Using Random Forest Analysis to Identify Student Demographic and High School-Level Factors That Predict College Engineering Major Choice

Autor/inn/en	Tan, Li; Main, Joyce B.; Darolia, Rajeev
Titel	Using Random Forest Analysis to Identify Student Demographic and High School-Level Factors That Predict College Engineering Major Choice
Quelle	In: Journal of Engineering Education, 110 (2021) 3, S.572-593 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Main, Joyce B.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1069-4730
DOI	10.1002/jee.20393
Schlagwörter	High School Students; Predictor Variables; Majors (Students); Engineering Education; Student Characteristics; Demography; Longitudinal Studies; Class Rank; Mathematics Achievement; Student Interests; Science Interests; Gender Differences; High School Longitudinal Study of 2009 (NCES) + Suchen Sie Ihr Suchwort? High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Prädiktor; Ingenieurausbildung; Demografie; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Studieninteresse; Geschlechterkonflikt
Abstract	Background: Given the importance of engineers to a nation's economy and potential innovation, it is imperative to encourage more students to consider engineering as a college major. Previous studies have identified a broad range of high school experiences and demographic factors associated with engineering major choice; however, these factors have rarely been ranked or ordered by relative importance. Purpose/Hypothesis: This study leveraged comprehensive, longitudinal data to identify which high school-level factors, including high school characteristics and student high school experiences as well as student demographic characteristics and background, rank as most important in terms of predictive power of engineering major choice. Design/Method: Using data from a nationally representative survey, the High School Longitudinal Study of 2009, and the random forest method, a genre of machine learning, the most important high school-level factors in terms of predictive power of engineering major choice were ranked. Results: Random forest results indicate that student gender is the most important variable predicting engineering major choice, followed by high school math achievement and student beliefs and interests in math and science during high school. Conclusions: Gender differences in engineering major choice suggest wider ranging cultural phenomena that need further investigation and systemic interventions. Research findings also highlight two other areas for potential interventions to promote engineering major choice: high school math achievement and beliefs and interests in math and science. Focusing interventions in these areas may lead to an increase in the number of students pursuing engineering. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01