Analysis of College Students' Practical Teaching Effect Based on Machine Learning Correlation Analysis Algorithm: Take the Software Technology Course as an Example
- DOI
- 10.2991/emim-18.2018.159How to use a DOI?
- Keywords
- Training courses, Auxiliary management, Abnormal early warning, Evaluation reference, Machine learning, Correlation analysis
- Abstract
In view of the difficulty in managing the training class in Colleges and universities, this paper tries to assist the training teachers to strengthen management from the point of machine learning algorithm. After analyzing the characteristics of the training class, we get 7 characteristic attributes including the training performance, the click of the mouse, the number of keyboards and the number of programs, and then we decide to use the association analysis method in machine learning to analyze the association rules between the characteristic attributes. . Finally, by using three steps of the association analysis algorithm - collecting data, preparing data and training algorithms, the frequent item sets of students with good training results {mouse, keyboard and program} are obtained. It can be found out that if the characteristic attribute values of a student are deviate from the trait attribute values of the students with good results during the training process. Too large indicates problems, which can help teachers warn students' abnormal situations and provide reference for evaluating training results.
- 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 - Xingxing Liu AU - Chenggang Yang PY - 2018/08 DA - 2018/08 TI - Analysis of College Students' Practical Teaching Effect Based on Machine Learning Correlation Analysis Algorithm: Take the Software Technology Course as an Example BT - Proceedings of the 8th International Conference on Education, Management, Information and Management Society (EMIM 2018) PB - Atlantis Press SP - 782 EP - 788 SN - 2352-5398 UR - https://doi.org/10.2991/emim-18.2018.159 DO - 10.2991/emim-18.2018.159 ID - Liu2018/08 ER -