MétaCan
Menu
Retour à la cohorte
Enregistrement W4235948536 · doi:10.18357/jcs.v42i1.16889

Call for Papers - Innovative Professional Learning in Early Childhood Education and Care: Inspiring Hope and Action

2017· paratext· en· W4235948536 sur OpenAlexvenueaboutno aff
Journal of Childhood Studies

Notice bibliographique

RevueJournal of Childhood Studies · 2017
Typeparatext
Langueen
DomainePsychology
ThématiqueEducational and Psychological Assessments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTransformative learningProfessional developmentEarly childhood educationProfessional learning communityPsychologyPedagogySociology

Résumé

récupéré en direct d'OpenAlex

Guest editors: Joanne Lehrer (Université du Québec en Outaouais), Christine Massing (University of Regina), Scott Hughes (Mount Royal University), and Alaina Roach O’Keefe (University of Prince Edward Island) Not only is professional learning conceptualised as critical for increasing educational quality and enhancing children’s learning and developmental outcomes (e.g. Lazarri et al., 2013; Munton et al., 2002; Penn, 2009; Vandenbroeck et al., 2016), but specific elements of professional learning (in both initial and continuing education, or preservice and in-service learning) have been identified as essential to transforming early childhood educators’ and preschool teachers’ professional identities and practice. For example, critical and supported reflection (Thomas & Packer, 2013), learning experiences that target entire teams (Vangrieken, Dochy, & Raes, 2016), collaborative and empowering practice (Helterbran & Fennimore, 2004), and competent leadership (Colmer et al., 2008) have all been found to be effective means of supporting professional learning. While there appears to be consensus in the literature around what needs to be done, and even around how it should be done, numerous constraints prevent the implementation and maintenance of sustainable and transformational professional learning in ECEC. Vandenbroeck and colleagues (2016) go beyond the focus on individuals and childcare teams, identifying two further levels necessary for competent systems of professional learning: partnerships between local early childhood programs and social, cultural, and educational institutions (such as colleges and universities); and governance regarding vision, finance, and monitoring. In the Canadian context, the Canadian Child Care Federation has also stressed the importance of a system-wide strategy to strengthen the child care workforce (CCCF, 2016). However, early childhood services in Canada are under the purview of the provincial and territorial governments and, therefore, the conditions, regulations, certification requirements, curriculum documents, and educational systems vary widely from jurisdiction to jurisdiction. The educational requirements for certification, for example, may include no formal training (in NWT and Nunavut), one entry-level short course, one-year certificates, or two-year diplomas. This complicates efforts to define who the early childhood professional is and what opportunities are constitutive of professional learning (Prochner, Cleghorn, Kirova, & Massing, 2016). While these disparities within the field may impede the development of a cohesive strategy, Campbell et al. (2016) recently asserted that much can be learned from sharing and appreciating the rich diversity of approaches to professional learning both within and across provinces and territories. In addition, examples from other countries serve to broaden the discussion and expand our understanding of what is possible (Vandenboreock et al., 2016). This special issue, then, is dedicated to sharing stories of hope and coordinated action, linking theory with practice. We seek Canadian and international submissions related to professional learning practices that extend beyond individual programs, showcasing partnerships and community mobilization efforts within and across various settings for young children (child care, Kindergarten, drop-in centres, etc.) in relation to philosophical, practical, critical, transformative, personal, and/or hopeful themes. Each submission will respond to one or more of the key questions, including, but not limited to: How can professional learning be conceptualised? How do we build and maintain effective partnerships to foster professional learning? What strategies for transformative community mobilization might be shared? How can innovative strategies be applied on a wider scale? How might taken-for-granted professional learning and evaluation practice be disrupted? What story about professional learning do you need (or want) to tell? How has your community been transformed through a particular activity, event, or practice? How might the lives and futures of children be positively shaped by engagement in partnerships and mobilization? Where might we be in 5, 10, or 15 years through such endeavours? We welcome submissions in multiple formats, including research articles, theoretical papers, multimedia pieces, art work, book reviews, and so forth. These may be submitted in English, French, or in any Canadian Indigenous language. Submissions are due August 1, 2017 and should be submitted as per Journal of Childhood Studies submission guidelines. References Campbell, C., Osmond-Johnson, P., Faubert, B., Zeichner, K., Hobbs-Johnson, A. with S. Brown, P. DaCosta, A. Hales, L. Kuehn, J. Sohn, & K. Steffensen (2016). The state of educators’ professional learning in Canada. Oxford, OH: Learning Forward. Canadian Child Care Foundation [CCCF], (2016). An Early Learning and Child Care Framework for Canada’s Children. Retrieved from: http://www.cccf-fcsge.ca/wp-content/uploads/CCCF_Framework-ENG.pdf Colmer, K., Waniganayake, M. & Field, L. (2014). Leading professional learning in early childhood centres: who are the educational leaders?, Australasian Journal of Early Childhood, 39(4), 103-113. Helterbran, V.R. & Fennimore, B.S. (2004). Early childhood professional development: Building from a base of teacher investigation. Early Childhood Education Journal, 31(4), 267-271. Lazarri, A., Picchio, M., & Musatti, T. (2013). Sustaining ECEC quality through continuing professional development: systemic approaches to practitioners’ professionalization in the Italian context. Early Years: An International Research Journal, 33(2), 133-145. Munton, T., Mooney, A., Moss, P., Petrie, P., Calrk, A., Woolner, J. et al., (2002). Research on ratios, group size, and staff qualifications and training in early years and childcare settings. London: University of London. Penn, H. (2009). Early childhood education and care: Key lessons from research for policy makers. Brussels: Nesse. Prochner, L., Cleghorn, A., Kirova, A., & Massing, C. (2016). Teacher education in diverse settings: Making space for intersecting worldviews. Rotterdam, The Netherlands: Sense Publishers. Thomas, S., & Packer, D. S. (2013). A Reflective Teaching Road Map for Pre-service and Novice Early Childhood Educators. International Journal of Early Childhood Special Education, 5(1), 1-14. Vandenbroeck, M., Peeters, J., Urban, M. & Lazzari, A. (2016). Introduction. In M. Vandenbroeck, M. Urban & J. Peeters (Eds.) Pathways to Professionalism in Early Childhood Education and Care, (pp. 1-14). London: Routledge. Vangrieken, K., Dochy, F., & Raes, E. (2016). Team learning in teacher teams: team entitativity as a bridge between teams-in-theory and teams-in-practice. European Journal Of Psychology Of Education - EJPE (Springer Science & Business Media B.V.), 31(3), 275-298. doi:10.1007/s10212-015-0279-0

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)0,016
score de la tête « metaresearch » (Gemma)0,048
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,731
Score d'incertitude au seuil0,901

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,048
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0040,003
Communication savante0,0200,013
Science ouverte0,0050,008
Intégrité de la recherche0,0160,013
Charge utile insuffisante (le modèle a refusé de juger)0,2690,138

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 Opus0,056
Tête enseignante GPT0,436
Écart entre enseignants0,381 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2017
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueJournal of Childhood StudiesMême sujetEducational and Psychological AssessmentsTravaux en français237 207