Exploring Partnerships in Early Childhood Teacher Education through Scenario-based Learning
Bibliographic record
Abstract
Belonging to “a family, a cultural group, a neighbourhood and a wider community” (Department of Education,Employment and Workplace Relations [DEEWR], 2009, p. 7) is integral to children’s early development and learning.Acknowledging families as “children’s first and most influential educators” (DEEWR, 2009, p. 7), DEEWR notes that,“as children participate in everyday life, they develop interests and construct their own identities and understandings ofthe world” (Ibid). So, when children transition from the family context to participate in early education, establishingand maintaining partnerships with families and community members is essential to early childhood pedagogy. TheEarly Years Learning Framework acknowledges, “Belonging is central to being and becoming in that it shapes whochildren are and who they can become” (Ibid).While an important component of education, professional topics such as partnerships can be given less priority inuniversity subjects that focus on curriculum components. To “bridge perceived gaps between subject theory andprofessional practice” (Errington, 2010, p. 17) professional topics can be explored through scenario-based learning.This paper presents findings about the understanding and implementation of partnerships through scenario-basedlearning in a third year, online early childhood education subject, “Early Childhood Education and Care 2”. Theresearch question was, “How can scenario-based learning be implemented to increase students’ understanding andpractice of partnerships?”
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".