Student-Generated Interview Podcasts: An Assignment Template
Bibliographic record
Abstract
Podcast assignments in higher education foster students’ deep engagement in course content, knowledge construction, technical skills, and problem-solving abilities. Persuaded by the success of previous podcast case studies, we designed a podcast assignment in a First Year Seminar course wherein students created a ten-minute podcast based on an interview that compared theoretical concepts to the lived experience of the interviewee. Students were guided through four distinct stages of the assignment: (1) knowledge and skill preparation; (2) organizing and conducting the interview; (3) interview synthesis and post-production; and (4) peer review and class reflection. The results of a student survey indicate that the podcast assignment design and format supported students’ achievement of learning outcomes and that students valued the podcast assignment. From the instructors’ perspective, the podcast assignment allowed students to achieve learning outcomes, improve oral communication skills, and engage with course content in a deep and authentic way. Finally, we provide an assignment template with timelines for instructors considering implementing a student-generated podcast into their course and suggestions for its implementation.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".