Diving into Lake Devo: Modes of representation and means of interaction and reflection in online role-play
Bibliographic record
Abstract
This paper outlines an action research project involving the development of an educational online role-playing website, known as Lake Devo. Designed in keeping with constructivist principles, the website is used in select post-secondary courses at Ryerson University and allows learners to work synchronously, using visual, audio, and text elements to create avatars and interact in online role-play scenarios. The website also provides an integrated area for debrief following role-play activities. The features of the website were deliberately intended to provide a viable alternative to text-only online role-play activities, while not requiring the highly sophisticated elements of 3D virtual environments. During the period of the project on which this article reports, learners were invited to use the Lake Devo website for an assigned role-play activity. Online learner survey responses were collected following the pilot implementations of the website to determine the extent to which the non-text modes of representation (visual, audio) in Lake Devo, along with an integrated debrief area on the site, supported the learners in their online role-play activity. The preliminary findings suggest that Lake Devo provides an environment that effectively supports online role-play. The simple format of the Lake Devo avatars, the availability of visual and audio elements, and the ability to create a lasting artifact for review in a dedicated debrief area engage students and also reinforce the constructivist and collaborative nature of role-play activities. For practitioners beyond the Lake Devo project team and the Ryerson context, the Lake Devo website provides an example of an online role-play environment that offers alternatives to text-based and/or 3D virtual worlds.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".