Student-Generated Interview Podcasts: An Assignment Template
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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 it