Making sense of learner and learning Big Data: reviewing 5 years of Data Wrangling at the Open University UK

Rienties, Bart; Cross, Simon; Marsh, Vicky and Ullmann, Thomas (2017). Making sense of learner and learning Big Data: reviewing 5 years of Data Wrangling at the Open University UK. Open Learning: The Journal of Open, Distance and e-learning, 32(3) pp. 279–293.

DOI: https://doi.org/10.1080/02680513.2017.1348291

Abstract

Most distance learning institutions collect vast amounts of learner and learning data. Making sense of this “Big Data” can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, in 2012 the Open University UK (OU) instigated a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical, and sense-making advice to staff and senior management. Given substantial changes within the OU over the last 18 months (e.g., new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case-study provides an in-depth review of lessons learned of 5 years of data wrangling. Using semi-structured interviews with key stakeholders (10 senior managers/associate deans) and ten Data Wranglers (DWs), a clear mismatch was identified in terms of resources, expertise, and skills that can effectively address key needs from Faculties. Furthermore, inconsistencies in terms of reporting and responding to bespoke requests were noted by stakeholders. Given the essential role of DW for the OU, a new DW structure is proposed to ensure effective provision of in-depth, evidence-based data analyses, pedagogical insight, and actionable advice for Faculties. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.

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