Predicting Student Performance in a Master of Data Science Program Using Admissions Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Zhao, Yijun; Xu, Qiangwen; Chen, Ming; Weiss, Gary M.
Titel	Predicting Student Performance in a Master of Data Science Program Using Admissions Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (9 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Grade Prediction; Data Analysis; Masters Programs; Admission Criteria; College Admission; Data Use; Graduate Study; College Entrance Examinations; Language Tests; English (Second Language); Second Language Learning; Graduate Record Examinations; Test of English as a Foreign Language + Suchen Sie Ihr Suchwort? Auswertung; Magister course; Magisterstudiengang; Admission; Admission procedures; Zulassungsbedingung; Zulassungsverfahren; Zulassung; Hochschulzugang; Hochschulzulassung; Aufbaustudium; Graduiertenstudium; Hauptstudium; Aufnahmeprüfung; Language test; Sprachtest; English as second language; English; Second Language; Englisch als Zweitsprache; Zweitsprachenerwerb
Abstract	Predicting student success in a data science degree program is a challenging task due to the interdisciplinary nature of the field, the diverse backgrounds of the students, and an incomplete understanding of the precise skills that are most critical to success. In this study, the applicant's future academic performance in a Master of Data Science program is assessed using information from the admission application, such as standardized test scores, undergraduate grade point average, declared major, and school ranking. Simple data analysis methods and visualization techniques are used to gain a better understanding of how these variables impact student performance, and several classification algorithms are used to induce models to distinguish between students that will perform very well and those that will perform very poorly. Historical admissions and grading data are used to perform these analyses and build the classification models. The analyses and predictive models that are generated provide insight into the factors that identify good and poor candidates, and can aid in future admissions decisions. [For the full proceedings, see ED607784.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01