AN OVERVIEW OF THE CURRENT PROGRESS OF THE HEI’S SUPPORT SYSTEMS FROM THE ENTRANTS’ PERSPECTIVES
DOI:
https://doi.org/10.31649/1999-9941-2022-53-1-28-36Keywords:
entrants, higher educational institution (HEI), major, decision support, literature reviewAbstract
One of the strategically important processes of the higher education institution activity is the enrollment campaign. In the information and knowledge society, the effectiveness of its implementation depends on many factors, one of which is the use of information technology. Therefore, the purpose of this paper is to examine current researches and determine the existing trends aims to support the decision-making of HEI`s and major from entrants perspective. This literature review uses Scopus and Web of Science databases and Google Scholar web search engine. Major findings include three lines of research that generate contributions on this topic: predicting the success of admission, recommendation of the major or education institution, and investigation of factors influencing the entrant`s choice. The review indicates that the most common is the use of data mining to solve researches tasks. The results of this study allow us to identify key points that are critical at the initial stage of solving decision support issues and to detect the main future directions of research.
References
A. Towers, N. Towers, «Re-evaluating the postgraduate students’ course selection decision making process in the digital era», Stud. High. Educ., vol. 45, no. 6, p. 1133–1148, 2020, doi: 10.1080/03075079.2018.1545757.
P. Zhezhnych, O. Berezko, K. Zub, and I. Demydov, «Analysis of Features and Abilities of Online Systems and Tools Meeting Information Needs of HEIs’ Entrants», Proceedings of the 2nd Interna-tional Workshop on Control, Optimisation and Analytical Processing of Social Networks, vol. 2616, 76–85, Lviv, Ukraine.
P.-C. Chang, C.-H. Lin, and M.-H. Chen, «A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems», Algorithms, vol. 9, no. 3, p. 47, 2016, doi: 10.3390/a9030047.
Abiyoga, A. Wicaksana, and N. M. S. Iswari, «Decision Support System for Choosing an Elective Course Using Naive Bayes Classifier», Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, vol. 850, p. 97–110, 2020 doi: 10.1007/978-3-030-26428-4_7.
E. Daniati, «Decision Support Systems to Determining Programme for Students Using DBSCAN And Naive Bayes: Case Study: Engineering Faculty Of Universitas Nusantara PGRI Kediri» in 2019 Inter-national Conference of Artificial Intelligence and Information Technology (ICAIIT), Yogyakarta, Indonesia, p. 238–243, 2019, doi: 10.1109/ICAIIT.2019.8834474.
O. Iatrellis, A. Kameas, and P. Fitsilis, «Academic Advising Systems: A Systematic Literature Review of Empirical Evidence», Educ. Sci., vol. 7, no. 4, 2017, p. 90, doi: 10.3390/educsci7040090.
M. H. Mohamed and H. M. Waguih, «A proposed academic advisor model based on data mining classification techniques», Int. J. Adv. Comput. Res., vol. 8, no. 36, 2018, pp. 129–136, doi: 10.19101/IJACR.2018.836003.
D. Cruz, A. Basallo, M. III, B. Aguilar, J. Calvo, C. Arroyo, J. Delima, A. Jhone «Higher Education In-stitution (HEI) Enrollment Forecasting Using Data Mining Technique», Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 2, 2020, pp. 2060–2064, doi: 10.30534/ijatcse/2020/179922020.
L. Du and Q. Li, «A Data-Driven Approach to High-Volume Recruitment: Application to Student Admission», Manuf. Serv. Oper. Manag., vol. 22, no. 5, 2020, pp. 942–957, doi: 10.1287/msom.2019.0779.
A. Slim, «Predicting Student Enrollment Based on Student and College Characteristics», in Interna-tional Conference on Educational Data Mining (EDM), 11th, Raleigh, NC, Jul 16-20, 2018
A. C. Rivera, M. Tapia-Leon, and S. Lujan-Mora, «Recommendation Systems in Education: A Sys-tematic Mapping Study», in Proceedings of the International Conference on Information Technolo-gy & Systems (ICITS 2018), vol. 721, 2018, pp. 937–947. doi: 10.1007/978-3-319-73450-7_89.
S. Yazdipour and N. Taherian, «Data Driven Decision Support to Fund Graduate Studies in Abroad Universities», in International Conference on Machine Learning and Data Science (MLDS), Noida, pp. 44–50, 2017, doi: 10.1109/MLDS.2017.17.
D. J. Devarapalli, «Classification Method to Predict Chances of Students’ Admission in a Particular College», in Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, vol. 1245, pp. 225–238, 2017, doi: 10.1007/978-981-15-7234-0_19.
S. Sridhar, S. Mootha, and S. Kolagati, «A University Admission Prediction System using Stacked En-semble Learning», Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India, pp. 162–167, 2020, doi: 10.1109/ACCTHPA49271.2020.9213205.
C. Li, Z. Ma, H. Zhang, and Y. Liu, «The Prediction Model For College Admission Score Based On Support Vector Machine», ICIC Express Letters, Part B: Applications An International Journal of Research and Surveys, vol. 8, 2017, pp. 889–893.
N. Chakrabarty, S. Chowdhury, and S. Rana, «A Statistical Approach to Graduate Admissions’ Chance Prediction», Innovations in Computer Science and Engineering, vol. 103, 2020, pp. 333–340. doi: 10.1007/978-981-15-2043-3_38.
M. S. Acharya, A. Armaan, і A. S. Antony, «A Comparison of Regression Models for Prediction of Graduate Admissions», in International Conference on Computational Intelligence in Data Science (ICCIDS), pp. 1–5, 2019 doi: 10.1109/ICCIDS.2019.8862140.
N. Gupta, A. Sawhney, and D. Roth, «Will I Get in? Modeling the Graduate Admission Process for American Universities», IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain, 2016, pp. 631–638. doi: 10.1109/ICDMW.2016.0095.
S. Singhal і A. Sharma, «Prediction of Admission Process for Gradational Studies using Al Algo-rithm», Eur. J. Mol. Clin. Med., vol. 7, no. 4, с. 116–120, 2020.
M. A. Khan, M. Dixit, and A. Dixit, «Demystifying and Anticipating Graduate School Admissions us-ing Machine Learning Algorithms», IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India, 2020, pp. 19–25. doi: 10.1109/CSNT48778.2020.9115788.
Chithra, «Prediction for University Admission using Machine Learning», Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 4922–4926, 2020, doi: 10.35940/ijrte.F9043.038620.
Md. Protikuzzaman, M. Kanti, M. Kumar, and B. Chandra, «Predicting Undergraduate Admission: A Case Study in Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangla-desh», Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 12, 2020, doi: 10.14569/IJACSA.2020.0111217.
P. Nandal, «Deep Learning in diverse Computing and Network Applications Student Admission Pre-dictor using Deep Learning», in Proceedings of the International Conference on Innovative Compu-ting & Communications (ICICC), 2020, doi: 10.2139/ssrn.3562976.
A. Panchal and R. Nair, «College Recommendation System using Data Mining and Natural Lan-guage Processing», International Journal of Engineering Science and Computing, 2018.
B. Wu, Z. Ke, M. Fu, and Y. Xia, «SOUA: Towards Intelligent Recommendation for Applying for Overseas Universities», in International Conference on Intelligent Computing, Automation and Sys-tems (ICICAS), Chongqing, China, 2019, pp. 124–128. doi: 10.1109/ICICAS48597.2019.00033.
A. AlGhamdi, A. Barsheed, H. AlMshjary, and H. AlGhamdi, «A Machine Learning Approach for Graduate Admission Prediction», in Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing, Singapore, 2020, pp. 155–158. doi: 10.1145/3388818.3393716.
D. M. Khairina, F. Ramadhani, S. Maharani, and H. R. Hatta, «Department recommendations for prospective students Vocational High School of information technology with Naïve Bayes method», in 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, 2015, pp. 92–96. doi: 10.1109/ICITACEE.2015.7437777.
M. Hasan, S. Ahmed, D. Md. Abdullah, and Md. S. Rahman, «Graduate school recommender sys-tem: Assisting admission seekers to apply for graduate studies in appropriate graduate schools», in 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 2016, pp. 502–507. doi: 10.1109/ICIEV.2016.7760053.
A. Baskota and Y.-K. Ng, «A Graduate School Recommendation System Using the Multi-Class Sup-port Vector Machine and KNN Approaches», in IEEE International Conference on Information Re-use and Integration (IRI), Salt Lake City, UT, 2018, pp. 277–284. doi: 10.1109/IRI.2018.00050.
S. Aarthi, M. Sarvathanayan, and B. P. Kumar, «Post-Graduate College Admission Recommender Using Data Analytics», International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6, 2019.
W. A. Elnozahy, G. A. El Khayat, L. Cheniti-Belcadhi, and B. Said, «Question Answering System to Support University Students’ Orientation, Recruitment and Retention», Procedia Comput. Sci., vol. 164, pp. 56–63, 2019, doi: 10.1016/j.procs.2019.12.154.
S. Alghamdi, N. Alzhrani, and H. Algethami, «Fuzzy-Based Recommendation System for University Major Selection», in Proceedings of the 11th International Joint Conference on Computational In-telligence, Vienna, Austria, 2019, pp. 317–324. doi: 10.5220/0008071803170324.
V. Sharma, T. Trehan, R. Chanana, and S. Dawn, «StudieMe: College Recommendation System», in 3rd International Conference on Recent Developments in Control, Automation & Power Engineer-ing (RDCAPE), Noida, India, 2019, pp. 227–232. doi: 10.1109/RDCAPE47089.2019.8979030.
Subba Reddy, Y., Govindarajulu P., «College Recommender system using student’ prefer-ences/voting: A system development with empirical study». IJCSNS International Journal of Com-puter Science and Network Security, vol. 18, no. 1, 2018
D. J. Dhanashri, «College Recommendation System For Admission», International Research Journal of Engineering and Technology (IRJET), vol. 5, no. 3, pp. 1269–1272, 2018.
S. Ahmed, A. S. Md. L. Hoque, M. Hasan, R. Tasmin, D. Md. Abdullah, and A. Tabassum, «Discov-ering knowledge regarding academic profile of students pursuing graduate studies in world’s top uni-versities», in 2016 International Workshop on Computational Intelligence (IWCI), Dhaka, Bangla-desh, 2016, pp. 120–125. doi: 10.1109/IWCI.2016.7860351.
M. Qamhieh, H. Sammaneh, and M. N. Demaidi, «PCRS: Personalized Career-Path Recommender System for Engineering Students», IEEE Access, vol. 8, pp. 214039–214049, 2020, doi: 10.1109/ACCESS.2020.3040338.
T. Park і C. Kim, «Predicting the Variables That Determine University (Re-)Entrance as a Career De-velopment Using Support Vector Machines with Recursive Feature Elimination: The Case of South Korea», Sustainability, vol. 12, no. 18, p. 7365, 2020, doi: 10.3390/su12187365.
R. Ahlawat, S. Sahay, S. Sabitha, and A. Bansal, «Analysis of factors affecting enrollment pattern in Indian universities using k-means clustering», in 2016 International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things: Connect your Worlds, Noida, 2016, pp. 321–326. doi: 10.1109/INCITE.2016.7857639.
M. Farid Shamsudin, A. Mohd Ali, R. Ab Wahid, and Z. Saidun, «Factors Influence Undergraduate Students’ Decision Making To Enroll And Social Media Application As An External Factor», Human-it. Soc. Sci. Rev., vol. 7, no. 1, pp. 126–136, 2019, doi: 10.18510/hssr.2019.7116.
H. I. Patel, «Assessment of Affecting Factors for Higher Education Admission Process», Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 63–67, 2019, doi: 10.35940/ijeat.A1042.109119.
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