Board Games as Play-full Pedagogical Pivots for STEM Teaching and Learning
Bibliographic record
Abstract
Drawing inspiration from Ellsworth’s (2005) work on thinking with pedagogically nonprescriptive objects and the pedagogies they permit and prohibit, we turn our attention to similar educational “texts” increasingly used in STEM (i.e., science, technology, engineering, mathematics) education—board games. We tinker with board games as they refuse and resist the ways that STEM education often privileges cognitive destinations rather than relational learning journeys that enfold the whole learning self, the content, as well as the materiality of learning. We ask, how might games simultaneous act as locations of, and as, pedagogy that inflect experiences of student learning? To answer this question, we explore the pedagogical intents expressed by game designers themselves by their design diaries, blogs and interviews while thinking with Ellsworth’s concept of pedagogical pivot . In exploring game designers’ statements, we map out some of the potentialities that this pedagogical medium might offer STEM teaching and learning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".