Year Zero Economics - Using Edu-Larping to Explore Economic Systems in the Ninth Grade
Bibliographic record
Abstract
The curriculum of the province of Alberta, Canada, stipulates that grade nine students learn about economics, comparing the United States and Canada (Alberta Education 2007). Supervisor Ken Koziej assigned Mikael Hellström to teach this unit to three grade nine classes during his practicum. Hellström had pioneered the use of edu-larps, game-based learning, and gamification as a sessional instructor at the Universities of Alberta and Calgary (Hellström 2016; 2017) and chose those methods to deliver the unit. The game design for the tabletop role-playing game Mutant: Year Zero (The Free League 2015) incorporates mechanics for play on three levels: character, for world exploration; resource extraction and world exploration; and base building. The latter two levels effectively create an in-game economy. Collaborating with his supervisor, Hellström adapted them for classroom use. The goal was to create a unit fulfilling Mochocki’s (2014) criteria for edu-larp, in other words, that it is a) mono-disciplinary and targets a single school subject; b) knowledge-oriented and communicates textbook subject matter to students; and c) teacher-friendly, by not demanding time-consuming preparations. This paper describes the unit, the process of converting elements of Mutant: Year Zero for teaching, how students played the unit, and the post-game evaluation. While the unit did not fulfill all of Mochocki’s criteria, student engagement was high, consistent with previous findings on game-based learning (Prensky 2005; Gee 2007; Hattie 2009).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".