Woes of an RCT for Game-Based Learning Research - Past Problems and Potential Solutions
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are the gold standard for scientific research but pose many obstacles for game-based learning (GBL) studies. This poster is intended to foster a discussion about RCTs by summarizing two recent trials performed by the authors and comparing the distinctly different protocols including length of stimulus exposure, levels of internal and external validity, and sample size considerations. Finally, a proposed protocol for a third RCT is presented that attempts to reconcile the pros and cons from the previously described studies, while maintaining rigour in the research - the authors seek feedback from conference attendees as to the protocol's viability.
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 | 0.743 | 0.855 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.036 | 0.042 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".