Engagement Analysis for Algebrakit
Consultancy project analysing user engagement of an online learning platform and developing a subject-level difficulty index for the client.
Context
Algebrakit builds technology for mathematics education. As a data analytics consultant, I analysed behavioural data from the online learning platform and translated it into something the company could act on.
Problem
The client wanted to understand engagement on the platform — and, in particular, how the difficulty of its subjects could be measured and compared in a way that supports product and content decisions.
My role
I analysed user engagement data and developed a subject-level difficulty index — a measure that condenses behavioural signals into a comparable difficulty score per subject.
Data
User engagement data from the online learning platform.
Approach
- Exploratory analysis of engagement patterns across the platform's subjects.
- Designing and validating a subject-level difficulty index from behavioural signals.
- Iterative visualisation design, tested against the question: would the client know what to do after seeing this?
Results & insights
The project delivered an analysis of user engagement and a subject-level difficulty index the client can use to compare subjects and prioritise content work.
Challenges & limitations
- Working within the constraints of a real client relationship: scoping questions, respecting data boundaries, and delivering on a deadline.
- Any difficulty index involves judgement calls about what counts as difficulty — those choices had to be explicit and defensible.
What I learned
Consultancy compresses the full analytics workflow into a few weeks: understand the domain, analyse honestly, and communicate clearly. It taught me to treat the client conversation as part of the method, not an afterthought.
Related project
Predicting Student Dropout in Online Learning