dc.contributor.author | Zub, K. | en |
dc.contributor.author | Zhezhnych, P. | en |
dc.contributor.author | Зуб, Х. В. | uk |
dc.contributor.author | Жежнич, П. І. | uk |
dc.date.accessioned | 2023-03-21T12:47:26Z | |
dc.date.available | 2023-03-21T12:47:26Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Zub K. An overview of the current progress of the HEI’s support systems from the entrants’ perspectives [Text] / K. Zub, P. Zhezhnych // Інформаційні технології та комп'ютерна інженерія. – 2022. – № 1. – С. 28-36. | en |
dc.identifier.issn | 1999-9941 | |
dc.identifier.uri | http://ir.lib.vntu.edu.ua//handle/123456789/36503 | |
dc.description.abstract | One of the strategically important processes of the higher education institution activity is the enrollment campaign. In the infor-mation and knowledge society, the effectiveness of its implementation depends on many factors, one of which is the use of information tech-nology. 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 admis-sion, 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. | en |
dc.description.abstract | Одним із стратегічно важливих процесів діяльності вищого навчального закладу є вступна кампанія. В суспільстві інформації та знань ефективність його реалізації залежить від багатьох факторів, одним із яких є використання інформаційних технологій. Тому, метою цієї роботи є вивчення поточних досліджень та визначення їх тенденцій, спрямованих на підтримку прийняття рішень щодо закладу вищої освіти та спеціальності з точки зору абітурієнтів. У цьому літературному огляді використано наукомет-ричні бази даних Scopus та Web of Science, а також пошукову систему Google Scholar. Основні висновки полягають у виділення трьох напрямів досліджень, які створюють внесок у цій темі: прогнозування успішності вступу, рекомендація спеціальності або навчального закладу та дослідження факторів, що впливають на вибір абітурієнта. Огляд свідчить, що найпоширенішим є використання добування та аналізу даних для вирішення завдань дослідженнь. Результати цього дослідження дозволяють визначити ключові моменти, які є критичними на початковому етапі вирішення задач підтримки прийняття рішень, та виявити основні майбутні напрямки досліджень. | uk |
dc.language.iso | en | en |
dc.publisher | ВНТУ | uk |
dc.relation.ispartof | Інформаційні технології та комп'ютерна інженерія. № 1 : 28-36. | uk |
dc.relation.uri | https://itce.vntu.edu.ua/index.php/itce/article/view/860 | |
dc.subject | entrants | en |
dc.subject | higher educational institution (HEI) | en |
dc.subject | major | en |
dc.subject | decision support | en |
dc.subject | literature review | en |
dc.subject | вступники | uk |
dc.subject | заклад вищої освіти (ЗВО) | uk |
dc.subject | спеціальність | uk |
dc.subject | підтримка прийняття рішень | uk |
dc.subject | огляд літератури | uk |
dc.title | An overview of the current progress of the HEI’s support systems from the entrants’ perspectives | en |
dc.title.alternative | Огляд сучасного стану систем підтримки абітурієнтів закладів вищої освіти | uk |
dc.type | Article | |
dc.identifier.udc | 004.9 | |
dc.relation.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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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, In-donesia, p. 238–243, 2019, doi: 10.1109/ICAIIT.2019.8834474. | en |
dc.relation.references | O. Iatrellis, A. Kameas, and P. Fitsilis, «Academic Advising Systems: A Systematic Literature Re-view of Empirical Evidence», Educ. Sci., vol. 7, no. 4, 2017, p. 90, doi: 10.3390/educsci7040090. | en |
dc.relation.references | M. H. Mohamed and H. M. Waguih, «A proposed academic advisor model based on data mining clas-sification techniques», Int. J. Adv. Comput. Res., vol. 8, no. 36, 2018, pp. 129–136, doi: 10.19101/IJACR.2018.836003. | en |
dc.relation.references | D. Cruz, A. Basallo, M. III, B. Aguilar, J. Calvo, C. Arroyo, J. Delima, A. Jhone «Higher Education Institution (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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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 | en |
dc.relation.references | 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 Technology & Systems (ICITS 2018), vol. 721, 2018, pp. 937–947. doi: 10.1007/978-3-319-73450-7_89. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | S. Sridhar, S. Mootha, and S. Kolagati, «A University Admission Prediction System using Stacked Ensemble Learning», Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India, pp. 162–167, 2020, doi: 10.1109/ACCTHPA49271.2020.9213205. | en |
dc.relation.references | 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 Re-search and Surveys, vol. 8, 2017, pp. 889–893. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | M. A. Khan, M. Dixit, and A. Dixit, «Demystifying and Anticipating Graduate School Admissions using 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | A. Panchal and R. Nair, «College Recommendation System using Data Mining and Natural Language Processing», International Journal of Engineering Science and Computing, 2018. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | D. M. Khairina, F. Ramadhani, S. Maharani, and H. R. Hatta, «Department recommendations for pro-spective 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. | en |
dc.relation.references | M. Hasan, S. Ahmed, D. Md. Abdullah, and Md. S. Rahman, «Graduate school recommender system: Assisting admission seekers to apply for graduate studies in appropriate graduate schools», in 5th In-ternational Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 2016, pp. 502–507. doi: 10.1109/ICIEV.2016.7760053. | en |
dc.relation.references | 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 Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 277–284. doi: 10.1109/IRI.2018.00050. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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 Intel-ligence, Vienna, Austria, 2019, pp. 317–324. doi: 10.5220/0008071803170324. | en |
dc.relation.references | V. Sharma, T. Trehan, R. Chanana, and S. Dawn, «StudieMe: College Recommendation System», in 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 2019, pp. 227–232. doi: 10.1109/RDCAPE47089.2019.8979030. | en |
dc.relation.references | Subba Reddy, Y., Govindarajulu P., «College Recommender system using student’ prefer-ences/voting: A system development with empirical study». IJCSNS International Journal of Comput-er Science and Network Security, vol. 18, no. 1, 2018 | en |
dc.relation.references | D. J. Dhanashri, «College Recommendation System For Admission», International Research Journal of Engineering and Technology (IRJET), vol. 5, no. 3, pp. 1269–1272, 2018. | en |
dc.relation.references | 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. | en |
dc.relation.references | 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. | en |
dc.relation.references | T. Park і C. Kim, «Predicting the Variables That Determine University (Re-)Entrance as a Career Development 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. | en |
dc.relation.references | 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 Tech-nology (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. | en |
dc.relation.references | 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», Hu-manit. Soc. Sci. Rev., vol. 7, no. 1, pp. 126–136, 2019, doi: 10.18510/hssr.2019.7116. | en |
dc.relation.references | 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. | en |
dc.identifier.doi | https://doi.org/10.31649/1999-9941-2022-53-1-28-36 | |