Pedagogical Foundation to Promote Students’ Engagement and Creativity While Co-creating a Music Learning Game
Bibliographic record
Abstract
Learning-game co-creation is a pedagogical activity where learners draw on their knowledge of a specific topic to collaboratively create a learning game that peers can later use to gain a new understanding of a topic (Kafai & Burke, 2015; Kangas, 2010). Indeed, this approach offers important learning opportunities, as students call upon their acquired knowledge to create the different components of their game (e.g., rules, objective, dynamics, elements), all while also calling upon their creativity and encouraging their engagement with the activity. Studies exploring game creation as a learning activity have allowed us to identify and understand the common phases of this process. However, it is less clear to determine which are pedagogical principles the teacher should consider when implementing this approach into their own “teaching reality.” In this paper, we present a pedagogical experience based on co-creating a music learning game with seven young musicians (age: 10–14). More specifically, our paper presents the educational, operational, and conceptual models that enabled us to establish a robust pedagogical foundation upon which we built this learning activity that, to our knowledge, had never been explored in our field (music education). Therefore, we will explain how we used each model to a) structure the set of activities that enabled the participants to create their own music learning game, b) guide the researcher’s pedagogy act to co-create a music learning game with the students, and c) understand the participants’ creative response towards this learning activity.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".