Facilitating Community-Based Field Experiences: Experiences From Children's Museums, a Museum School, and a Construction Site
Bibliographic record
Abstract
This paper will present the findings of a qualitative collective case study examining the experiences of four teacher educators who facilitate community-based field experiences for preservice teachers.Community-based field experiences are conceptualized as those occurring in community settings but outside the formal PK-12 educational system.The participants, three from the United States and one from Canada, were all responsible for the development and facilitation of the community-based experiences for their students.Through collaborative conversations spanning more than a year, two research questions were considered.In what ways do community-based field experiences enhance the work of teacher educators?In what ways do community-based field experiences challenge teacher educators' work?Both enhancements and challenges to the work of teacher educators are identified. ObjectiveWe know that early, diverse, and sustained field experiences are one element of a successful teacher education program (Darling-Hammond, 2006;Zeichner, 2010).However, contexts and experiences vary widely (Forzani, 2014; NCATE, 2010), which presents challenges for teacher educators as they decide how to best prepare preservice teachers using clinical, practice-based field experiences.Drawing upon the theory of practice-based teacher education and research associated with learning to teach in community settings, we conducted a collective case study of four community-based field experiences facilitated by separate teacher educators.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".