Changing the Volume of Online Lectures: Does Audio Level Affect Learning Quality?
Bibliographic record
Abstract
Due to the onset of the Covid-19 pandemic, students have increasingly begun to learn virtually, often watching online lectures. Some students might believe that turning up the volume on those lectures can enhance their learning efficiency, and this assumption is supported by a 2009 study (Rhodes & Castel, 2009) where students were shown to have higher judgments of learning (JOLs) for louder words compared to quiet words. I designed a within-participants experiment under two conditions to study the real effect of louder audio on learning by making the participants watch videos at different volumes, answer several questions regarding their JOLs, and complete one quiz for each video. The final data suggest that louder audio had significant influence neither on students’ JOLs nor on their assessment performance. Although the online participants’ assessment performance presented a similar result as that in the Rhodes and Castel study, the finding of no influence of volume on JOLs contradicts Rhodes and Castel.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".