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Developing a Hybrid Model to Predict Student First Year Retention in STEM Disciplines Using Machine Learning Techniques
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Journal of STEM Education Volume 15, Number 3, ISSN 1557-5284 Publisher: Laboratory for Innovative Technology in Engineering Education (LITEE)

Abstract

Citation

Alkhasawneh, R. & Hargraves, R. (2014). Developing a Hybrid Model to Predict Student First Year Retention in STEM Disciplines Using Machine Learning Techniques. Journal of STEM Education, 15(3),. Laboratory for Innovative Technology in Engineering Education (LITEE). Retrieved May 17, 2022 from .

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