Temporal patterns and dynamics of e-learning usage in medical education View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-02

AUTHORS

Pietro Panzarasa, Bernard Kujawski, Edward J. Hammond, C. Michael Roberts

ABSTRACT

Despite the increasing popularity of e-learning systems across a variety of educational programmes, there is relatively little understanding of how students and trainees distribute their learning efforts over time. This study aimed to analyse the usage patterns of an e-learning resource designed to support specialty training. Data were collected from e-learning Anaesthesia, a web-based training programme offered by the Royal College of Anaesthetists in partnership with e-Learning for Healthcare. We constructed the time series of 45,020 records of knowledge and self-assessment sessions accessed by 2491 trainees between August 2008 and February 2010. Analysis of the time series suggested that e-learning usage was characterised by concentrations of rapidly occurring sessions within short time frames of intense activity, separated by disproportionally long periods of reduced activity. Non-uniform temporal fluctuations of usage were pronounced especially for self-assessment sessions, the timing of which was highly correlated with examination dates. While on average trainees’ involvement in knowledge sessions was larger than in self-assessment sessions, for both sessions average hourly activity and length remained stable between 9:00 am and 10:00 pm during weekdays. Average daily activity decayed as the weekend approached, but average session length did not vary significantly across the week. Combined with previous research on distributed practice, learning time distribution and test-enhanced learning, our study has implications for the improvement of long-term retention through the redistribution of knowledge sessions uniformly over time and the sustainment of frequent information retrieval and repeated testing. Findings on hourly and daily periodicities also suggest how new learning materials could be broken down into suitable components that fit learners’ time constraints. More... »

PAGES

13-35

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11423-015-9407-4

DOI

http://dx.doi.org/10.1007/s11423-015-9407-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026181667


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