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. 2021 Dec 21:7:e807.
doi: 10.7717/peerj-cs.807. eCollection 2021.

Who participates in computer science education studies? A literature review on K-12 subjects

Affiliations

Who participates in computer science education studies? A literature review on K-12 subjects

Anna van der Meulen et al. PeerJ Comput Sci. .

Abstract

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014-2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Keywords: Computer science education; K12; Literature review.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Studies categorized by the reported percentage of male students.
Figure 2
Figure 2. Studies categorized by the reported percentage of white students.
Figure 3
Figure 3. Studies categorized by the reported number of different races/ethnicities.
Figure 4
Figure 4. Studies categorized by the reported percentage of students with low socio-economic status (28 studies in total).
Figure 5
Figure 5. Studies categorized by the reported percentage of students with experience in programming (52 studies in total).

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