Research on the Data Analysis of College Classroom Teaching Behavior by Using Deep Learning

DOI

10.2991/assehr.k.200401.014How to use a DOI?

Keywords

classroom behavior, pattern recognition, deep learning, correction matrix

Abstract

With education stepping into the era of intelligence, intelligent classroom behavior recognition of students is becoming more and more important. However, due to the complexity and variety of students’ classroom behavior, it is difficult to recognize intelligent students’ classroom behavior. In order to improve the intensive reading of intelligent student behavior recognition, this paper uses a variety of data sources for cross comparison, and uses the mature random forest algorithm and correction matrix in the data analysis of classroom teaching behavior in Colleges and universities. Through the analysis, it shows that deep learning can timely and accurately feedback the classroom teaching phenomenon and the data and intelligence of teaching activities, which is conducive to the improvement of teaching methods, the optimization of classroom teaching and management, to improve the efficiency of teaching and learning and help the teaching reform.

Copyright

© 2020, 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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TY  - CONF
AU  - Bing Gong
AU  - Fan Jing
PY  - 2020
DA  - 2020/04/06
TI  - Research on the Data Analysis of College Classroom Teaching Behavior by Using Deep Learning
BT  - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019)
PB  - Atlantis Press
SP  - 48
EP  - 50
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200401.014
DO  - 10.2991/assehr.k.200401.014
ID  - Gong2020
ER  -