Playing Political Science - Leveraging Game Design in the Post-Secondary Classroom
Bibliographic record
Abstract
The Multiplayer Classroom describes how a course in computer game design can be based on the same structure as a computer game (Sheldon 2012). Students play this game through the entire term. Sheldon also had students take on roles based on Bartle’s taxonomy of player types (Bartle 1996), leveraging it to structure group work and accommodating different learning types. During the Winter term of 2015, I taught two courses in Political Science at the University of Calgary: Topics in Comparative Politics in the Industrialized World and Introduction to Public Administration. Having previously leveraged gamification principles in teaching extensively (Hellström 2015), operationalizing Sheldon’s design was a logical next step. This paper describes that effort, including challenges and opportunities for how Sheldon’s design can be used. The design requires a complete change in the point of departure for the course, from the implementation of Bartle’s Taxonomy, to how the curriculum is presented to the students through potentially asynchronous game events rather than through the linear structure of the classic lecture series. These techniques will be familiar to those who are acquainted with computer games or live action role-playing (larp). The paper will also include some reflections on potential for future research in terms of how game-based learning could enhance the post-secondary political science classroom.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".