The Virtual Avatar Lab (VAL): Tapping into Virtual Live Environments to Practice Classroom Feedback Conversations
Bibliographic record
Abstract
Providing effective feedback is a skill preservice teachers develop through practice. According to Hattie (2012), feedback is essential in the learning process, is prevalent in effective teaching, and its purpose is to help students determine current level of performance, so adjustments can be made to enhance performance to desired level. This qualitative case study was developed to provide 16, K-12 preservice teacher candidates with an opportunity to practice providing feedback in a virtual live environment. Candidates participated in (e.g. interacted with avatar, observed interaction, and/or critiqued interaction) a 60-minute simulation to practice feedback, then completed oral reflection and a written reflective survey (questionnaire) consisting of rating scales and corresponding written response items. Qualitative data collected was coded and analyzed for themes, while quantitative data explored central tendencies, and variations for each survey indicator. Results indicate live simulated sessions in the Virtual Avatar Lab (VAL) were beneficial in developing feedback skills. Teacher candidates reported favorable perceptions with respect to preparation to facilitate feedback conversations, in most cases felt the student avatars were authentic, and felt better prepared to help their future students take ownership of their own areas of strength and room for growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".