao Liu
AU  - Xinjie He
AU  - Borong Ma
PY  - 2019/10
DA  - 2019/10
TI  - A Computer-aided Analysis of the Causes of College Students’ Learning Disability
BT  - Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)
PB  - Atlantis Press
SP  - 72
EP  - 74
SN  - 2352-538X
UR  - https://doi.org/10.2991/mbdasm-19.2019.17
DO  - 10.2991/mbdasm-19.2019.17
ID  - Wu2019/10
ER  -