Creative Coding Communities: Exploring the Relationship Between Scratch Use and Child Well-Being
Bibliographic record
Abstract
This international study assessed the relationship between the use of Scratch, the world's largest coding community for children, and child well-being. Specifically, the study focused on the presence of well-being indicators in the RITEC-8 framework (RITEC, 2024) including creativity, relationships, competence, and autonomy within Scratch users’ behaviors. The participants (N = 17,315) included children aged between 8 and 16 who used Scratch in 2023 and came from 29 countries. The study examined trends in Scratchers’ behaviors including project creation, sharing, studio curation, and socialization based on their country of origin, gender and age. The data were used to construct user profiles to examine whether there was variability in these profiles based on users’ country of origin, gender, age, and cultural orientation. User profiles were created using negative binomial grade of membership factorization and patterns in profile variability based on demographic factors were explored via multinomial logistic regression. Findings revealed three user profiles: 1) Code-Heavy Creators, 2) Social Creators, and 3) Remix-Heavy Content Contributors. Code-Heavy Creators primarily focused on complex coding projects, reflecting high levels of competence. Social Creators engaged heavily in commenting, project loving, and studio curation, demonstrating a strong inclination toward building relationships and community engagement. Remix-Heavy Content Contributors remixed and shared projects frequently, exhibiting a blend of all well-being indicators, showing high creativity, competence, and engagement in collaborative work. A ternary diagram was used to visualize the degree of membership each user had within different profiles. The dense clustering along the edges between social creators and remix-heavy content contributors, as well as between code-heavy creators and remix-heavy content contributors, indicated that many users exhibited mixed characteristics of these profiles, suggesting that behaviors associated with these profiles may not be entirely distinct, but rather part of a behavioral continuum. The relative sparsity of extreme cases in social creators further suggested that this profile may be less frequent among Scratch users, with users displaying a mix of behaviors rather than strongly aligning with this particular profile. Age, gender, country of origin, and cultural orientation emerged as significant predictors of profile membership. The United States and Japan stood out with the highest rates of project sharing and commenting behavior, respectively. These two countries also had overwhelmingly higher odds of engaging in social creation, as opposed to Brazil, Mexico, and France favoring code-heavy creation. In addition, gender-based differences in Scratch behavior indicated that project sharing and commenting were predominantly done by boys, however when narrowing behavioral trends for girls, commenting was highest for girls in Canada. Non-binary users and those who preferred not to disclose their gender had significantly higher odds of engaging in social creation. The study also found a clear developmental trajectory in Scratch engagement, where younger users engaged in fewer behaviors (e.g., commenting, creation, remixing), and increased over time, peaking between ages 10 to 14, and then decreasing as they got older. Lastly, users from individualistic cultures were nearly twice as likely to engage in social behaviors compared to users from collectivistic cultures. In addition, users from collectivistic cultures users were largely categorized into the remix-heavy content contributors' profile, thus engaging in more collaboration and community involvement. These findings highlight significant demographic patterns in digital engagement and suggest that individual and cultural factors shape how users interact with the platform. Results support the need to explore potential avenues to modify digital platforms to incorporate more intentional design to propel groups to engage in digitally literate behaviors that do not typically engage in these behaviors. Supports for girls across Scratch features including sharing, assisting girls in gaining coding competence, and offering a pathway to find spaces to ask for help or share thoughts and ideas with other Scratch users would be beneficial. These findings demonstrate that gender diversity shapes participation in digital creative spaces and suggest that inclusive and supportive design elements may be important in fostering engagement among gender-diverse users. In addition, trends across countries that indicate countries have lower levels of sharing, and commenting, offer an opportunity to site developers to integrate features that target these geographic locations and offer similar supports. The increase in sharing and remixing behaviors among older children (10-14) suggests that as they develop digital skills and confidence, they gain a stronger sense of agency over their creative work. Encouraging young Scratch users to share and remix earlier may help build this sense of autonomy. The study expanded upon the implications that these behavioral trends have on global use of digital, instructional technology and technology’s potential to impact various communities of children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".