Designing Voice Reflection for Students
Bibliographic record
Abstract
Research has revealed the positive effects of reflection on helping students manage their psychological well-being. Hence, we are motivated to investigate the design space of how we can communicate the values of doing reflections. We limit our work to voice reflection because of its inclusivity and effectiveness. We designed a pilot survey on Qualtrics to collect qualitative responses and integrated voice recording features from Phonic.ai. Participants were presented with 4 sample voice recordings related to college students' daily lives and asked to complete a simple voice reflection activity based on the samples they listened to. Then, they were asked to provide feedback on these examples. We deployed the survey on Amazon Mechanical Turk (MTurk) and collected 221 effective responses. By conducting thematic analysis, we found several insightful themes: emotional speech, diverse content, and clear structure are important elements to include, while examples should avoid being overly scripted. The findings suggest ways to design effective examples to engage students in voice reflections and open up the possibilities for further investigations into the design features of voice reflection platforms.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".