Foundations of dynamic learning analytics: Using university student data to increase retention

Sara de Freitas, David Gibson, Coert Du Plessis, Pat Halloran, Ed Williams, Matt Ambrose, Ian Dunwell, Sylvetser Arnab

Research output: Contribution to journalArticle

49 Citations (Scopus)
83 Downloads (Pure)

Abstract

With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self-organising maps, to generate conversations, shared inquiry and solution-seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates.
Original languageEnglish
Pages (from-to)1175-1188
JournalBritish Journal of Educational Technology
Volume46
Issue number6
Early online date15 Oct 2014
DOIs
Publication statusPublished - Nov 2015

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university
learning
student
demand for information
consumption behavior
educational institution
electronic learning
experience
conversation
stakeholder
educator
trend
interaction
school
education

Bibliographical note

This is the peer reviewed version of the following article: de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Ambrose, M., Dunwell, I. and Arnab, S. (2015) Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, volume 46 (6): 1175-1188, which has been published in final form at http://dx.doi.org/10.1111/bjet.12212. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.

Cite this

Foundations of dynamic learning analytics: Using university student data to increase retention. / de Freitas, Sara; Gibson, David; Du Plessis, Coert; Halloran, Pat; Williams, Ed ; Ambrose, Matt; Dunwell, Ian; Arnab, Sylvetser.

In: British Journal of Educational Technology, Vol. 46, No. 6, 11.2015, p. 1175-1188.

Research output: Contribution to journalArticle

de Freitas, Sara ; Gibson, David ; Du Plessis, Coert ; Halloran, Pat ; Williams, Ed ; Ambrose, Matt ; Dunwell, Ian ; Arnab, Sylvetser. / Foundations of dynamic learning analytics: Using university student data to increase retention. In: British Journal of Educational Technology. 2015 ; Vol. 46, No. 6. pp. 1175-1188.
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