New Tools, New Tricks? Evaluating Games and Simulations from Multiple Perspectives
Bibliographic record
Abstract
This paper focuses on new tools related to the formative and summative evaluation of online simulations and games for learning, in the context of the Canadian “Simulations and Advanced Gaming Environments (SAGE) for Learning network and its research. The SAGE project is a $3 million, bilingual initiative in which over 30 Canadian university-based researchers from 14 institutions are collaborating with partner representatives to better understand how SAGEs can support learning, particularly through application of current learning theories. In addition to traditional learning assessment methodologies, SAGE is applying a number of new tools and techniques to game and simulation evaluation; this paper focuses on (a) systematic reviews of the literature, as a means of determining the variables related to positive learning outcomes; (b) transcript analysis, as a means of determining types of thinking taking place in problem-based learning simulation sessions; and (c) the Virtual Usability Lab (VULab), a prototype tool for remotely and automatically capturing process and player response data without researcher intervention. We describe these tools and examples of their application as a starting point for discussion and further work on game and simulation evaluation.
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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 | 0.070 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".