Teaching Cases in a Virtual Environment: When the Traditional Case Classroom is Problematic
Bibliographic record
Abstract
ABSTRACT The rise in interactive digital media has catapulted faculty‐student contact abilities from the traditional Web 1.0 model to a post‐Web 2.0 world where students and faculty can have much more interaction in classroom exchanges. Since business cases have long been a pedagogy of choice among professors concerned with training the next generation of decision makers, the intent of this exploratory teaching execution was to gain insight into the case teaching experience in Second Life (SL). SL is a virtual world developed by Linden Lab that enables its users to interact with each other through avatars (virtual representations of the self). While online classrooms in SL are not new (a number of universities throughout the world have set up virtual campuses), business education scholarship is lacking as related to the use of virtual worlds for educational purposes. The aim of the execution was not to suggest that SL case teaching should displace the traditional case classroom interactions. Rather, the exercise found that a case‐based class can be held and attended independent of time, distance, and location should the need arise. Case teaching in SL offers an availability alternative or supplement to the traditional case teaching and learning approach.
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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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".