Teacher-Led Learning Circles for Formative Assessment:Full Report of International Research Findings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Her primary research focus is on improving professional learning outcomes for educators and students in K-12 education.She particularly emphasizes how educators utilize various forms of assessment and program data to enhance their professional learning and practice.Additionally, she has co-developed and continues to research the Approaches to Classroom Assessment Inventory (ACAI), a professional learning tool that assists teachers in understanding and refining their approach to assessment.Over the past decade, Dr. LaPointe-McEwan has led numerous education-based evaluation and research projects, collaborating with school districts, education networks, Ministries of Education, and educational organizations to improve outcomes for educators and students.Recently, she initiated a stream of research focusing on online learning, specifically exploring assessment approaches and innovations in online higher education and professional learning courses.In all her work, Dr. LaPointe-McEwan
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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 | 0.022 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it