Bibliographic record
Abstract
Video games play an important role in education, having a strong influence on the Net Generation; however, the idea of teachers as designers of digital classroom games to support student learning has not been widely embraced. The purpose of this study was to gain a deeper understanding of internal and external factors that influence teachers’ capacity to teach and inspire them to move from game users to game designers. This mixed-method case study involved a group of teachers who used and/or designed games for students. The four unique case groups were grounded in three cities and four school districts in Alberta, Canada. Qualitative data were collected from five teachers and six school administrators, six student focus group interviews, eight in-class observations, and two teacher-designed games. Quantitative data were collected from one online survey completed by all five participating teachers. First and second cycle data coding and analyses (Saldaña, 2013) were used to answer the following four research questions: 1) What are the key factors that influence teachers in using digital game-based learning environments? 2) What are the key factors that influence teachers in designing digital game-based learning environments)? 3) What are the conditions needed to develop teachers’ capacity to be designers of digital games? 4) What factors influence the transition of teachers from being game users to game designers to support student learning. iii Key findings from the analysis showed that 1) teachers’ passion towards Digital Gamebased Learning (DGBL) played an important role in motivating them not only to use games but design games, and 2) their technical and pedagogical knowledge worked as a foundation to help teachers transition from game users to game designers. The implications of this study are: 1) my research showcased potential opportunities for both pre-service and in-service teachers regarding designing games in the classroom; 2) school administrators my reference my study to provide resources to support teachers’ innovative teaching approaches and encourage them to be risk-takers; and 3) my research offers options for professional developers to develop courses on game design in order to prepare teachers to use/design games in pedagogically sound ways.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".