Supporting poverty-stricken college students in smart campus
Supporting poverty-stricken college students in smart campus
复制标题
资助贫困大学生智慧校园
DOI：
10.1016/j.future.2019.09.017
复制
发表时间：
2020-10
影响因子：
7.5
通讯作者：
Li Fei
中科院分区：
文献类型：
--
作者：
Wu Fan;Zheng Qinhua;Tian Feng;Suo Zhihai;Zhou Yuan;Chao Kuo-Ming;Xu Mo;Shah Nazaraf;Liu Jun;Li Fei
Chinese colleges have formulated supporting polices to help poverty-stricken college students to deal with the barriers in their living and learning. The difficulty in fully collecting the required information related to student's financial status and the imbalanced-data classification problem caused by the small proportion of poverty-stricken students among total students makes it a challenging problem. This problem results in a heavy workload for the college staff to identify poverty-stricken students, determine the amount of corresponding subsidy, and execute the supporting polices in an efficient way. Therefore, this paper attempts to address the above-mentioned challenges by proposing a smart campus system, which makes use of campus big data to identify poverty-stricken students and support the decision-making on the subsidy for them. The proposed system can also alert the counselors to provide psychological support for students in trouble. The major contributions of this research are as follows. Firstly, in addition to the features of students' amount of consumption on campus and its statistical characteristics used in existing researches, this paper proposes new features that describe diversity of consumable commodities, preference of consumption location and price, and characteristics of students' campus activities. Secondly, in order to solve the problem of dataset imbalance, four imbalanced data processing methods (Subsampling, Resampling, Cost-sensitive learning and SMOTE) have been applied to produce four different experimental datasets, and five classification algorithms (Random Forest, J48, Naive Bayes, SMO, Logistic regression) have been used to train the classification model on each dataset. The experimental results indicate that the model based on Resampling and Random Forest achieves the best performance in F1-measure of poverty-stricken students, among the combinations of four imbalanced processing methods and five classification algorithms. In addition, a method of quantization of subsidies, and strategies of early warning and counseling for students are also described in this paper. A system was developed based on the above-mentioned methods, which meets the needs of individualized and diversified support for poverty-stricken students. The methods and the proposed system have been put into practice, and it is serving more than 17,000 students. The system has significantly improved the efficiency and quality of student management, and reduced the workload of college staff. (C) 2019 Elsevier B.V. All rights reserved.
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DOI：
10.1109/icscse.2017.22
发表时间：
2017-11
期刊：
2017 International Conference on Smart City and Systems Engineering (ICSCSE)
影响因子：
--
作者：
Tongjun Jiang;Jianmei Cao;Dan Su;Xianglai Yang
通讯作者：
Tongjun Jiang;Jianmei Cao;Dan Su;Xianglai Yang
DOI：
10.1109/ihmsc.2016.206
发表时间：
2016-08
期刊：
2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
影响因子：
--
作者：
Yu Liu;Maodi Hu;X. Lu
通讯作者：
Yu Liu;Maodi Hu;X. Lu
影响因子：
3.9
作者：
Qu, Shaojie;Li, Kan;Wang, Yongchao
通讯作者：
Wang, Yongchao
DOI：
--
发表时间：
2006
期刊：
Chinese Journal of Behavioral Medicine and Brain Science
影响因子：
--
作者：
SU Yan-chao
通讯作者：
SU Yan-chao
DOI：
10.1109/icdmw.2015.45
发表时间：
2015-11
期刊：
2015 IEEE International Conference on Data Mining Workshop (ICDMW)
影响因子：
--
作者：
S. Fan;Ping Li;Ting Liu;Yan Chen
通讯作者：
S. Fan;Ping Li;Ting Liu;Yan Chen