Abstract
A significant influence on the choice of both specialty and specific educational institution made by each entrant during the admission campaign is the assessment of the applicant’s chances of admission. One possible way to solve this task is to build specialized web services using machine learning methods based on the processing of entry data for previous years. In this paper, research and experimental analysis of existing machine learning methods to solve this task were made. Experimental evaluation of Linear Regression, Stochastic Gradient Descent (SGD), Tree, AdaBoost, Random Forest, Support Vector Machine (SVM) and k-nearest neighbors’ algorithm (k-NN) was performed on a real data set. It was found that the highest prediction accuracy based on all accuracy indicators was obtained using methods based on Linear Regression and SGD. Since the latter method is the basis of a number of algorithms for training artificial neural networks, in the future it is advisable to evaluate and improve existing and develops, ANN-based approaches to improve the accuracy of the task in this study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhezhnych, P., Berezko, O., Zub, K., Demydov, I.: Analysis of features and abilities of online systems and tools meeting information needs of HEIs’ entrants. In: Proceedings of the 2nd IWCOAPSN, vol. 2616, pp. 76–85 (2020)
Zhezhnych, P., Demydov, I., Berezko, O., Shilinh, A.: Corporate culture influence on the HEI’s information image on the internet. In: Proceedings of the 2nd IWCOAPSN, vol. 2392, pp. 286–296 (2020)
Korzh, R., Peleschyshyn, A., Holub, Z.: Analysis of integrity and coverage completeness of the informational image of a higher education institution. In: Proceedings of 13th ICTCSET, pp. 825–827 (2016)
Cruz, A.P.D., et al.: Higher education institution (HEI) enrollment forecasting using data mining technique. Int. J. Adv. Trends Comput. Sci. Eng. 9(2), 2060–2064 (2020)
Kurysheva, H.V., Rijen, M.V., Dilaver, G.: How do admission committees select? Do applicants know how they select? Selection criteria and transparency at a Dutch University’. Int. J. Tert. Educ. Manag. 25(4), 367–388 (2019)
Sujay, S.: Supervised machine learning modelling & analysis for graduate admission prediction. Int. J. Trend Res. Dev. 7(4), 5–7 (2020)
Graduate Admission 2. https://kaggle.com/mohansacharya/graduate-admissions. Accessed 26 Nov 2020
Janani, P., Hema, P., Monisha, P.: Prediction of MS graduate admissions using decision tree algorithm. Int. J. Sci. Res. 9(3), 492–495 (2018)
AlGhamdi, A., Barsheed, H., AlMshjary, H., AlGhamdi, H.: A machine learning approach for graduate admission prediction. In: Proceedings of 2nd ICIVSP, pp. 155–158 (2020)
Koutina, M., Kermanidis, K.L.: Predicting postgraduate students’ performance using machine learning techniques. In: Proceedings of ICAIAI, pp. 159–168 (2011)
Chakrabarty, N., Chowdhury, S., Rana, S.: A statistical approach to graduate admissions’ chance prediction’, in innovations in computer science and engineering. In: Proceedings of 7th ICICSE, pp. 333–340 (2020)
Acharya, M.S., Armaan, A., Antony, A.S.: A comparison of regression models for prediction of graduate admissions’, in computational intelligence in data science. In: Proceedings of ICCIDS, pp. 1–5 (2019)
Demšar, J., et al.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14, 2349–2353 (2013)
Izonin, A., et al.: The combined use of the wiener polynomial and SVM for material classification task in medical implants production. Int. J. Intell. Syst. Appl. 10(9), 40–47 (2018)
Orange Visual Programming 3 documentation’. https://orange3.readthedocs.io/projects/orange-visual-programming/en/latest/widgets/model/tree.html. Accessed 27 Nov 2020
Tkachenko, R., et al.: Development of machine learning method of titanium alloy properties identification in additive technologies. East.-Eur. J. Enterp. Technol. 3(12), 23–31 (2018)
Izonin, K.N., Tkachenko, R., Zub, K.: An approach towards missing data recovery within IoT smart system. Procedia Comput. Sci. 155, 11–18 (2019)
Fedushko, S., Ustyianovych, T.: Predicting pupil’s successfulness factors using machine learning algorithms and mathematical modelling methods. In: Proceedings of ICCSEEA, pp. 625–636 (2020)
Raghavendra, C.K., Srikantaiah, K.C., Venugopal, K.R.: Personalized recommendation systems (PRES): a comprehensive study and research issues. Int. J. Mod. Educ. Comput. Sci. 10(10), 11–21 (2018)
Qingyun, L.: A quantitative analysis of postgraduate programs admission of the Hong Kong institute of education. Int. J. Mod. Educ. Comput. Sci. 7(3), 1–11 (2015)
Muhammad, U.F., Sani, M.I.: Data mining to prediction student achievement based on motivation, learning and emotional intelligence in man 1 ketapang. Int. J. Mod. Educ. Comput. Sci. 10(6), 53–60 (2018)
Abdulkadir, S., et al.: Assessment of students’ academic performance using admission entry requirements under the computer-based test and paper-pencil-based test in Kaduna State University, Kaduna – Nigeria. Int. J. Mod. Educ. Comput. Sci. 11(8), 48–60 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhezhnych, P., Zub, K., Berezko, O., Shilinh, A. (2021). A Comparison of Machine Learning Algorithms for Prediction Higher Education Institution’s Entrants Admissions. In: Hu, Z., Zhang, Q., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Logistics Engineering. ICAILE 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-030-80475-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-80475-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80474-9
Online ISBN: 978-3-030-80475-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)