Abstract:

Introduction:
Students' computing identities in young ages play a critical role in shaping their continued engagement in computer science (CS) education, which has the potential to distribute the social and economic benefits of the CS field to the underprivileged. However, little research has been conducted to examine the computing identity of elementary school students, particularly those in non-tech-friendly provinces. To address this gap, the present study investigated the computing identity of elementary school students in a suburban school district in the U.S.

Method:
Participants were 399 of 3rd to 5th graders who participated in the CS education program in a school district located in a first ring suburb of a metropolitan area in Minnesota. The participants responded to a set of questionnaires to measure the three subconstructs of computing identity (interest, competence, and recognition in CS), motivation for future CS programs, and CS knowledge level. We conducted a latent profile analysis (LPA) to identify the hidden groups of students based on their computing identity. We also examined the extent to which these profiles are associated with their motivation for future CS programs and CS knowledge test scores through multivariate analysis of variance and the BCH method.

Results:
Three profile groups emerged, namely the ‘Interest-centered’ group (high interest but moderate competence and low recognition; 42%), the ‘Competence & Recognition-centered’ group (low interest but moderate competence and moderate recognition; 50%), and the ‘Overall High’ group (high interest, high competence, and high recognition; 8%). The estimated CS knowledge scores were significantly higher in the ‘Overall High’ group than the other two groups. Also, the estimated mean of motivation for future CS programs were highest in the ‘Overall High’ group followed by ‘Competence & Recognition-centered’ group and ‘Interest-centered’ group.

Discussion:
The present study identified three profile groups of computing identity in elementary students. The results showed that the ‘Overall High’ group, with high levels in all three sub-constructs, had the highest scores in CS knowledge and motivation for future CS programs. These findings underscore the importance of developing programs and interventions that foster all three subconstructs of computing identity. However, the proportion of students in the ‘Overall High’ group was found to be small compared to the other two groups in the sample. This suggests that there is a need for further research to understand the antecedents of computing identity subconstructs and strategies to improve elementary students' computing identity.

Keywords:

Computing identity, computer science education, computer science skills, computer science motivation, latent profile analysis.