Student behaviour informs learning design

Researchers from Imperial College London developed an algorithm to guide online course design in this new article 'Data-driven unsupervised clustering of online learner behaviour' published by npj Science of Learning ⎮1 minute read

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Digital technology is proving to be a valuable compliment to education because of the growing demand for online courses. Students can coordinate study commitments around their social and work calendars, with the added bonus of studying wherever they want with a mobile device. The trend has highlighted the importance of identifying how students interact with online courses to guide curriculum design. 

Robert Peach and colleagues, Mauricio Barahano, Sophia Yaliraki and David Lefevre from Imperial College London, assessed students experiences with web-based online content. By applying a mathematical framework, the research team recorded the behaviour of 81 students studying 6 online courses as part of a two year post graduate management course taught at Imperial College. The algorithm identified trends in student online participation that reflected various behaviours, such as, the time learners engaged with study online and how quickly tasks were completed. The researchers applied this method not so much as a diagnostic tool but rather to inform learning designers about the best interventions to introduce, so student academic performance improves.  

For a more in depth description of the researchers experiences, read this free and open access article: Data-driven unsupervised clustering of online learner behaviour published by npj Science of Learning Journal.  

 

Gabrielle Ahern

Managing Community Editor, npj Science of Learning Community

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