Silence is Not Golden: Reducing Communication Apprehension in the University Classroom
Bibliographic record
Abstract
Learning research suggests that students are more motivated, learn better, become better critical thinkers, and have self-reported gains in character when answering questions, contributing to class discussions, or presenting to the class (see Rocca, 2010 for a review). However, Howard and Henry (1998) reported that 90% of course activities that involve classroom communication are made by only a handful of students. One reason for this is what researchers have termed communication apprehension (McCroskey, 1977), also referred to as participation anxiety (Karim & Shah, 2012). Many sources offer tips to help students manage their own anxiety (e.g., Young, 1990), however, few sources actually address tools instructors can use to create an environment that reduces the fear of participating. In this session, participants explore the underlying causes of communication apprehension/participation anxiety and strategies that can be implemented to create a low-anxiety classroom environment. The primary goal is to encourage participants to increase participation in their classrooms by changing the classroom from an atmosphere of insecurity and anxiety to one that enhances the natural communication strengths of students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".