Self-directed Professional Development for Teachers: Does it Increase Agency and Engagement?
Bibliographic record
Abstract
This case study changed the ‘rules’ surrounding professional development (PD) during the course of one school year to allow teachers to have more control over their professional learning experiences. The study evaluates indicators of teacher agency and engagement with respect to their learning, and looks for evidence of ‘transformative’ and/or ‘expansive’ learning. This study used pre and post-intervention surveys and interviews, and ‘participant-as-observer’ observation. Survey and interview codes were thematically organized into parameters indicating degrees of agency and engagement in PD activities and expansive/transformative learning. Pre- and post-intervention data was compared to determine if there were changes in these indicators. Almost all of teachers who participated in self-directed professional development (SDPD) did express more indications of engagement and agency. Several SDPD teachers expressed experiencing learning that was expansive and/or transformative, whereas there were no expressions of this type of learning pre-intervention. Several teachers saw SDPD as requiring more effort and organization, and there were concerns about accountability. This study and the findings that SDPD does increase teacher agency and engagement may encourage educators and educational leaders to see benefits in releasing control of PD to practitioners who are able and willing to meet their own learning needs.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".