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Enregistrement W7008701626

Creative Coding Communities: Exploring the Relationship Between Scratch Use and Child Well-Being

2025· dissertation· en· W7008701626 sur OpenAlexaboutno aff

Notice bibliographique

RevueCUNY Academic Works (City University of New York) · 2025
Typedissertation
Langueen
DomainePsychology
ThématiqueCreativity in Education and Neuroscience
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStudioCoding (social sciences)Cluster analysisMultinomial logistic regressionAutonomySchema (genetic algorithms)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,003
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,182
Tête enseignante GPT0,342
Écart entre enseignants0,161 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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