Are STEM Games Intended To Be Educative?
Bibliographic record
Abstract
Our research aimed to expand perceptions of learning in school science and mathematics as relational and dynamic selves and experiences in the making. We grounded our work in Ellsworth’s (2005) notion of pedagogical pivots, thus recognizing STEM board games as texts that propel learning and learners forward through affective and aesthetic experiences, interaction as relationality, and boundaries as porous and fluid. To animate our theoretical framework, we held participatory review groups and reported on a group of eight pre-service teachers who played Santorini (mathematics) and Evolution: The Beginning (science). The results indicate that participants engaged in substantial moments of becoming across all three pedagogical pivots, which were made apparent in numerous ways through game play and interaction with the game, specifically exemplified through four emergent themes: 1) engrossment and presence both inside and outside of the game; 2) a becoming-play (about the process of play) and a becoming-game (bound by rules, security); 3) a becoming-community (collective learning, reciprocity, relationality); and 4) a becoming-self (as identifies are (re)forming). The data that emerged is a confluence of connections that produce a “becoming-with-ness” of the game itself and are described through participant statements, displayed through bodily reactions and interactions with space (relational or architectural), with the game itself, with other players, and which are shown through field notes, video and audio recordings of game-play observations. Data from the observed “in between” spaces—moments where learning or thinking might occur—allows for an identification of games as rich “texts” for mathematics and science 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".