Listen Up! Using Podcasts in STEM Courses to Improve Engagement and Facilitate Review
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.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".