How Do Educational Experiences Predict Computing Identity?

Despite increasing demands for skilled workers within the technological domain, there is still a deficit in the number of graduates in computing fields (computer science, information technology, and computer engineering). Understanding the factors that contribute to students’ motivation and persistence is critical to helping educators, administrators, and industry professionals better focus efforts to improve academic outcomes and job placement. This article examines how experiences contribute to a student’s computing identity, which we define by their interest, recognition, sense of belonging, and competence/performance beliefs. In particular, we consider groups underrepresented in these disciplines, women and minoritized racial/ethnic groups (Black/African American and Hispanic/Latinx). To delve into these relationships, a survey of more than 1,600 students in computing fields was conducted at three metropolitan public universities in Florida. Regression was used to elucidate which experiences predict computing identity and how social identification (i.e., as female, Black/African American, and/or Hispanic/Latinx) may interact with these experiences. Our results suggest that several types of experiences positively predict a student’s computing identity, such as mentoring others, having a job, or having friends in computing. Moreover, certain experiences have a different effect on computing identity for female and Hispanic/Latinx students. More specifically, receiving academic advice from teaching assistants was more positive for female students, receiving advice from industry professionals was more negative for Hispanic/Latinx students, and receiving help on classwork from students in their class was more positive for Hispanic/Latinx students. Other experiences, while having the same effect on computing identity across students, were experienced at significantly different rates by females, Black/African American students, and Hispanic/Latinx students. The findings highlight experiential ways in which computing programs can foster computing identity development, particularly for underrepresented and marginalized groups in computing.

How Do Educational Experiences Predict Computing Identity?

  • Author Lunn, Stephanie; Ross, Monique; Hazari, Zahra; Weiss, Mark Allen; Georgiopoulos, Michael; Christensen, Kenneth
  • Publication Title ACM Transactions On Computing Education
  • Publication Year 2022
  • BPC Focus Gender, Underrepresented Racial/Ethnic Groups, Black/African American Students, Latinx/Hispanic
  • Methodology Survey, Qualitative, Multi-institution
  • Analytic Method Structural Equation Modeling (SEM), Correlation, Regression
  • Institution Type Minority Serving Institutions, Historically Black Colleges and Universities/Predominantly Black Institutions
  • DOI 10.1145/3470653
  • URL https://doi.org/10.1145/3470653