Human versus Machine: Do College Advisors Outperform a Machine-Learning Algorithm in Predicting Student Enrollment? EdWorkingPaper No. 23-699

Autor/inn/en	Akmanchi, Suchitra; Bird, Kelli A.; Castleman, Benjamin L.
Institution	Annenberg Institute for School Reform at Brown University
Titel	Human versus Machine: Do College Advisors Outperform a Machine-Learning Algorithm in Predicting Student Enrollment? EdWorkingPaper No. 23-699
Quelle	(2023), (30 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Academic Advising; Artificial Intelligence; Algorithms; Prediction; High Achievement; Low Income Students; High School Students; College Enrollment + Suchen Sie Ihr Suchwort? Akademischer Rat; Künstliche Intelligenz; Algorithm; Algorithmus; Vorhersage; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin
Abstract	Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We compare algorithmic and human predictions in the context of a national college advising program, focusing in particular on predicting high-achieving, lower-income students' college enrollment quality. College advisors slightly outperform a prediction algorithm; however, greater advisor accuracy is concentrated among students with whom advisors had more interactions. The algorithm achieved similar accuracy among students lower in the distribution of interactions, despite advisors having substantially more information. We find no evidence that the advisors or algorithm exhibit bias against vulnerable populations. Our results suggest that, especially at scale, algorithms have the potential to provide efficient, accurate, and unbiased predictions to target scarce social services and resources. (As Provided).
Anmerkungen	Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: AISR_Info@brown.edu; Web site: http://www.annenberginstitute.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01