Weaving Family Engagement Practices into Preservice Teacher Education: Supporting Future Educators in Partnering with Families from the Start
Bibliographic record
Abstract
This reflection of my practice arises from my background as an educator working with children and their families for over thirty years, as well as the professional learning I have engaged with throughout my career. In this reflective practice article, I focus on how family engagement knowledge and practices can be integrated into preservice teacher education courses by including outcomes addressing family engagement and providing immersive experiences with children and families in course content and assignments. Preservice teachers state that working with children and their families in these courses has significantly enhanced their learning and positively influenced their teaching philosophy and future practice. I use Schon’s conceptualization of reflective practice, the ability to reflect on one’s actions to facilitate continuous learning and improvement, in my own work as a practitioner. In this article, I demonstrate praxis and share specific examples from my practice to invite readers to use and adapt them for their own use and context.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".