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Scholarly insight Spring 2018: a Data wrangler perspective

Ullmann, Thomas; Lay, Stephanie; Cross, Simon; Edwards, Chris; Gaved, Mark; Jones, Edwina; Hidalgo, Rafael; Evans, Gerald; Lowe, Sue; Calder, Kathleen; Clow, Doug; Coughlan, Tim; Herodotou, Christothea; Mangafa, Chrysoula and Rienties, Bart (2018). Scholarly insight Spring 2018: a Data wrangler perspective. Open University UK, Milton Keynes.

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Abstract

In the movie classic Back to the Future a young Michael J. Fox is able to explore the past by a time machine developed by the slightly bizarre but exquisite Dr Brown. Unexpectedly by some small intervention the course of history was changed a bit along Fox’s adventures. In this fourth Scholarly Insight Report we have explored two innovative approaches to learn from OU data of the past, which hopefully in the future will make a large difference in how we support our students and design and implement our teaching and learning practices. In Chapter 1, we provide an in-depth analysis of 50 thousands comments expressed by students through the Student Experience on a Module (SEAM) questionnaire. By analysing over 2.5 million words using big data approaches, our Scholarly insights indicate that not all student voices are heard. Furthermore, our big data analysis indicate useful potential insights to explore how student voices change over time, and for which particular modules emergent themes might arise.
In Chapter 2 we provide our second innovative approach of a proof-of-concept of qualification path way using graph approaches. By exploring existing data of one qualification (i.e., Psychology), we show that students make a range of pathway choices during their qualification, some of which are more successful than others. As highlighted in our previous Scholarly Insight Reports, getting data from a qualification perspective within the OU is a difficult and challenging process, and the proof-of-concept provided in Chapter 2 might provide a way forward to better understand and support the complex choices our students make.
In Chapter 3, we provide a slightly more practically-oriented and perhaps down to earth approach focussing on the lessons-learned with Analytics4Action. Over the last four years nearly a hundred modules have worked with more active use of data and insights into module presentation to support their students. In Chapter 3 several good-practices are described by the LTI/TEL learning design team, as well as three innovative case-studies which we hope will inspire you to try something new as well.
Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties, we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight. We hope that you see some potential in the two innovative approaches, and perhaps you might want to try some new ideas in your module. While a time machine has not really been invented yet, with the increasing rich and fine-grained data about our students and our learning practices we are getting closer to understand what really drives our students.

Item Type: Other
Extra Information: Final Anonymised Version
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Learning and Teaching Innovation (LTI) > Quality Enhancement & Learning Analytics
Learning and Teaching Innovation (LTI) > Technology Enhanced Learning (TEL)
Research Group: Centre for Research in Education and Educational Technology (CREET)
Item ID: 56732
Depositing User: Bart Rienties
Date Deposited: 18 Oct 2018 10:30
Last Modified: 18 Oct 2018 13:29
URI: http://oro.open.ac.uk/id/eprint/56732
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