Big data applied in secondary education student's achievement by using principal component analysis
- https://doi.org/10.2991/iceeecs-16.2016.41How to use a DOI?
- Education information; Big data technologies; Student performance; Analysis
With the advent of the era of big data, data analysis, penetrated into all walks of life among the analytical method has become more abundant. And inside the field of education, especially for high school inside analysis of the data it is relatively simple, which most high school's data is students' test scores. Principal component analysis method to middle school students mainly use the results were dimensionality reduction is calculated for each principal component scores and principal component composite score, then score cluster analysis method subjects isolated partial science students, science and migraine students were characteristic analysis, finally using a regression analysis of student test results were estimated, analyzed in the comprehensive examinations can play well and play mad two-part student discipline characteristics. Finally got partial science students tend to either science or the main component of integrated ranked among test exam has good performance.
- © 2016, 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 - Rui Li AU - Meiyan Tian PY - 2016/12 DA - 2016/12 TI - Big data applied in secondary education student's achievement by using principal component analysis BT - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) PB - Atlantis Press SP - 185 EP - 189 SN - 2352-538X UR - https://doi.org/10.2991/iceeecs-16.2016.41 DO - https://doi.org/10.2991/iceeecs-16.2016.41 ID - Li2016/12 ER -