We are pleased to offer you our first Scholarly insight 2016: a Data wrangler perspective. The OU is going through several fundamental changes, whereby strategic, pedagogical informed research and insight what drives student learning and academic performance is essential. Making sense of Big Data in particular can be a challenge, especially when data is stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. In 2012 the Open University UK (OU) instigated a Data Wrangling initiative, which provided every Faculty with a dedicated academic with expertise in 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, increased reliance on analytics), an extensive discussion with various stakeholders within the Faculties was initiated to make sure that data wranglers provide effective pedagogical insight based upon best practice and evidence-based analyses and research (see new Data wrangler structure).
Demand for actionable insights to help support OU staff and senior management in particular with module and qualification design is currently strong (Miller & Mork, 2013), especially a desire for evidence of impact of “what works” (Ferguson, Brasher, et al., 2016). Learning analytics are now increasingly taken into consideration when designing, writing and revising modules, and in the evaluation of specific teaching approaches and technologies (Rienties, Boroowa, et al., 2016). A range of data interrogation and visualization tools developed by the OU supports this (Calvert, 2014; Toetenel & Rienties, 2016b).
With the new ways of working with Data Wrangling, first we have provided our basic statistical analyses in form of our Key Metrics report. Second, from January 2017 onwards we will focus again on dealing with bespoke requests from Faculties, and where possible share the insights across all Schools and Faculties. Third, this Scholarly insight has a different purpose to previous Data wrangler work, namely we aim to provide state-of-the-art and forward looking insights into what drives our students and staff in terms of learning and learning success. Based upon consultation with the Faculties, seven key cross-Faculty themes were identified that influence our students’ learning experiences, academic performance, and retention. The first five chapters focus on how the OU designs modules, formative and summative assessments and feedback, helps students from informal to formal learning, and how these learning designs influence student satisfaction. All five chapters indicate that the way we design our modules fundamentally influences student satisfaction, and perhaps more importantly academic retention. Clear guidelines and good-reads are provided for how module teams, ALs, and others can improve our focus on Students First. In Chapter 6-7, we specifically address how individual student demographics (e.g., age, ethnicity, prior education) and accessibility in particular influence the students’ learning journeys, with concrete suggestions how to support our diverse groups of students. Note that each chapter can be read independently and in any particular order. We are looking forward to your feedback
