Applying the science of learning to teacher professional development and back again: Lessons from 3 country contexts
Bibliographic record
Abstract
BACKGROUND: Evidence from the science of learning suggests that playful learning pedagogical approaches exist along a spectrum and can support student learning. Leveraging active engagement, iterative, socially interactive, meaningful, and joyful interactions with content also supports student learning. Translating these concepts into guidance and support for teachers is lacking. METHOD: We introduce a tool designed to support teachers in implementing across the facilitation spectrum and leverage the characteristics that help children learn. Across three international contexts, we engaged with 1207 teachers and the tool was used 4911 times. RESULTS: Student age, the intended learning goal, and context influenced teachers' use of the tool, suggesting that contextualization is critical, even when basing programs on evidence-based, universal principles given by the science of learning. CONCLUSION: Science of learning research must be effectively translated but we must use evidence from teachers and real-life classrooms to inform those studying the science of learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".