Development of Curriculum Guides for the Assassin's Creed Discovery Tour Games to Enhance Teachers' Adoption of Games for Learning
Bibliographic record
Abstract
While games are often used for learning, educators hesitate to adopt them into classrooms due to a lack of acceptance and knowledge of how to teach with games (e.g., Callaghan et al., 2017). In this project, we created curriculum guides for two educational games, Assassin's Creed Discovery Tour: Ancient Greece and Egypt, to a) identify methods and theories appropriate for creating teacher guides for game-based learning; and b) test if such guides can facilitate teachers' adoption of games. The theories used to develop the guides included the Technological, Pedagogical, Content Knowledge framework (TPACK, Mishra & Koehler, 2006), the Technology Acceptance Model (TAM, Davis, 1989), and the Learning Mechanics-Game Mechanics framework (Arnab et al., 2015). The guide is an interactive website containing four sections: 1) Curriculum Section, learning goals are selected from a list and then game and classroom activities are suggested; 2) Game-Activity Section, in-game activities are selected and then links are drawn to learning outcomes; 3) Lesson Plans tailored to different subjects, ages, and instruction modalities (student vs teacher led); and 4) Technical FAQ, addressing common technical and practical barriers. To test whether the curriculum guides improve adoption, a post-test only between-subjects experiment are being conducted with in-service teachers (n=120) to see if exposure to the guide increases their TPACK and TAM. Follow-up focus groups will be conducted to provide in-depth interpretations of teachers' feedback. This project presents a methodological model illustrating the development of curriculum guides that will support teachers' implementation of video games for learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".