Combining Academics and Social Engagement: A Major-Specific Early Alert Method to Counter Student Attrition in Science, Technology, Engineering, and Mathematics

Autor/inn/en	Sage, Andrew J.; Cervato, Cinzia; Genschel, Ulrike; Ogilvie, Craig A.
Titel	Combining Academics and Social Engagement: A Major-Specific Early Alert Method to Counter Student Attrition in Science, Technology, Engineering, and Mathematics
Quelle	In: Journal of College Student Retention: Research, Theory & Practice, 22 (2021) 4, S.611-626 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sage, Andrew J.) ORCID (Ogilvie, Craig A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1521-0251
DOI	10.1177/1521025118780502
Schlagwörter	Majors (Students); Identification; Student Satisfaction; At Risk Students; STEM Education; College Freshmen; Student Participation; Communities of Practice; School Holding Power; Academic Persistence; Academic Achievement; Grade Point Average; Standardized Tests; Student Educational Objectives; Self Efficacy; Predictor Variables; Research Universities; Public Colleges; Interest Inventories; College Entrance Examinations; ACT Interest Inventory; ACT Assessment + Suchen Sie Ihr Suchwort? Identifikation; Identifizierung; STEM; Studienanfänger; Schülermitarbeit; Schülermitwirkung; Studentische Mitbestimmung; Community; Schulleistung; Standadised tests; Standardisierter Test; Self-efficacy; Selbstwirksamkeit; Prädiktor; Forschungseinrichtung; Interest profile; Interessenprofil; Aufnahmeprüfung; Assessment; Eignungsprüfung; Eignungstest; Hochschulzulassung
Abstract	Students are most likely to leave science, technology, engineering, and mathematics (STEM) majors during their first year of college. We developed an analytic approach using random forests to identify at-risk students. This method is deployable midway through the first semester and accounts for academic preparation, early engagement in university life, and performance on midterm exams. By accounting for cognitive and noncognitive factors, our method achieves stronger predictive performance than would be possible using cognitive or noncognitive factors alone. We show that it is more difficult to predict whether students will leave STEM than whether they will leave the institution. More factors contribute to STEM retention than to institutional retention. Early academic performance is the strongest predictor of STEM and institution retention. Social engagement is more predictive of institutional retention, while standardized test scores, goals, and interests are stronger predictors of STEM retention. Our approach assists universities to efficiently identify at-risk students and boost STEM retention. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01