A field-initiated vision of research infrastructure for STEM education
Bibliographic record
Abstract
STEM education research has historically been under-equipped, relying on standardized tests and questionnaires while other fields deploy space telescopes and particle accelerators. What would happen if we designed research infrastructure for STEM education to address our priority needs? The INTERACT Incubator is a research coordination network whose goal was to develop a field-initiated vision of novel infrastructure that would enable aspirational research that could advance equity in STEM education. The incubator brought together a diverse cohort of experimental social psychologists who study social cues in STEM, experimental cognitive psychologists who study learning in authentic classroom settings, as well as education stakeholders and technologists with expertise in digital infrastructure for education. In Phase 1 we conducted a needs assessment, where we brainstormed aspirational research studies and identified three core infrastructure requirements: coordinated data collection and measurement systems, sustainable large-scale research–practice partnership frameworks, and knowledge repositories combined with professional learning networks. In Phase 2 we designed an integrated solution to address these needs. The INTERACT Incubator’s solution differs from existing research infrastructure because ours was systematically designed to address research needs identified by the field itself, rather than building research services on top of existing research capacities or operational systems. This commentary documents a consensus vision for novel infrastructure that would enable the research needed to achieve meaningful progress in STEM education.
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 | 0.163 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".