Abstract
The identification of students with financial difficulties is one of the main problems in campus data research. Effective and timely identification not only provides convenience to campus administrators but also helps students who are really in financial hardship. The popular using of smart cards makes it possible to identify students with financial difficulties through big data. In this paper, we collect behavioural records from undergraduate students’ smart cards and propose five features by which to associate with students’ poverty level. Based on these features, we proposed the Apriori Balanced Algorithm (ABA) to mine the relationship of poverty level with students’ daily behaviour. Association rules show that students’ poverty level is most closely related to their academic performance, followed by consumption level, diligence level, and life regularity. Finally, we adopted the semisupervised K-means algorithm to more accurately find out students with financial difficulties. Tested by classical classification algorithms, our method has a higher identification rate, which is helpful for university administrators discover students in real financial hardship effectively.
Highlights
Nowadays, college students gradually become the main labour force in the society and have an important impact on the country’s economic and social development [1]
Application of Apriori Balanced Algorithm (ABA) and Results. e results of the ABA are shown in Tables 10 and 11, which are a 2-item set and a 3item set showing the correlation between behavioural characteristics and poverty level
It is clear that the Balanced_support of “Poor, Late” (0.2000) is higher than that of “Poor, Early” (0.1489). is is because that the diligence level is measured by the time of students’ first meal every day, and students may skip or seldom eat their breakfast for the sake of saving money, leading to a later time of first meal. When it comes to consumption level, we find that the Balanced_supports of “Poor, Medium” and “Poor, Low” are both higher than “Poor, High,” indicating that students in financial hardship spend lower money on average, which is in accordance with the reality
Summary
College students gradually become the main labour force in the society and have an important impact on the country’s economic and social development [1]. Thanks to the rapid development of digital campus, college students’ daily behaviours can be recorded in the campus smart-card system, so researchers are increasingly paying attention to the study of campus big data [2,3,4,5]. As a branch of campus behaviour research, finding students with financial difficulties can effectively help those who really need it and provide school administrators with a solution to find them and to give some financial support. Thanks to the rapid construction of smart campus, the student one-card system, known as the smart-card system, has been designed to record students’ behaviours of daily life. Thanks to the rapid construction of smart campus, the student one-card system, known as the smart-card system, has been designed to record students’ behaviours of daily life. ese behaviours include the consumption in the canteen, the Internet login records [7], the book-borrowing records [8], checkin records, and so on. e increasing amount of these data has provided opportunities for us to analyse students’ behaviour through novel information technologies
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