Unpacking the potential of educational gaming: A new tool for gaming research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article begins by reviewing the theoretical bases for the contention that advanced computer-based educational gaming can provide powerful learning experiences, and overviews the limited research on the use of such games. Although studies to date have generally supported their value, most of the published investigations have methodological limitations. Critical process data are typically not collected, and unreliable student and teacher self-reports are heavily relied on in evaluating the educational efficacy of many games. To address these and other limitations, the authors have developed research software that can remotely and unobtrusively record screen activity during game play in classroom settings together with synchronized audio of player discussion. A field trial of this data collection system in which 42 college students were studied as they played a coursework-related Web-based learning game is described, and the article discusses how the trial outcomes concretely demonstrate the methodological advantages the tool offers researchers.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it