Raising business communication students’ awareness of nonverbal features of interaction
Bibliographic record
Abstract
Analysis of conversations between international university students in the Corpus of English as a Lingua Franca Interaction (CELFI, McDonough & Trofimovich, 2019 McDonough, K., & Trofimovich, P. (2019). Corpus of English as a lingua franca interaction. Concordia University. [Google Scholar]) has demonstrated that holds, which are temporary cessations of dynamic movement, are a robust visual cue of nonunderstanding that can be reliably interpreted by external observers as signals of listener comprehension difficulties (e.g. McDonough et al., 2019 McDonough, K., Trofimovich, P., Lu, L., & Abashidze, D. (2019). The occurrence and perception of listener visual cues during nonunderstanding episodes. Studies in Second Language Acquisition, 41(5), 1151–1165. https://doi.org/10.1017/S0272263119000238[Crossref] , [Google Scholar], 2022 McDonough, K., Lindberg, R., & Trofimovich, P. (2022). Examining rater perception of holds as a visual cue of nonunderstanding. Studies in Second Language Acquisition, 44(5), 1240–1259. https://doi.org/10.1017/S0272263122000018[Crossref] , [Google Scholar], 2023 McDonough, K., Lindberg, R., Trofimovich, P., & Tekin, O. (2023). The visual component of non-understanding: A systematic replication of McDonough, Trofimovich, Lu, & Abashidze (2019). Language Teaching, 56(1), 113–127. https://doi.org/10.1017/S0261444821000197[Crossref] , [Google Scholar]). Using CELFI materials, this study used an experimental design to explore whether business communication students (N = 64) benefit from instructional activities designed to raise their awareness of holds as a signal of nonunderstanding. The students carried out perception tests in Week 1 and Week 5 that presented video excerpts from CELFI showing the depicted listeners’ hold onsets and releases, and the students rated those listeners’ comprehension. In the interim, 31 students completed weekly awareness-raising activities via Moodle (2 hours per week) for four weeks. A mixed ANOVA showed that students who participated in the awareness-raising activities showed significant improvement in their ability to discriminate between hold onsets and releases. Implications for the use of awareness-raising activities to promote recognition of nonverbal behavior are discussed.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".