Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".