Digital game‐based learning once removed: Teaching teachers
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
Abstract In the spring of 2005, the author designed and taught a graduate‐level course on digital game‐based learning primarily for teachers. Teachers cannot be expected to embrace digital games as a tool for learning unless they have a sound understanding of the potential as well as the limitations, and are confident in their ability to use games effectively to enhance learning. The course was designed as an introduction to digital games and gaming for instruction and learning. In it, students explored the theories, the possibilities, considerations and constraints related to the design of instructional games, and the use of learning and commercial entertainment games in classroom and out‐of‐class settings. The design of the course, along with the rationales, will be outlined and participant reaction will be profiled. Suggestions for future course designs are described, as well as key elements crucial for teacher preparation. Ultimately, the success of digital games as a medium for learning depends to a large extent on the abilities of new and practicing teachers to take full advantage of this medium.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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