Modeling Teacher Nonverbal Immediacy: Refinement and Analysis
Bibliographic record
Abstract
This paper presents a refined computational approach for assessing teacher nonverbal immediacy (NVI) from classroom video recordings. Building on a previously published baseline model, we re-examined the original ten-feature representation to evaluate whether focused, theory-driven refinements could improve model performance without altering the preprocessing pipeline. The revised feature set includes distance variability, face visibility, and a merged negative affect channel, alongside existing measures of gesture intensity, perceived proximity, and facial expressions. Models were trained and validated on a dataset of 403 annotated 30-second video segments from German secondary school classrooms. We evaluated four classical regressors—linear regression, support vector regression, random forest, and extra trees—alongside the original multilayer perceptron (MLP). The refined MLP improved the correlation with human ratings from 0.44 to 0.49 and showed the most consistent prediction errors across teachers. Feature sensitivity analysis confirmed that the model’s predictions aligned with theoretical expectations: greater proximity, facial visibility, and expressive gestures were associated with higher immediacy. These results highlight the value of integrating behavioral theory into model design and support the use of automated tools for scalable, interpretable analysis of nonverbal communication in classroom settings.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".