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Record 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 on OpenAlexaboutno aff
Huichao Xie, Seaneen Sloan, Ching-I Chen, Laura Dunne

Bibliographic record

VenueFrontiers in Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)CoachingCitizen journalismEmpowermentParticipatory action researchEarly childhood educationCurriculumEarly childhood

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.312
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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