A Quantitative Machine Learning Approach to Master Students Admission for Professional Institutions [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Zhao, Yijun; Lackaye, Bryan; Dy, Jennifier G.; Brodley, Carla E.
Titel	A Quantitative Machine Learning Approach to Master Students Admission for Professional Institutions [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; College Admission; Masters Programs; Professional Education; College Applicants; Prediction; Computer Science Education; Graduate Students; Statistical Analysis; Massachusetts (Boston) + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Hochschulzugang; Hochschulzulassung; Zulassung; Magister course; Magisterstudiengang; Berufsausbildung; College applications; Studienbewerber; Vorhersage; Computer science lessons; Informatikunterricht; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Statistische Analyse
Abstract	Accurately predicting which students are best suited for graduate programs is beneficial to both students and colleges. In this paper, we propose a quantitative machine learning approach to predict an applicant's potential performance in the graduate program. Our work is based on a real world dataset consisting of MS in CS [Master of Science in Computer Science] students in the College of Computer and Information Science program at Northeastern University. We address two challenges associated with our task: subjectivity in the data due to change of admission committee membership from year to year and the shortage of training data. Our experimental results demonstrate an effective predictive model that could serve as a Focus of Attention (FOA) tool for an admission committee. [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