Fullness of life as minimal unit: Science, technology, engineering, and mathematics (STEM) learning across the life span
Bibliographic record
Abstract
Abstract Challenged by a National Science Foundation–funded conference, 2020 Vision: The Next Generation of STEM Learning Research, in which participants were asked to recognize science, technology, engineering, and mathematics (STEM) learning as lifelong, life‐wide, and life‐deep, we draw upon 20 years of research across the lifespan to propose a new way of thinking about and investigating the topic. We proposeFullness of Life(orTotal Life) as the minimal unit of analysis that allows people generally and researchers specifically to make sense of cognition. This move reverses traditional perspectives: Rather than understanding life from the position of STEM activities, we understand STEM learning from the perspective of life taken as a whole. We propose three attendant concepts that do not focus on stable knowledge content but on (a) the ability to mobilize and augment knowledge (knowledgeability), (b) the necessity to develop the disposition of thedébrouillard/eand bricoleur, and (c) the necessity to conceive knowledgeabilityascollectiveproperty, outcome ofcollectivepraxis. We conclude by commenting on five dimensions suggested as need requirements for implementing a 2020 vision for STEM learning research. © 2010 Wiley Periodicals, Inc.Sci Ed94:1027–1048, 2010
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".