Exploring a Peer Support Structure for Jupyter Notebooks and R on M348 (Applied Statistical Modelling)

Project leader(s): Jotham Gaudoin
Theme: Supporting students
Faculty: STEM
Status: Current
Dates: April 2022 to July 2023

M348 (Applied Statistical Modelling) is a new module in applied statistics that will involve developing skills in the R software package via the use of Jupyter notebooks. It is a compulsory module for students on several qualifications and these students have a range of different backgrounds. Students from Q36 (Mathematics and Statistics) are likely to have a better understanding of the mathematical concepts in the module, having had more exposure to these than students from Q15 (Economics and Mathematical Sciences) or R30 (Economics), who may well better understand the practical interpretation of relevant results in their context. Meanwhile, students from R38 (Data Science) will have had rather more computational background imparted by the Level 1 and Level 2 study in computing that they will have undertaken by the time they study M348.

Our aim in this project is to leverage some of the computing expertise demonstrated by particular groups of students on M348. In each of the practical statistics modules available within the department of Mathematics and Statistics, students are helped to develop skills in a particular software package in which they can undertake their analyses. In M348, this will be R via the use of Jupyter notebooks. Unfortunately, even the installation process for the Jupyter notebooks with R is rather more complex than students will be used to and driving the software requires interaction with a command line interface, rather than the simpler ‘point-and-click’ that students will have been accustomed to in previous statistics modules. We have observed that during presentations of other statistics modules, there tend to be a small number of students who help other students with their statistical computing and coding difficulties and we wish to formalise this through asking for a small number of confident volunteers who will take M348 to undertake some advance training (in particular, accessing Unit 1 of the module early, with the support of a tutor) to then prepare to help other students with their computing difficulties in a peer support setup. If successful, this model could then be rolled out across other statistics modules.