Set super-chicken to 3! Student and teacher perceptions of Spaceteam ESL
Bibliographic record
Abstract
Digital gaming in education is an area that has been rapidly expanding in popularity and is gradually being applied to second language (L2) contexts (Godwin-Jones, 2014). Mobile gaming in particular offers the benefits of digital gaming while also offering the portability and accessibility of mobile devices (Ogata & Yana, 2003; Stockwell, 2010). This pilot study examines student and teacher perceptions of a mobile team-building game entitled Spaceteam ESL. Although not created as an educational game, Spaceteam ESL allows students to interact in the target L2 (English) while providing a comfortable and enjoyable environment to practice the language. We hypothesize that its regular use may contribute to the development of oral fluency in the target language, as it engages learners in an activity that encourages them to reuse the language that they already know in an automatized (fast) but comprehensible manner. In general, our analyses indicate that users and their instructor perceive Spaceteam ESL positively, as a fun and effective way to practice English.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".