Pre-Service Teachers Designing and Constructing "Good Digital Games".
Bibliographic record
Abstract
There is a growing interest in the application of digital games to enhance learning across many educational levels. This paper investigates pre-service teachers’ ability to operationalize the learning principles that are considered part of a good digital game (Gee, 2007) by designing digital games in Scratch. Forty pre-service teachers, enrolled in an optional educational technology course, designed and constructed their own digital games in an authentic learning context. The course was structured to prepare pre-service teachers to use game design and construction in their future pedagogical practice. These pre-service teachers had various levels of game-playing experience, but little-to-no previous game-design/building experience. To evaluate the digital games, we created the Game Design Assessment Survey, which determined the degree to which a core set of learning principles, identified from the literature, were present in the digital games constructed by the pre-service teachers. Results suggested that pre-service teachers were generally unaware of the learning principles that should be included in the design of a good digital game, but were familiar with quality principles of interface usability. In addition, no relationship was found between the amount of time pre-service teachers played digital games and their ability to design and construct a good game.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".