Recognition and Analysis of Poor Students on College Students Campus Card Consumption Data Based on Big Data
Aifeng Li, Zhineng Xiao, Biyun Liang
Available Online May 2017.
- https://doi.org/10.2991/icmeit-17.2017.21How to use a DOI?
- Big data, Campus card, Poor student recognition, Consumption data
- Subsidization for students from low-income families is a major student management work for colleges and universities. With deep and extensive use of big data in all sectors, relevant big data shall be used to identify poor students. Using big data rationally for targeted subsidization represents in-depth application of big data in educational field. In this thesis, the researchers collected 36546 data concerning dining consumption of students in three months, used Datist, a big data analysis software to build a model, acquired concerning dining habits, consuming behaviors, situations in school and consumption indicators of the students, and then selected poor students. This study laid a solid foundation for large-scale dynamic implementation of "campus big data and targeted subsidization", accurate recognition of poor students and rational student analysis in future.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Aifeng Li AU - Zhineng Xiao AU - Biyun Liang PY - 2017/05 DA - 2017/05 TI - Recognition and Analysis of Poor Students on College Students Campus Card Consumption Data Based on Big Data BT - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017) PB - Atlantis Press SP - 111 EP - 114 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-17.2017.21 DO - https://doi.org/10.2991/icmeit-17.2017.21 ID - Li2017/05 ER -