Abstract

In 2019, The Open University celebrated its 50th anniversary. For most of its fifty years it has been delivering statistics education to both specialists and non-specialists, simultaneously and at scale. A single module must deliver statistics teaching across multiple qualifications in many different disciplines which means it is not possible to tailor module material and examples for each individual qualification. This is contrary to the way many service teaching modules are designed. For example, the introductory statistics module at The Open University delivers basic statistics and probability to around 1500 students a year. By structuring the material around topics to which everyone can relate; money, education and health, the module is able to engage students across a range of different qualifications from mathematics to economics, business studies, computing and natural sciences. Feedback from students has been overwhelmingly positive. Students are provided with printed material, which is supplemented with a variety of interactive elements, which will be described in this paper. Online forums provide a place for peer support. Small group tutorials, both face to face and online, are provided by tutors. Each tutor will typically support 20 students and provide feedback to those students on their assessable work. The students in an individual tutor’s group could be studying one of many different qualifications. All the module material, including all the interactive elements, are embedded in the university’s virtual learning environment which has made it possible to monitor how students engage with the material. This knowledge, and qualitative feedback from students, has provided support for the idea that by teaching through a narrative approach, with examples based on scenarios which have a broad interest to everyone, it is possible to engage all students with statistics regardless of their qualification. By taking this same narrative approach a new module is being created which will be studied concurrently by students in the final stages of their qualifications in mathematics, statistics, data science and economics. This paper will outline how the module will utilize Jupyter notebooks to deliver practical modelling with real data sets chosen to be of interest to all the different cohorts of students. During the last 50 years The Open University has always had to deliver modules at scale to students across many disciplines. With the increased use of data across qualifications, and the need for people to upskill in data analysis, it is essential to explore ways in which statistical training can be delivered at scale.

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