Bibliographic record
Abstract
Videogames are a popular medium in our society and have an enormous mass appeal, reaching audiences that number in the millions. Even though games are mostly viewed as leisurely pastimes, they can incorporate many effective pedagogical practices and have an enormous potential to deliver STEM education to millions of users simultaneously (Mayo, 2009). Unlike other media, games are highly interactive and have many attributes that could be adapted as pedagogical tools (Annetta, 2008). Given their popularity, many educators have made attempts to incorporate gaming in their classes to support student learning and engagements (Pennington et al, 2014; Bowling et al, 2013; Chuck, 2011; Takemura and Kurabayashi, 2014). This is particularly true in STEM fields, where playing games as education tools has led to significant improvements in test results, student motivation, and knowledge retention (Boeker et al, 2013; Sadler et al, 2013). This workshop aims to familiarize participants with the basis of gaming as used in scientific education and to guide them on the path of designing and implementing a game in their own teaching. Through the course of the workshop, the participants are introduced to several games and are encouraged to think of ways to incorporate these, or similar games, into their own curriculum.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".