TY - GEN
T1 - Predictive analytics for university student admission: A literature review
AU - LI, Kam Cheong
AU - Wong, Tak Ming Billy
AU - CHAN, Hon Tung
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - learning analytics
KW - machine learning
KW - predictive analytics
KW - student admission
KW - university education
UR - https://www.scopus.com/pages/publications/85171445418
U2 - 10.1007/978-3-031-35731-2_22
DO - 10.1007/978-3-031-35731-2_22
M3 - Conference contribution
AN - SCOPUS:85171445418
SN - 9783031357305
VL - 13978
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 250
EP - 259
BT - Blended Learning
A2 - Li, Chen
A2 - Cheung, Simon K. S.
A2 - Wang, Fu Lee
A2 - Lu, Angel
A2 - Kwok, Lam For
T2 - 16th International Conference on Blended Learning, ICBL 2023
Y2 - 17 July 2023 through 20 July 2023
ER -