Results of a Personalized Intervention to Promote Children’s and Adolescent’s Computer Programming Career Opportunities

• In Event: Children's and Adolescents' Technology Use to Enhance Cognitive and Educational Outcomes

Abstract

In the U.S., there is a deficit in the number of women and underrepresented minority (“URM”; i.e., African American, Hispanic/Latinx, American Indian/Alaskan Natives) students in STEM (NSF 2019; Estrada et al., 2016). This disparity may begin as early as middle school when children start to form identities around their academic interests which shapes their career trajectories (Blotnicky et al., 2018; Wang & Degol, 2013, 2017).
The present study seeks to address this issue by describing results from an intervention involving students’ use of a e-learning platform designed to promote students’ awareness and interest in computer science. By delivering curated culturally-responsive content matched to students’ interests and past ratings of activities, the web app creates personalized learning opportunities for students who have historically had less access and opportunities to careers in computer science. We hypothesized that (1) there would be a gradual increase in computer science attitudes during the intervention; and (2) such change would be greater among students who were female or from an underrepresented minority group in STEM.
Methods: During the 2018-2019 academic year, middle and high school students (N = 297) from partnering schools were invited to take part in the intervention (Mean age=12.39 years, SD age=.69 years; 45.8% female; 77.4% URM). Only students who returned signed consent documentation were enrolled in the study.
Students completed activities recommended by the app to earn “badges” (Mean=29.75, SD=32.79, Median=18). Five surveys were administered, approximately 2-3 months apart. A baseline survey measured attitudes towards math (9-items: 3 each for math identity, intrinsic motivation, and self-efficacy) and computer programming (12 items: 3 each for knowledge, self-efficacy, career aspirations, and interest) (AUTHOR, 2019). The 12-item computer programming scale was administered as a repeated measure on four additional occasions. For each dimension of the scale, an average scale score was derived (see Table 1).
Results: Four separate linear growth models were tested using as a repeated outcome measure one of the four dimensions of the computer programming attitudes (see Figure 1). All four models demonstrated acceptable fit (CFI>.90, TLI>.90, RMSEA<.06, SRMR<.08). There was a significant increase in career aspirations (𝛽=1.188, p<.05) and interest in computer programming (𝛽=1.185, p<.05). Figure 1 shows the model regression coefficients predicting variation in the latent intercept and slope. URM status, but not gender, accounted for growth in perceived knowledge (𝛽=.200, p<.05) and interest in computer programming (𝛽=.193, p<.05). Students with lower initial math identity tended to grow more rapidly in their career aspirations (𝛽=.432, p<.01), while students with lower initial math self-efficacy tended to grow more quickly in their interest (𝛽=.361, p<.05).
Discussion: Our findings suggest that URM students tended to have a greater improvement in both their perceived knowledge and interest in computer programming during the intervention. For students with lower initial math identity and self-efficacy, there was a greater rate of growth in career aspirations and interest, suggesting the closing of certain disparities. Particularly given that career aspirations and interest in computer programming appear malleable, future intervention work should target these constructs.

Group Authors

Curated Pathways to Innovation

Authors

• Teresa Marie Ober, University of Notre Dame
  Presenting Author

• Ying Cheng, University of Notre Dame
  Non-Presenting Author

• Tim Urdan, Santa Clara University
  Non-Presenting Author

• Paul Brenner, University of Notre Dame
  Non-Presenting Author

• Janice Zdankus, Hewlett Packard Enterprise
  Non-Presenting Author

• Philip Gonsalves, YWCA Silicon Valley
  Non-Presenting Author

• Emmanuel Johnson, YWCA Silicon Valley
  Non-Presenting Author

• Vanessa Martinez, YWCA Silicon Valley
  Non-Presenting Author