Listen Up! Using Podcasts in STEM Courses to Improve Engagement and Facilitate Review
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
This workshop focuses on how to integrate podcasts into science-based courses (e.g., chemistry, psychology). To some students, science-based courses can be perceived as ‘dry’ and difficult to engage with at a level that facilitates retention. Given that engrossing, high-quality teaching is cited as inspiring course enjoyment and leading students to further pursue STEM education (e.g., Horowitz, 2009), lecturers are often looking for ways to increase student interest. More than this, it is the hope of many educators that more enjoyable coursework will lead to better retention and understanding of the material (e.g., Kuh et al., 2008). As a news and entertainment vehicle, podcasts have continued to grow in popularity over the past decade or more. However, the efficacy of using podcasts within educational settings has been mixed (e.g., Daniel & Woody, 2010; Lee & Chan, 2007). This workshop will introduce podcasts as a learning medium and describe ways in which they can be used to effectively complement traditional teaching approaches, either as an enhancement to the course, or as a resource for student review. Attendees will be introduced to several ready-made STEM podcast resources and engage in discussions on how to develop new content that is effective, both logistically and pedagogically.
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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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