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Scholarly Insight Spring 2017: A Data Wrangler Perspective

Rienties, Bart; Rogaten, Jekaterina; Nguyen, Quan; Edwards, Chris; Gaved, Mark; Holt, Deborah; Herodotou, Christothea; Clow, Doug; Cross, Simon; Coughlan, Tim; Jones, Jan and Ullmann, Thomas (2017). Scholarly Insight Spring 2017: A Data Wrangler Perspective. Open University UK.

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Abstract

We are pleased to offer you our Scholarly insight Spring 2017: 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. One of the strategic priorities of Students First (Open University UK, 2017b) and big shifts in OU Redesign (Open University UK, 2017a) is to “simplify and clarify choice for students based upon recommend learning and/or appropriate directed pathways”. Making sense of Big Data in particular can be a challenge, especially students are following different pathways and qualification routes across the OU. Demand for actionable insights to help students make the best out of their OU experience and qualification in particular is currently strong (Open University UK, 2017a, 2017b), especially a desire for evidence of impact of “what works” (Ferguson & Clow, 2017). Furthermore, insights from big data and learning analytics in particular are now increasingly taken into consideration at the OU when designing, writing and revising modules, and in the evaluation of specific teaching approaches and technologies (Herodotou, Heiser, & Rienties, 2017; Herodotou, Rienties, et al., 2017; Rienties, Boroowa, et al., 2016).
With the new ways of working with Data Wrangling, first we have provided our basic statistical analyses in form of our Key Metrics report. Based upon three well-attended workshops to further improve our working with the Faculties in January/February 2017, for which we are forever grateful, we have further fine-tuned our ways of working, and Key Metrics report in particular (e.g., adding nations data). Second, the Datawrangler team has worked extensively with the Faculties on “bespoke requests” from Faculties, and where possible shared the insights across all Schools and Faculties. Some of this work is reflected in this report, while other insights will be shared in the next Autumn report. Third, the focus groups indicated that overall the Scholarly insight report was well received, but was rather lengthy and lacked specific input and recommendations for each Faculty.
Therefore, we have worked with key stakeholders to identify the top 10 big data and pedagogical “concerns and problems” in each Faculty, which we afterwards narrowed down to a top 5, and subsequently a top 3. Working organically in various Faculty sub-group meetings and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly Insights can help to inform and help first and foremost our students, but also spark some ideas how to further improve our module designs and qualification pathways. Some of the topics in this top 10 will be addressed in the next Autumn report, and we are of course keen to hear what other topics require Big Data and Scholarly insight.

Item Type: Other
Copyright Holders: 2016 Open University UK
Extra Information: This version of the report is edited for an external (non-Open University) audience. Please contact the lead author if you want to receive a full copy of the detailed report, and indicate specifically whether or not you are a member of the Open University UK.
Keywords: learning analytics; data wrangling; learning design
Academic Unit/School: Learning Teaching and Innovation (LTI) > Institute of Educational Technology (IET)
Learning Teaching and Innovation (LTI)
Learning Teaching and Innovation (LTI) > Research Strategy & Operations
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Centre for Research in Education and Educational Technology (CREET)
Centre for Policing Research and Learning (CPRL)
Health and Wellbeing PRA (Priority Research Area)
Item ID: 49648
Depositing User: Bart Rienties
Date Deposited: 19 Jun 2017 15:54
Last Modified: 10 Oct 2017 16:44
URI: http://oro.open.ac.uk/id/eprint/49648
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