Title: Accurate identification of economic hardship students: a data-driven approach

Authors: Chunyan Yu; Linfeng Gu; Guilin Chen; Aiguo Wang

Addresses: School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China; School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, China; College of Education Science, Nanjing Normal University, Nanjing, 210000, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China ' School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, China ' School of Electronic Information Engineering, Foshan University, Foshan, 528051, China

Abstract: Universities provide subsidies to students with economic hardship every year. However, the traditional method cannot accurately find those who really need help. Recently, students often use e-cards to consume in campus, and the consumption data from e-cards reflect the students' behaviours. In this paper, a data-driven method which combines statistical methods with machine learning classification algorithms (SM²L) is proposed to identify students with economic hardship by using consumption data. First, SM²L extracts seven features about meals and bath and excludes some abnormal consumption individuals according to consumption time and amount. Second, three classification algorithms are used to classify students and get different numbers of students with economic hardship by adjusting the parameters. Third, output the intersection of the result from phase 2 and the students who consume more in school. Experiments show that SM²L can accurately identify students with economic hardship.

Keywords: consumption data; economic hardship students; identification; e-card; k nearest neighbour; KNN; support vector machine; SVM; logistic regression.

DOI: 10.1504/IJAHUC.2023.133451

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.44 No.1, pp.36 - 47

Received: 10 Nov 2022
Accepted: 21 Feb 2023
Published online: 15 Sep 2023 *

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