Elementary School Teachers’ Self-Assessment of Use of Positive Behavior Support Strategies and Goal Setting Related to Equity-Focused Features
Bibliographic record
Abstract
The goal of the Maximize Program is to collaborate with educators to develop resources and procedures to facilitate teachers’ use of equity-focused behavioral supports. In this study, we describe teachers’ responses to the first iteration of the interactive Maximize Technology Platform. Ninety elementary school teachers from three schools were encouraged to use the platform to learn about the foundational concept of equity literacy, complete a self-assessment of practices, and set a goal for improvement. We observed teachers’ platform use, self-reported use of 10 behavior support strategies, goals set for improving equity-focused features of these strategies, and reported progress during the first quarter of the academic year. Over 70% of teachers reported frequent use of four strategies: Classroom Expectations, Praise, Greetings, and Community Circles. Fewer teachers reported using Student Choice, Effective Questioning, and Corrective Feedback. Variations in use between general education and other teachers were observed. Over 60% of teachers set an equity-focused goal. Variability in the types of goals set and rates of reported improvement highlight the complexity of this work. Results offer promise about the use of interactive technology to facilitate professional learning and goal-setting about equity initiatives and offer insights for leveraging interactive technology to facilitate teachers’ implementation of equity-focused practices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".