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ERIC Number: EJ1048907
Record Type: Journal
Publication Date: 2014
Pages: 8
Abstractor: ERIC
ISSN: EISSN-1557-5284
Developing a Hybrid Model to Predict Student First Year Retention in STEM Disciplines Using Machine Learning Techniques
Alkhasawneh, Ruba; Hargraves, Rosalyn Hobson
Journal of STEM Education: Innovations and Research, v15 n3 p35-42 Oct-Dec 2014
The purpose of this research was to develop a hybrid framework to model first year student retention for underrepresented minority (URM) students comprising African Americans, Hispanic Americans, and Native Americans. Identifying inputs that best contribute to student retention provides significant information for institutions to learn about student needs, how to support student academic success, and how to increase retention in STEM fields. Institutions can also rely on using qualitative analysis to examine students' experiences during the freshman year to acquire useful information on different student retention behaviors from a diverse population. Based on this information, better programs and student services can be developed.
Institute for STEM Education and Research. P.O. Box 4001, Auburn, AL 36831. Tel: 334-844-3360; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A