The Impact of Emotional Prosody on Online Learning in Undergraduates
Bibliographic record
Abstract
ABSTRACT As online learning continues to become prevalent in universities, interest grows in how it may impact learning. Emotional prosody (EP), the utilization of acoustic cues embedded in utterances that convey emotion, may be important for online learning. Focusing on the role of EP for online learning, this study examined how EP impacted the learning performance of 80 undergraduates. Participants watched a 5‐min‐long video lecture on a novel topic wherein the teacher used positive or neutral EP, delivered using video and audio of the teacher, or audio only. Later quiz performance showed that the utilization of positive EP resulted in significantly better learning performance than neutral EP. Additionally, the best learning performance occurred when the lecture video utilized positive EP in combination with an audio‐only delivery. These findings suggest that online educators should be mindful of the impact the presence or absence of EP may have on learner performance.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".