RESUMO
Podcasts are an increasingly popular medical education modality, especially in surgical fields. However, the cost of developing a high-quality medical education podcast presents a barrier to many content creators. The authors developed the podcast series 'The Lenspod,' designed to be a cost-efficient but high-quality education resource in ophthalmology. The REC financial framework has been previously used to estimate the financial costs of technology-based medical education. Using this framework, costs were competitive with other medical education podcasts. It is our hope that similar methodology may be used to create and disseminate future podcasts for medical education.
Les balados sont une modalité d'enseignement médical de plus en plus populaire, en particulier dans les domaines chirurgicaux. Cependant, le coût de création d'un balado éducatif de qualité en médecine constitue un obstacle pour de nombreux créateurs de contenu. Les auteurs sont les créateurs de la série de balados The Lenspod, qui se veut une ressource éducative à la fois rentable et de qualité en ophtalmologie. Appliquant le cadre financier REC, déjà utilisé pour estimer les coûts financiers de modes d'enseignement médical basés sur la technologie, nous avons constaté que les coûts de notre balado sont compétitifs par rapport à d'autres en éducation médicale. Nous espérons qu'une méthode similaire sera utilisée pour créer et diffuser davantage de balados éducatifs en médecine.
Assuntos
Educação Médica , Oftalmologia , Humanos , Estudantes , Avaliação EducacionalRESUMO
We propose a latent topic model with a Markov transition for process data, which consists of time-stamped events recorded in a log file. Such data are becoming more widely available in computer-based educational assessment with complex problem-solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward-backward variational expectation-maximization (FB-VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem-solving item in the 2012 version of the Programme for International Student Assessment (PISA).