Breaking barriers, building community: improving student engagement with preparation for studying online multidisciplinary science by distance learning - a case study

Christopher Hutton, Julie Robson

Abstract


Developing an online learning community can help to improve student success. This presents a challenge as the student cohort is often time-limited and shifting in composition before a module starts. The continued evaluation and development of an online preparatory website for the new, year one module S112 - Science: concepts and practice is reported. Evaluation of the site in 2017-18 showed high levels of student satisfaction with the resources, though very little engagement with the asynchronous forum. Therefore, improvements before the October 2018 module start centred on increasing student engagement on the forum: Firstly, tutors moderating the forums were tasked with developing several optional scientific tasks for staged release to generate student discussion. Secondly, volunteer peer mentors from the 2017-18 presentation were recruited and trained as “student buddies” to provide non-academic advice and support.

Student engagement increased markedly prior to the October 2018 module. The clearest emergent theme from tutors who moderated the forums was that students were seeking to establish a study community, rather than obtain subject-specific advice. Student response to the buddies was positive, with students more willing to ask questions of the buddies than of the tutor moderators. Data for S112 show that registrations at module start increased by 23.5% from 2017 to 2018, and early withdrawals (14 days after module start) dropped by 2%.

Our results provide ideas for building online student engagement, particularly in scenarios where students may come and go, and have little available time: for example, bridging gaps in time or academic readiness, whether by blended or distance learning.


Keywords


Online community; Learning community; Building community; Transient; Asynchronous; Peer mentor; Distance learning; Blended learning

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DOI: https://doi.org/10.29311/ndtps.v0i14.3217

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