Partnering with Educators to Iteratively Co-create Tools to Support Teachers’ Use of Equity-Focused Positive Behavioral Supports
Bibliographic record
Abstract
Abstract In the Maximize Project, we are engaging in a research-practice partnership to co-create implementation strategies to facilitate elementary school teachers’ use of equity-focused positive behavior supports (EF-PBS). In this paper, we describe the processes used to build an interactive, technology-based platform to enhance teachers’ use of EF-PBS via self-reflection, self-assessment, goal setting, and goal review. We describe how we established a multi-disciplinary, multi-state community advisory board to collaborate on Version 1 of platform (Phase 1). We explain how we obtained quantitative and qualitative feedback about the platform from educators in three partnering schools, and how we used those data to produce Version 2 of the platform (Phase 2). Platform use data suggested high utilization in Quarter 1 (August–October) of the school year, when there was protected time to complete activities. However, platform use was moderate in Quarter 2 (October–December) and low in Quarters 3 and 4 (January–May). Educator feedback revealed moderate acceptability, feasibility, and appropriateness of the platform and highlighted ways to improve the user experience (e.g., streamlining steps in goal setting, making resources about strategy implementation easier to find). We discuss lessons learned to inform school mental health co-creation endeavors, including strategies for supporting diverse perspectives, for enhancing advisory board members’ voices and confidence, and for creating practical and feasible methods for teachers to benefit from co-created technology-based implementation strategies. Our processes offer guidance for others engaging in research-practice partnerships, developing education technologies and/or supporting teachers’ use of equity-focused practices to improve daily school experiences for all students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".