A complexity approach to investigating the effectiveness of an intervention for lower grade teachers on teaching science
Bibliographic record
Abstract
This article describes the effectiveness and sustainability of teacher professional development interventions from a complexity view point as well as a more ‘standard’ viewpoint. The first aim of this study is to give a theoretical overview of effective aspects of interventions regarding teachers’ professionalizing using recent literature. The second aim is to re-interpret effectiveness and effectiveness studies using a complexity approach. The third aim is to empirically illustrate a complexity approach to the effectiveness of interventions using a multiple case studyWe have described intervention specific aspects, teacher specific aspects, context specific aspects and implementation specific aspects and have shown in the cases that during an intervention these aspects intertwine and act as a complex dynamic system.The complexity approach to interventions has implications for future empirical research as well as for the design of interventions. In future research, results of small scale and large scale research should be combined, in order to obtain a better insight in all relevant aspects and their effect on teachers’ behavior. Research ought to concentrate on the effects that all contextual aspects have intertwiningly in order to design more effective interventions and with better implementation processes.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".