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Sage, Andrew J.; Cervato, Cinzia; Genschel, Ulrike; Ogilvie, Craig A. – Journal of College Student Retention: Research, Theory & Practice, 2021
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…
Descriptors: Majors (Students), Identification, Student Satisfaction, At Risk Students
Trosset, Carol; Evertz, Kathy; Fitzpatrick, Renata – Learning Assistance Review, 2019
To demonstrate effectiveness, writing centers collect quantitative and/or qualitative information about and from the students who use their services. A broader understanding of effectiveness requires us to consider both direct measures of writing quality and why some students do not use the writing center. This mixed-methods research followed one…
Descriptors: Laboratories, Writing Achievement, Use Studies, Consultants