Endowing a Game-Based Learning Hub for Augmenting Teaching and Learning: Design, Constellations and Perceptions from a Teachers Perspective
Bibliographic record
Abstract
Game-based learning is viewed as an immersive and pedagogically rich approach to enhancing teaching and learning in schools. However, teachers may feel overwhelmed from the dispersed, disorganised and invalidated plethora of game-based resources circulated over the Web that needs to be collected, reviewed and repurposed for designing and orchestrating game-based learning. This paper presents the design requirements of a game-based learning platform that may help teachers to find, retrieve, re-use and share gamebased learning along with opportunities of augmenting teachers’ creative potential and professional development. The paper also contemplates on qualitative findings of a small-scale study (n=18) on teachers’ different perceptions of game-based learning and constellations of employing a digital platform for increasing awareness and practice in the classroom. An empirically-based framework is developed that maps perceptions to actual practice. The findings may contribute to developing discourse on processes, practices and strategies that teachers would employ, which in turn would inform the design of GBL systems dedicated to support teachers in their effort to use game-based learning most relevant to them.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".