Practicing What We Preach: Emergent Curriculum in Early Years Teacher Education Programs
Bibliographic record
Abstract
Traditional models of teacher education programmes of early literacy instruction encourage participants to plan, prepare, and present literacy lessons and integrated units of study for early grades. Literacy in the Early Years courses are often grounded in constructivist theory and designed to help pre-service teachers provide balanced literacy instruction for elementary grades. Even though many of these educational institutions are beginning to introduce new courses and programs that focus on problem-based learning, discovery learning, experiential learning, cooperative learning, service learning and inquiry-based learning (Justice, Rice, Roy, Hudspith, & Jenkins, 2009), there is little attention paid to emergent practice (Stacey, 2009) in early literacy pre-service education programs. The goal for this workshop is to “practice what we preach” by introducing concepts of play-based, emergent curriculum that model flexible, inquiry-based approaches to curriculum content. In Early Literacy courses, one module can be left to explore ideas, questions, interests, and theoretical concepts that the students identified.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".