{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:08:49Z","timestamp":1755796129737,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031357305"},{"type":"electronic","value":"9783031357312"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-35731-2_22","type":"book-chapter","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T19:01:39Z","timestamp":1688842899000},"page":"250-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predictive Analytics for University Student Admission: A Literature Review"],"prefix":"10.1007","author":[{"given":"Kam Cheong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Billy Tak-Ming","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hon Tung","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,9]]},"reference":[{"issue":"5","key":"22_CR1","first-page":"30","volume":"46","author":"P Long","year":"2011","unstructured":"Long, P., Siemens, G.: Penetrating the fog: analytics in learning and education. EDUCAUSE Review 46(5), 30\u201340 (2011)","journal-title":"EDUCAUSE Review"},{"key":"22_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s12528-022-09324-3","author":"BTM Wong","year":"2022","unstructured":"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","journal-title":"J. Comput. High. Educ."},{"issue":"2","key":"22_CR3","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1108\/ITSE-12-2017-0065","volume":"15","author":"BTM Wong","year":"2018","unstructured":"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\u2013154 (2018)","journal-title":"Interact. Technol. Smart Educ."},{"issue":"3\/4","key":"22_CR4","first-page":"293","volume":"12","author":"KC Li","year":"2018","unstructured":"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\u2013308 (2018)","journal-title":"Int. J. Serv. Stand."},{"issue":"1","key":"22_CR5","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1108\/AAOUJ-01-2017-0009","volume":"12","author":"BTM Wong","year":"2017","unstructured":"Wong, B.T.M.: Learning analytics in higher education: an analysis of case studies. Asian Assoc. Open Univ. J. 12(1), 21\u201340 (2017)","journal-title":"Asian Assoc. Open Univ. J."},{"key":"22_CR6","unstructured":"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\u20131089 (2007)"},{"key":"22_CR7","unstructured":"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\u2013389 (2018)"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"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\u2013214 (2008)","DOI":"10.1145\/1593105.1593159"},{"issue":"1","key":"22_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.econedurev.2011.07.005","volume":"31","author":"P Nurnberg","year":"2012","unstructured":"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\u20138 (2012)","journal-title":"Econ. Educ. Rev."},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"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\u2013417 (2016)","DOI":"10.1145\/2903150.2911717"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Jamison, J.: Applying machine learning to predict Davidson college\u2019s admissions yield. In: Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, pp. 765\u2013766 (2017)","DOI":"10.1145\/3017680.3022468"},{"issue":"1","key":"22_CR12","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s40692-019-00143-7","volume":"7","author":"B-M Wong","year":"2019","unstructured":"Wong, B.-M., Li, K.C.: A review of learning analytics intervention in higher education (2011\u20132018). J. Comput. Educ. 7(1), 7\u201328 (2019). https:\/\/doi.org\/10.1007\/s40692-019-00143-7","journal-title":"J. Comput. Educ."},{"issue":"1","key":"22_CR13","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1504\/IJMLO.2020.103901","volume":"14","author":"KC Li","year":"2020","unstructured":"Li, K.C., Wong, B.T.M.: The use of student response systems with learning analytics: a review of case studies (2008\u20132017). Int. J. Mob. Learn. Organ. 14(1), 63\u201379 (2020)","journal-title":"Int. J. Mob. Learn. Organ."},{"issue":"22","key":"22_CR14","doi-asserted-by":"publisher","first-page":"10907","DOI":"10.3390\/app112210907","volume":"11","author":"B Sekeroglu","year":"2021","unstructured":"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)","journal-title":"Appl. Sci."},{"key":"22_CR15","first-page":"8924028","volume":"2022","author":"SA Alwarthan","year":"2022","unstructured":"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)","journal-title":"Appl. Comput. Intell. Soft Comput."},{"issue":"4","key":"22_CR16","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.cptl.2017.12.004","volume":"10","author":"RE Wilcox","year":"2018","unstructured":"Wilcox, R.E., Lawson, K.A.: Predicting performance in health professions education programs from admissions information \u2013 comparisons of other health professions with pharmacy. Curr. Pharm. Teach. Learn. 10(4), 529\u2013541 (2018)","journal-title":"Curr. Pharm. Teach. Learn."},{"key":"22_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.nepr.2020.102865","volume":"48","author":"R Al-Alawi","year":"2020","unstructured":"Al-Alawi, R., Oliver, G., Donaldson, J.F.: Systematic review: predictors of students\u2019 success in baccalaureate nursing programs. Nurse Educ. Pract. 48, 102865 (2020)","journal-title":"Nurse Educ. Pract."},{"issue":"5815","key":"22_CR18","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1126\/science.1136618","volume":"315","author":"NR Kuncel","year":"2007","unstructured":"Kuncel, N.R., Hezlett, S.A.: Standardized tests predict graduate students\u2019 success. Science 315(5815), 1080\u20131081 (2007)","journal-title":"Science"},{"issue":"5","key":"22_CR19","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1080\/02602938.2021.1958142","volume":"47","author":"T de Boer","year":"2022","unstructured":"de Boer, T., Van Rijnsoever, F.: In search of valid non-cognitive student selection criteria. Assess. Eval. High. Educ. 47(5), 783\u2013800 (2022)","journal-title":"Assess. Eval. High. Educ."},{"issue":"69","key":"22_CR20","first-page":"1","volume":"11","author":"A Parlina","year":"2020","unstructured":"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\u201326 (2020)","journal-title":"Information"},{"issue":"3","key":"22_CR21","doi-asserted-by":"publisher","first-page":"204","DOI":"10.3103\/S0147688219030109","volume":"46","author":"IV Selivanova","year":"2019","unstructured":"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\u2013212 (2019)","journal-title":"Sci. Tech. Inf. Process."},{"issue":"2","key":"22_CR22","first-page":"77","volume":"17","author":"V Mahnic","year":"2015","unstructured":"Mahnic, V.: Scrum in software engineering courses: an outline of the literature. Glob. J. Eng. Educ. 17(2), 77\u201383 (2015)","journal-title":"Glob. J. Eng. Educ."},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/EPEC48502.2020.9320119"},{"issue":"2","key":"22_CR24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3991\/ijoe.v17i02.20049","volume":"17","author":"I El Guabassi","year":"2021","unstructured":"El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A.: A recommender system for predicting students\u2019 admission to a graduate program using machine learning algorithms. Int. J. Online Biomed. Eng. 17(2), 135\u2013147 (2021)","journal-title":"Int. J. Online Biomed. Eng."},{"key":"22_CR25","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10462-022-10171-y","volume":"56","author":"M Kiaghadi","year":"2022","unstructured":"Kiaghadi, M., Hoseinpour, P.: University admission process: a prescriptive analytics approach. Artif. Intell. Rev. 56, 233\u2013256 (2022)","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"22_CR26","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1057\/eej.2010.3","volume":"37","author":"JF Ragan","year":"2011","unstructured":"Ragan, J.F., Li, D., Matos-D\u00edaz, H.: Using admission tests to predict success in college evidence from the University of Puerto Rico. East. Econ. J. 37(4), 470\u2013487 (2011)","journal-title":"East. Econ. J."},{"issue":"4","key":"22_CR27","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1002\/j.2168-9830.2009.tb01035.x","volume":"98","author":"IW Wait","year":"2009","unstructured":"Wait, I.W., Gressel, J.W.: Relationship between TOEFL score and academic success for international engineering students. J. Eng. Educ. 98(4), 389\u2013398 (2009)","journal-title":"J. Eng. Educ."},{"issue":"8","key":"22_CR28","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3991\/ijet.v17i08.29827","volume":"17","author":"N Matar","year":"2022","unstructured":"Matar, N., Matar, W., Al Malahmeh, T.: Predictive model for students\u2019 admission uncertainty using Na\u00efve Bayes classifier and Kernel Density Estimation (KDE). Int. J. Emerg. Technol. Learn. 17(8), 75\u201396 (2022)","journal-title":"Int. J. Emerg. Technol. Learn."},{"issue":"12","key":"22_CR29","first-page":"138","volume":"11","author":"M Protikuzzaman","year":"2020","unstructured":"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\u2013145 (2020)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/ICCIDS.2019.8862140"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/SoutheastCon44009.2020.9249747"},{"key":"22_CR32","doi-asserted-by":"crossref","unstructured":"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\u2013F2A6 (2007)","DOI":"10.1109\/FIE.2007.4417958"},{"issue":"1","key":"22_CR33","first-page":"64","volume":"35","author":"A Waters","year":"2014","unstructured":"Waters, A., Miikkulainen, R.: Grade: machine-learning support for graduate admissions. AI Mag. 35(1), 64\u201375 (2014)","journal-title":"AI Mag."},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Gao, Z., Gatpandan, M.P., Gatpandan, P.H.: Classification decision tree algorithm in predicting students\u2019 course preference. In: Proceedings of the 2nd International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2021, pp. 93\u201397 (2021)","DOI":"10.1109\/ISCEIC53685.2021.00026"},{"issue":"9042216","key":"22_CR35","doi-asserted-by":"publisher","first-page":"55462","DOI":"10.1109\/ACCESS.2020.2981905","volume":"8","author":"HA Mengash","year":"2020","unstructured":"Mengash, H.A.: Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access 8(9042216), 55462\u201355470 (2020)","journal-title":"IEEE Access"},{"key":"22_CR36","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.procs.2018.10.166","volume":"141","author":"S Al-Saqqa","year":"2018","unstructured":"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\u2013189 (2018)","journal-title":"Procedia Comput. Sci."}],"container-title":["Lecture Notes in Computer Science","Blended Learning : Lessons Learned and Ways Forward"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35731-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T21:11:18Z","timestamp":1729717878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35731-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031357305","9783031357312"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35731-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Blended Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbl2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hksmic.org.hk\/icbl\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.16","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.24","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}