Analyzing and clustering students' application preferences in higher education

Zs T Kosztyán; É Orbán-Mihálykó; Cs Mihálykó; V V Csányi; A Telcs

doi:10.1080/02664763.2019.1709052

Analyzing and clustering students' application preferences in higher education

J Appl Stat. 2020 Jan 14;47(16):2961-2983. doi: 10.1080/02664763.2019.1709052. eCollection 2020.

Authors

Zs T Kosztyán^{1

2

3}, É Orbán-Mihálykó⁴, Cs Mihálykó⁴, V V Csányi^{1

2}, A Telcs^{1

2

5

6}

Affiliations

¹ MTA-PE Budapest Ranking Research Group, Budapest, Hungary.
² Faculty of Business and Economics, Institute of Management, Department of Quantitative Methods, University of Pannonia, Veszprem, Hungary.
³ Institutes of Advanced Studies, Köszeg (iASK), Koszeg, Hungary.
⁴ Department of Mathematics, University of Pannonia, Veszprem, Hungary.
⁵ Wigner Research Center for Physics, Budapest, Hungary.
⁶ VIAS - Virtual Institute of Advances Studies.

Abstract

We present a framework based on a higher education application preference list that allows a different type of flexible aggregation and, hence, the analysis and clustering of application data. Preference lists are converted into scores. The proposed approach is demonstrated in the context of higher education applications in Hungary over the period of 2006-2015. Our method reveals that efforts to leverage center-periphery differences do not fulfill expectations. Furthermore, the student's top preference is very hard to influence, and recruiters may build their strategy on information about the first and second choices.

Keywords: Institutional policy; application preferences; impacts of financing change; institutional preference characteristics; preference order clustering methods.