The Association between Female Students' Computer Science Education and STEM Major Selection: Multilevel Structural Equation Modeling

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Author(s): Lee, Ahlam
  • Language:
    English
  • Source:
    Computers in the Schools. 2020 37(1):17-39.
  • Publication Date:
    2020
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
    • Peer Reviewed:
      Y
    • Source:
      23
    • Education Level:
      Higher Education
      Postsecondary Education
      High Schools
      Secondary Education
    • Subject Terms:
      Females; Gender Differences; Computer Science Education; Disproportionate Representation; Self Efficacy; STEM Education; College Credits; Correlation; Majors (Students); Course Selection (Students); Student Attitudes; Longitudinal Studies; High School Students; Predictor Variables; Structural Equation Models; Mathematics Achievement; Mathematics Skills; College Entrance Examinations; Scores; Mathematics Tests; Educational Strategies
    • Subject Terms:
      Education Longitudinal Study of 2002 (NCES); ACT Assessment
    • Accession Number:
      10.1080/07380569.2020.1720553
    • ISSN:
      0738-0569
    • Abstract:
      Concerning the underrepresentation of female students in computer science (CS) classes at the K-12 level and math-intensive STEM fields, this study investigated the relationship between female students earning less credits in CS courses during high school and their STEM major choices. Data were drawn from a nationally representative sample of U.S. high school graduates from the restricted data of the Educational Longitudinal Study of 2002 which were collected by the National Center for Educational Statistics. The multilevel structural equation modeling revealed that (a) female students' lower frequency of taking CS courses was associated significantly with their underrepresentation in STEM fields, (b) earning less credits in CS courses was a stronger factor for female students' underrepresentation in STEM fields compared to the well-documented predictors for STEM major choice--math self-efficacy and math scores on the ACT, and (c) no significant difference in ACT math scores emerged between male and female students, which challenges the dominant view that male students' greater math aptitude causes the persistent gender gap that leads to women's underrepresentation in STEM fields. The study suggests that integrating CS in traditional classrooms as well as promoting a rigorous CS curriculum at the secondary level would be an effective strategy to increase female participation in math-intensive STEM fields.
    • Abstract:
      As Provided
    • Publication Date:
      2020
    • Accession Number:
      EJ1248210