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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Long, P., Siemens, G.: Penetrating the fog: analytics in learning and education. EDUCAUSE Review 46(5), 30–40 (2011)
Wong, B.T.M., Li, K.C., Cheung, S.K.S.: An analysis of learning analytics in personalised learning. J. Comput. High. Educ. (2022). https://doi.org/10.1007/s12528-022-09324-3
Wong, B.T.M., Li, K.C., Choi, S.P.M.: Trends in learning analytics practices: a review of higher education institutions. Interact. Technol. Smart Educ. 15(2), 132–154 (2018)
Li, K.C., Wong, B.T.M., Ye, C.J.: Implementing learning analytics in higher education: the case of Asia. Int. J. Serv. Stand. 12(3/4), 293–308 (2018)
Wong, B.T.M.: Learning analytics in higher education: an analysis of case studies. Asian Assoc. Open Univ. J. 12(1), 21–40 (2017)
Roth, S., Koonce, D., Devalapura, L., Khajuria, S.: A model to predict Ohio University student matriculation from admissions data. In: Proceedings of the 2007 Industrial Engineering Research Conference, pp. 1084–1089 (2007)
Slim, A., Hush, D., Ojah, T., Babbitt, T.: Predicting student enrolment based on student and college characteristics. In: Proceedings of the 11th International Conference on Educational Data Mining, pp. 383–389 (2018)
Stanley, C.J.: A data mining study of the matriculation of Covenant College applicants. In: Proceedings of the 46th Annual Southeast Regional Conference on XX, ACM-SE, vol. 46, 1593159, pp. 209–214 (2008)
Nurnberg, P., Schapiro, M., Zimmerman, D.: Students choosing colleges: Understanding the matriculation decision at a highly selective private institution. Econ. Educ. Rev. 31(1), 1–8 (2012)
Lux, T., Pittman, R., Shende, M., Shende, A.: Applications of supervised learning techniques on undergraduate admissions data. In: Proceedings of the 2016 ACM International Conference on Computing Frontiers, pp. 412–417 (2016)
Jamison, J.: Applying machine learning to predict Davidson college’s admissions yield. In: Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, pp. 765–766 (2017)
Wong, B.-M., Li, K.C.: A review of learning analytics intervention in higher education (2011–2018). J. Comput. Educ. 7(1), 7–28 (2019). https://doi.org/10.1007/s40692-019-00143-7
Li, K.C., Wong, B.T.M.: The use of student response systems with learning analytics: a review of case studies (2008–2017). Int. J. Mob. Learn. Organ. 14(1), 63–79 (2020)
Sekeroglu, B., Abiyev, R., Ilhan, A., Arslan, M., Idoko, J.B.: Systematic literature review on machine learning and student performance prediction: critical gaps and possible remedies. Appl. Sci. 11(22), 10907 (2021)
Alwarthan, S.A., Aslam, N., Khan, I.U.: Predicting student academic performance at higher education using data mining: a systematic review. Appl. Comput. Intell. Soft Comput. 2022, 8924028 (2022)
Wilcox, R.E., Lawson, K.A.: Predicting performance in health professions education programs from admissions information – comparisons of other health professions with pharmacy. Curr. Pharm. Teach. Learn. 10(4), 529–541 (2018)
Al-Alawi, R., Oliver, G., Donaldson, J.F.: Systematic review: predictors of students’ success in baccalaureate nursing programs. Nurse Educ. Pract. 48, 102865 (2020)
Kuncel, N.R., Hezlett, S.A.: Standardized tests predict graduate students’ success. Science 315(5815), 1080–1081 (2007)
de Boer, T., Van Rijnsoever, F.: In search of valid non-cognitive student selection criteria. Assess. Eval. High. Educ. 47(5), 783–800 (2022)
Parlina, A., Ramli, K., Murif, H.: Theme mapping and bibliometrics analysis of one decade of big data research in the scopus database. Information 11(69), 1–26 (2020)
Selivanova, I.V., Kosyakov, D.V., Guskov, A.E.: The impact of errors in the scopus database on the research assessment. Sci. Tech. Inf. Process. 46(3), 204–212 (2019)
Mahnic, V.: Scrum in software engineering courses: an outline of the literature. Glob. J. Eng. Educ. 17(2), 77–83 (2015)
Walid, M.A.A.; Ahmed, S.M.M.; Sadique, S.M.S.: A comparative analysis of machine learning models for prediction of passing bachelor admission test in life-science faculty of a public university in Bangladesh. In: The 2020 IEEE Electric Power and Energy Conference, EPEC 2020, p. 9320119 (2020)
El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A.: A recommender system for predicting students’ admission to a graduate program using machine learning algorithms. Int. J. Online Biomed. Eng. 17(2), 135–147 (2021)
Kiaghadi, M., Hoseinpour, P.: University admission process: a prescriptive analytics approach. Artif. Intell. Rev. 56, 233–256 (2022)
Ragan, J.F., Li, D., Matos-Díaz, H.: Using admission tests to predict success in college evidence from the University of Puerto Rico. East. Econ. J. 37(4), 470–487 (2011)
Wait, I.W., Gressel, J.W.: Relationship between TOEFL score and academic success for international engineering students. J. Eng. Educ. 98(4), 389–398 (2009)
Matar, N., Matar, W., Al Malahmeh, T.: Predictive model for students’ admission uncertainty using Naïve Bayes classifier and Kernel Density Estimation (KDE). Int. J. Emerg. Technol. Learn. 17(8), 75–96 (2022)
Protikuzzaman, M., Baowaly, M.K., Devnath, M.K., Singh, B.C.: Predicting undergraduate admission: a case study in Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh. Int. J. Adv. Comput. Sci. Appl. 11(12), 138–145 (2020)
Acharya, M.S., Armaan, A., Antony, A.S.: A comparison of regression models for prediction of graduate admissions. In: Proceedings of the 2nd International Conference on Computational Intelligence in Data Science, p. 8862140 (2019)
Bitar, Z., Al-Mousa, A.: Prediction of graduate admission using multiple supervised machine learning models. In: Conference Proceedings of IEEE SOUTHEASTCON 2020, p. 9249747 (2020)
Hien, N.T.N., Haddawy, P.: A decision support system for evaluating international student applications. In: Proceedings of Frontiers in Education Conference, FIE, vol. 4417958, pp. F2A1–F2A6 (2007)
Waters, A., Miikkulainen, R.: Grade: machine-learning support for graduate admissions. AI Mag. 35(1), 64–75 (2014)
Gao, Z., Gatpandan, M.P., Gatpandan, P.H.: Classification decision tree algorithm in predicting students’ course preference. In: Proceedings of the 2nd International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2021, pp. 93–97 (2021)
Mengash, H.A.: Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access 8(9042216), 55462–55470 (2020)
Al-Saqqa, S., Al-Naymat, G., Awajan, A.: A large-scale sentiment data classification for online reviews under apache spark. Procedia Comput. Sci. 141, 183–189 (2018)
Acknowledgment
The work described in this paper was partially supported by a grant from Hong Kong Metropolitan University (CP/2022/04).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-35731-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35730-5
Online ISBN: 978-3-031-35731-2
eBook Packages: Computer ScienceComputer Science (R0)