Notice bibliographique
Résumé
Contemporary pedagogies for early years and kindergarten education, such as play-based learning, promote academic and developmental growth through developmentally appropriate practices (Fromberg & Bergen, 2006; Goldstein, 2007; Pyle et al., 2017). Despite the growing endorsement for play-based education in policy documents (Ontario Ministry of Education, 2016), research on assessment within the pedagogy is less abundant (Pyle & DeLuca, 2017), leaving educators without practical strategies for integrating assessment into a play-based program. Further research is needed to support play-based educators in navigating rising academic and assessment standards in early education (Graue et al., 2017) while implementing modern assessment perspectives, such as capturing student learning through child-centered, developmentally appropriate, and authentic means (Gullo & Graue, 2020; Pyle et al., 2020). When leveraged effectively, visual data (e.g. information captured through photos/videos) is endorsed as an optimal method for integrative play-based assessment (Dahlberg, 2012; Ontario Ministry of Education, 2012, 2015; Pyle et al., 2020). However, studies show that although play-based kindergarten educators collect large quantities of visual documentation, they lack a sufficient framework to translate this documentation into assessment (Pyle et al., 2020). This study used qualitative inquiry and consisted of four sequential stages. First, a secondary data analysis of previously conducted semi-structured interviews explored Canadian kindergarten educators’ experiences integrating assessment into play-based education and identified themes to inform survey development. Second, nationwide survey data collected further insights into kindergarten educators’ assessment experiences, including the advantages and challenges of leveraging visual data for classroom assessment. Third, a reflexive thematic analysis revealed overarching themes and conceptualized the findings to inform the subsequent resource development. Lastly, a proof-of-concept web application was constructed to reflect the participant’s experiences and address the benefits and barriers identified in earlier stages. Findings revealed that educators experienced benefits when leveraging visual assessment data to inform interpretations (offering educators’ cognitive support and enhancing reflexivity), metacognition (supporting developmentally aligned student self and peer reflection), and communication (enriching home-to-school partnership and collaborative assessment practices). The study also revealed that educators faced barriers to consistently accessing the benefits of visual data due to the unfeasible amount of time required to do so. As an underlying contributor, structural issues in the technology used by educators resulted in insufficient assessment and organization features, ultimately requiring them to use numerous platforms. These challenges were exacerbated as educators faced institutional barriers where collaborative assessment practices were not supported. Subsequently, a proof-of-concept web application was created to translate the findings into practical app features reflective of best practices and responsive to the barriers that educators face.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,011 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».