Getting started with Learning Analytics: a Literature Review

“What is learning analytics anyway?”

I’ve answered this question a lot since starting my PhD 5 weeks ago, with varying degrees of eloquence.

My go-to definition when writing assignments for my MA was:

LEARNING ANALYTICS is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”

Society of Learning Analytics Research (n.d)

My operational – since that’s my background, rather than academic – answer is that Learning Analytics is looking at the data produced by online learning as it happens, and responding to that information in as close to real time as possible. The response is an action that leads to improvement in learning.

A simple example: if 100 students take an online quiz and 98 of them fail question 4 but pass overall, the tutor can investigate. Was the information needed for question 4 adequately covered in the course? Was the wording of question 4 clear? Is the answer to question 4 correctly coded in the quiz? Immediate action (covering the information for question 4 in the next lesson perhaps) means no knowledge gap, and the issue can be rectified for the next cohort.

My draft thesis title is Data driven reflection in professional learning: Learning design to engage final year student Chartered Accountants in learning analytics. I want to investigate the role of actionable feedback on both performance in mock exams and engagement with course materials. Part of this study will be creating a tool that uses marking data to give students real-time consistent and actionable feedback.

I’ve started my literature review by reading “Utilizing Learning Analytics to Support Study Success” (Ifenthaler et al, (eds) 2019). This extensive and relevant collection covers learning theory, implementation, and challenges both in depth and in an accessible way. I am still experimenting with how I will take useful notes on long (300+ pages) texts (I will use Mendeley for shorter articles and papers) – for this book I generated 44 pages of typed notes. These notes include follow up reading – 51 references in the edited version, which I now need to prioritise.

A useful exercise was to create a list of ‘key phrases’ from the notes I made – words or phrases that come up time and time again. I’ll use this to start to structure my literature review. In a moment of creativity (or perhaps procrastination) I dropped them into and generated this word cloud.

It certainly shows the complexities of this field of study.

Now, onwards with that list of reading!


Ifenthaler, D., Mah, D., & Yau, JY. (eds) (2019) Utilizing Learning Analytics to Support Study Success, Switzerland, Springer Nature Switzerland AG. Available at (Accessed 5 November 2021).

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Published by sarahjalcock

PhD researcher at the Open University with a focus on Learning Analytics, Learning Analytics Dashboards, feedback and Self Regulated Learning. Follow me on Twitter @SarahAlcock19. Author text is licenced under Creative Commons Attribution-ShareAlike CC BY-SA

2 thoughts on “Getting started with Learning Analytics: a Literature Review

  1. You’ll probably find it easier to make notes on edited books chapter by chapter, as that’s likely to be how you’ll reference them. That will also help you to identify which chapters are stronger and which are more relevant to your own work.
    Wordclouds are usually not as helpful as you expect them to be in making sense of your reading. In this case, they suggest you didn’t make many notes about reflection or learning design. Is that because the book didn’t deal with these subjects or because you’re actually more interested in other things?


    1. Thank you for these tips @r3becca, yes I’m keeping track of the chapters carefully as I fell foul of this problem in my MA! The word cloud was really a distraction exercise than a tool, but it did show the complexity of the field as a whole. The book is quite wide ranging but the follow up reading I have listed are all related to reflection, visualisations and learning design as these are key for me.


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