Listen up! Exploring the impact of podcasts as a teaching aid and assessment method in management education
Notice bibliographique
Résumé
Background 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). Context 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».