Training Graduate Students’ Shaping Skills in an Immersive Virtual Reality Environment
Bibliographic record
Abstract
Graduate students need a wide range of professional skills, and shaping is one of the critical skills they must learn. This study trained graduate students to acquire shaping skills in an immersive virtual reality environment using the Portable Operant Research Teaching Lab (PORTL). To date, no known study has (a) evaluated the effectiveness of shaping skills training for graduate students or (b) attempted to teach these skills in a virtual environment. We used a single-case A-B design across participants with three graduate students who learned shaping skills in an immersive virtual reality environment using the PORTL curriculum. The shaping skills comprised creating a teaching plan, setting up for a session, delivering reinforcement, and evaluating a session. For all participants, training resulted in improvement in shaping skills. Participants also maintained the shaping skills for a minimum of two weeks. Further, the effect of the training generalized to a novel confederate learner for all participants. Additionally, participants showed high satisfaction with learning shaping skills in an immersive virtual reality (iVR) environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".