Listen up! Exploring the impact of podcasts as a teaching aid and assessment method in management education
Bibliographic record
Abstract
Background<br/>There is growing recognition of the value of Technology Enhanced Learning (TEL) in terms of maximising the student learning experience and improving employability, provided it is used effectively (Higher Education Academy 2019). TEL is broadly considered to be the application of information and communication technologies to teaching and learning (Kirkwood and Price 2013) of which the podcast is an increasingly popular form. Within higher education, they have been used for a range of purposes, including the delivery of audio recordings of lectures (Copley 2007); a supplementary instructional tool (Baker et al 2008); a means of assessment (Powell and Robson 2014); and a format for feedback (France and Wheeler 2007). Perceived benefits of utilising podcasts include their flexibility and capacity to support independent learning (Heilesen 2010). When used as a form of assessment, podcasts may allow students to enhance their technical skills; provide insight into new technology; and increase confidence in using alternative forms of media. In addition, they can facilitate the development of a range of transferable skills, such as problem solving and time management (Powell and Robson 2014). However, the novel format may cause students to experience heightened emotions and greater concerns regarding their ability to complete the assignment (Sharpe and Benfield 2005). There are also practical considerations to be addressed, such as the quality of guidance given and the means of submission. Despite their increased used, there is a lack of robust evidence as to their impact in terms of enhancing the teaching and learning experience (Kazlauskas and Robinson 2012). <br/><br/>Context <br/>At Queen’s Management School, podcasts have been adopted as both a teaching and learning aid and an assessment method. Regarding the former, postgraduate students completing a module on Business Governance and Ethics are directed to listen to episodes of the School’s ‘Good Business Podcast’ series, which brings together brings together academics, entrepreneurs, industry leaders, and other key stakeholders to discuss issues related to ethics, responsibility, and sustainability. The podcast, which is hosted by the Module Coordinator, is also made available to the public via the University website and the Mixcloud platform. Episodes thus far have featured the Head of Human Rights for Marks & Spencer and the International Manager of the BAFTA albert television production sustainability initiative, amongst others. In terms of assessment, in a final year undergraduate module on Innovation Management, students are required to produce a 15-minute podcast communicating key aspects of the module content. Students must select three topics from a choice of five across the module, defining and discussing their central argument and highlighting case study examples to demonstrate practical application. The podcast aims to test students’ depth of understanding of the topic along with broader skills such as effective communication, creativity and the associated technical skills required for the production.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".