Pre-Conference Workshop: Let's Play- Improving our Teaching in the Medium of Board Games
Bibliographic record
Abstract
This workshop paper focuses on exposing faculty to how the medium of board games provides an exceptional space for faculty development for our teaching and student learning. The “Let's Play” intervention [1] takes the form of a workshop where participants have opportunities to experience role reversal through being a learner again. Participants become active learners by playing board games that help them remember the experience of being a learner again. By choosing different types and styles of games, we can provide a space for the participants to discuss broader teaching practices, such as the importance of technical vocabulary, scaffolding ideas as we teach them, and the benefits of student-centered learning approaches. Another critical aspect of this intervention is that we hope to use role reversal to remind teachers how hard it is to learn in the hope that teachers will have more empathy for their learners. In this paper, we describe our workshop structure and pertaining literature and ideas on why board games are part of this medium. NOTE that many past participants are looking for how to use games in the classroom. This workshop does not address that aspect of the medium even though we have experience in this space [2].
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".