1 Introduction

Getting started with the scholarship of teaching and learning can be difficult. For the majority of academics whose subject expertise does not involve learning and teaching, the first hurdle of figuring out what questions you can ask and answer (and indeed are interested in) can be the toughest one to push past.

Once you have settled on an area of inquiry, you may find that the most appropriate methodologies to investigate your questions are not ones you have been trained in. For quantitatively-minded researchers, the availability of data can feel like simultaneous feast and famine - you may have access to huge amounts of data through learning analytics and standard student records but will be able to use almost none of it for research purposes due to the need for opt-in consent. Where such consent has been obtained, you may have small, non-representative samples, non-random attrition, and/or concerns about making data openly available.

Finally, the data you do have can be seriously messy: missing data, data from multiple sources with different structures and labels, data from different academic years where course structures and assessments have changed, anonymised data, or aggregated data.

If any of this sounds familiar, this book is for you.

Each tutorial in this book will contain:

  • A short summary of the evidence-base for the problem under investigation to promote engagement with the SoTL literature;
  • Real1 data drawn from commonly available sources such as Moodle, Turnitin, Microsoft Forms, and Echo360;
  • A walkthrough of how to clean and wrangle the data using a predominantly tidyverse approach;
  • A walkthrough of how to analyse and interpret, the analysis, alongside an honest discussion of the limitations of the approach used.

1.1 Planned tutorials

We have the following tutorials planned but we're happy to take suggestions - please e-mail .

  • Analysing the impact of whether students check their feedback through Turnitin Feedback Studio on subsequent assessment performance.
  • Creating exam board and moderation reports using R
  • Using Moodle logs to predict engagement and retention

1.2 Expectations of prior knowledge

1.2.1 R and RStudio

Minimal prior knowledge of R and RStudio is assumed throughout this book. All functions and code used will be explained, however, we assume that the reader understands how to:

  • Install R and RStudio

  • Navigate RStudio

  • Set the working directory appropriately

  • Install and load packages

  • Write and execute code

Each chapter will start with an overview of expected prior knowledge along with resources to recap if necessary.

1.2.2 Research methods and statistics

We assume a basic level of competency in research methods and statistics. However, we also recognise that many researchers are still less familiar with more modern approaches such as mixed effects models and will provide an appropriate level of explanation and further resources where necessary.