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Autor/inn/enTomkin, Jonathan H.; West, Matthew; Herman, Geoffrey L.
TitelAn Improved Grade Point Average, with Applications to CS Undergraduate Education Analytics
QuelleIn: ACM Transactions on Computing Education, 18 (2018) 4, Artikel 17 (16 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1946-6226
SchlagwörterGrade Point Average; Grading; Predictor Variables; Grades (Scholastic); Computer Science Education; Majors (Students); Correlation; Standardized Tests; Scores; Undergraduate Students; Required Courses; Gender Differences; STEM Education; Illinois
AbstractWe present a methodological improvement for calculating Grade Point Averages (GPAs). Heterogeneity in grading between courses systematically biases observed GPAs for individual students: the GPA observed depends on course selection. We show how a logistic model can account for course selection by simulating how every student in a sample would perform if they took all available courses, giving a new "modeled GPA." We then use 10 years of grade data from a large university to demonstrate that this modeled GPA is a more accurate predictor of student performance in individual courses than the observed GPA. Using Computer Science (CS) as an example learning analytics application, it is found that required CS courses give significantly lower grades than average courses. This depresses the recorded GPAs of CS majors: modeled GPAs are 0.25 points higher than those that are observed. The modeled GPA also correlates much more closely with standardized test scores than the observed GPA: the correlation with Math ACT is 0.37 for the modeled GPA and is 0.20 for the observed GPA. This implies that standardized test scores are much better predictors of student performance than might otherwise be assumed. (As Provided).
AnmerkungenAssociation for Computing Machinery. 2 Penn Plaza Suite 701, New York, NY 10121. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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