<i>Good luck have fun</i>: The need for video game pedagogy in teacher education
Bibliographic record
Abstract
Abstract In education, the shift to emergency remote teaching found teachers working to increase student engagement in the online environment while still relying on face‐to‐face pedagogical approaches in the absence of sufficient Professional Development opportunities (DeCoito & Estaiteyeh, 2022). In response to the growing interest in video games in education, this article reconsiders the data collected for a single case of primary/junior preservice teachers (PTs) enrolled in a science education methods classroom to answer (a) How can video games be used as a learning object in a teacher education program? (b) How does using a video game in a science education class impact PTs' intent and understanding of using video games in their future classroom? (c) How PTs can be supported to understand how video games can be used? Results found video games acted as significant springboards for learning as PTs worked together to make meaning of STEM and reflected—both during and after gameplay—on video game use with their future students. Additionally, exposure to digital game‐based learning increased both intent and confidence of using video games as deep learning objects for their future classrooms. Recommendations and implications are discussed regarding the introduction and integration of video games in a teacher education program.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".