Identity Play in Gameful Learning: Avatars as Multiplayers in a Graduate Course
Bibliographic record
Abstract
Considering how games engage players in goal-driven pursuits, educators and researchers are paying attention to the learning and design principles of games for their efforts to support meaningful learning experiences in classrooms. We argue that educators’ experience in gameful learning is important in order for them to understand the gaming context of young people. We propose that as educators engage in identity play through gameful learning, they are able to discover their own potentials and work towards a constructionist ethos of creating both artifacts and selves. In this paper, we discuss how we designed a graduate course on digital game-based learning to engage participants who are educators in its concepts and practices. The design of the course was a progressive development for three years, which included gaining experience points (XP) mediated by a social media technology, Google+. The design was modified with subsequent iterations to implement the use of avatars and self-assessment of XP. The social media worked as a possibility space for the participants to explore, embody and implement new identities especially from the second iteration, which introduced the avatars. In this paper, we discuss the identity play across three iterations from three different perspectives: as a course facilitator, a student and researchers. Our findings, which are primarily based on learner-generated content on Google+, demonstrate the participants’ emergent play with identities in their effort towards being gameful.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".