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Predictive Analytics for University Student Admission: A Literature Review

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Blended Learning : Lessons Learned and Ways Forward (ICBL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13978))

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Abstract

This paper presents a literature review on the use of learning analytics to support prediction in university student admission. The review covers four areas: types of research issues examined, types of data, analytical techniques, and performance metrics used. A total of 59 research articles published between 2013 and 2022 in relation to the use of predictive learning analytics for student admission were collected from Scopus for analysis. The findings show the major types of research issues including admission outcome, academic performance, admission yield, chance of admission, and suitable major/field of study. The types of data frequently used include academic performance, educational background, socio-demographic data, admission-related data, and application-related data. The findings also show that logistic regression, decision tree, random forest, support vector machine, and neural network are the most commonly adopted analytical techniques, whereas accuracy, recall, precision, F-measure, and R-squared are the most frequently used performance metrics. The results contribute to identifying the features and patterns of predictive learning analytics with respect to university student admission.

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Acknowledgment

The work described in this paper was partially supported by a grant from Hong Kong Metropolitan University (CP/2022/04).

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Authors and Affiliations

  1. Hong Kong Metropolitan University, Hong Kong, China

    Kam Cheong Li, Billy Tak-Ming Wong & Hon Tung Chan

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  1. Kam Cheong Li
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  2. Billy Tak-Ming Wong
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  3. Hon Tung Chan
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Corresponding author

Correspondence to Billy Tak-Ming Wong .

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Chen Li

  2. Hong Kong Metropolitan University, Kowloon, Hong Kong

    Simon K. S. Cheung

  3. Hong Kong Metropolitan University, Kowloon, Hong Kong

    Fu Lee Wang

  4. City University of Hong Kong, Kowloon, Hong Kong

    Angel Lu

  5. HKCT Institute of Higher Education, Sha Tin District, Hong Kong

    Lam For Kwok

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Li, K.C., Wong, B.TM., Chan, H.T. (2023). Predictive Analytics for University Student Admission: A Literature Review. In: Li, C., Cheung, S.K.S., Wang, F.L., Lu, A., Kwok, L.F. (eds) Blended Learning : Lessons Learned and Ways Forward . ICBL 2023. Lecture Notes in Computer Science, vol 13978. Springer, Cham. https://doi.org/10.1007/978-3-031-35731-2_22

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  • DOI: https://doi.org/10.1007/978-3-031-35731-2_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-35731-2

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