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Enregistrement W4416717975 · doi:10.3389/feduc.2025.1739990

Editorial: Inclusion of children with social-emotional or behavioral needs in early childhood education

2025· article· en· W4416717975 sur OpenAlexaboutno aff
Huichao Xie, Seaneen Sloan, Ching-I Chen, Laura Dunne

Notice bibliographique

RevueFrontiers in Education · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCollaborative Teaching and Inclusion
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInclusion (mineral)CoachingCitizen journalismEmpowermentParticipatory action researchEarly childhood educationCurriculumEarly childhood

Résumé

récupéré en direct d'OpenAlex

The seven articles in this Research Topic advance a shared commitment to understanding and improving inclusion through diverse methodologies, geographical contexts, and theoretical lenses. Together, they illustrate how educators, researchers, and policymakers can build systems that are not only equitable but also responsive to the lived realities of children, families, and communities.A central contribution of this Research Topic is the emphasis on co-construction and shared expertise. In Inclusive early childhood education: exploring co-creation and the process of empowerment within participatory research and practice, Carr-Fanning and Rihtman (2025) investigate how participatory research can empower educators and community partners to develop culturally grounded inclusion programs for children with ADHD-type behaviours in Hungary, Romania, and Slovakia. Through collaborative curriculum design and co-created professional learning, they demonstrate how inclusion emerges as both a process and an outcome of empowerment. Their findings underscore that meaningful inclusion is contextually defined and must evolve through reflective partnerships that challenge traditional hierarchies of expertise This participatory ethos resonates across the Research Topic, that is, inclusion cannot be achieved for communities but must be achieved with them. Empowerment, in this sense, becomes both the method and measure of inclusive success.Several contributions highlight the centrality of educator learning and reflective practice in sustaining inclusion. Dionne et al. (2025), in Supporting the inclusion of young children in childcare settings through professional development: perceptions of educators and managers, illustrate how sustained coaching and leadership engagement in Quebec childcare centers enhance educators' competence, confidence, and responsiveness to children's diverse needs. Their findings emphasize that professional learning must be iterative, relational, and embedded within educators' daily realities.In the U.S. context, Making connections for children and teachers: using classroom-based implementation supports for teaching Pyramid Model practices in Head Start programs by Bulotsky-Shearer et al. ( 2025) demonstrates how practice-based coaching and communities of practice can strengthen teachers' implementation of social-emotional learning strategies. These supports not only improve classroom quality but also sustain teacher well-beingcritical for inclusive, nurturing learning environments.Together, these studies affirm that inclusion is enacted through people before it is institutionalized in systems. Educator capacity building, especially collaborative, reflective, coaching-based professional development, drives classroom practices that create inclusive conditions (Dunst et al., 2019). Meta-analytic and synthesis work shows that sustained, collaborative PD with coaching and feedback reliably changes teacher practice and supports child outcomes (Brunsek et al., 2020), while conceptual work on inclusive pedagogy emphasizes that teachers' pedagogical judgement and reflective practice are the locus of inclusion (Florian & Black-Hawkins, 2011).Understanding how inclusion is shaped by local culture, social narratives, and policy frameworks is another key theme. In Navigating inclusion: understanding social perception, educational opportunity, and challenges for neurodiverse students in Bangladeshi formal education, Chowdhury et al. ( 2024) reveal how social attitudes, stigma, and systemic inequities affect the educational experiences of neurodiverse learners in Bangladesh. Their analysis underscores that inclusive reform must address deep-rooted societal narratives and resource disparities that perpetuate exclusion.Similarly, Portrayals of special educational needs in Norwegian ECEC psychoeducational reports: a document analysis in the context of inclusion by Kristiansen and Uthus (2025) explores how children's needs are framed within assessment documents, analysing tensions between deficit-based and holistic understandings of difference. Their work calls for integrating children's voices and contextual knowledge into assessment practices, emphasizing that inclusion depends as much on discourse as on pedagogy.The meanings of 'diversity,' 'need,' and 'support' are interpreted through social and institutional lenses. Cross-national reviews and studies show that family roles, teacher judgments, and policy frames shape how inclusion is understood and enacted (Acar, Chen & Xie, 2021;Chan, 2011). Consequently, while inclusion is a universal value, its realization must be culturally constructed to align with community visions and priorities (McCoy, 2022;Arndt, 2018).Curriculum and data serve as vital levers for promoting inclusion when used to inform practice. Clayback et al. (2024) contribute two key studies that illustrate this balance. In Supporting all learners through high quality early childhood curricula: STREAMin3 implementation across Virginia, they present a curriculum model that integrates academic, social, and emotional learning through five Core Skills and six STREAM domains. The design promotes coherence and flexibility, allowing educators to adapt to diverse learning contexts.Their follow-up article, Using data to promote inclusion through early childhood mental health consultation (2025), explores how data-driven reflection supports educators in addressing behavioral challenges without resorting to exclusionary discipline. Here, data function as tools for self-awareness and systemic learning rather than surveillance. Datadriven decision making reinforces the principle that inclusive practice is responsive to the children and families it is serving, not prescriptive.Research on data-driven decision-making in early childhood settings emphasizes that when educators use data collaboratively and reflectively, it strengthens equitable instructional responses and child outcomes rather than narrowing practice (Sheridan et al., 2009). Similarly, high-quality, inclusive practices are most effective when they balance fidelity with flexibility, allowing teachers to adapt to diverse cultural and developmental contexts (Harn et al., 2013). Studies on practice-based coaching further highlight that embedding data cycles within curriculum implementation supports educators' reflection, confidence, and intentional teaching (Snyder et al., 2015). The articles featured in this Research Topic reinforces that curriculum and data are not neutral tools. Rather, they become instruments of inclusion when applied through relational, reflective, and contextually responsive professional practice.Across seven interlinked studies spanning Bangladesh, Norway, Canada, the United States, and Central and Eastern Europe, this Research Topic advances a holistic vision of inclusion, one that is data-informed, culturally responsive, and relationally grounded. We expect these contributions demonstrate the four unifying insights as described above. Together, these studies challenge us to envision inclusion not as a fixed endpoint but as a continual, contextsensitive journey of reflection, collaboration, and empowerment. As education systems worldwide strive to ensure that no learner is left behind, the insights from this Research Topic illuminate practical and philosophical pathways for realizing truly inclusive early childhood education, where every child, educator, and community can participate fully and flourish.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,285
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

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

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 Opus0,007
Tête enseignante GPT0,312
Écart entre enseignants0,306 · 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 tête enseignante, 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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