data

#MakeoverMonday Week 9 dataviz submission

Reading Time: 2 minutes

This week’s dataset was from European Institute for Gender Equality and shows the proportion of seats held by women in European parliaments and governments. Another great dataviz learning experience and on the whole I’m OK with the finished product this week, I know I still have a big journey ahead of me to create some decent work, but it feels like a big improvement and tangible progress on my previous effort. I used the timeline slider for the first time and explored Tableau’s formatting tools in more depth. I also applied some learnings from last time by going straight for portrait mode, using a decent font size, avoiding cognitive overload and keeping the screen elements to a minimum, leading with the key finding and then fleshing out the detail further down.

Learning dataviz with Makeover Monday

Reading Time: 4 minutes

I’ve been meaning to take part in Makeover Monday for some time as a way to improve my data storytelling skills. This weekly learning event has been running for a year or so and I love its simple but effective format: a data vizualisation and accompanying dataset is released at the start of each week and you simply read the brief, analyse the dataset and make over the visualisation, submitting your work into the Twitter dataviz community for feedback. I am a big fan learning by doing, so while a 10 week Coursera on Data Vizualisation might be interesting, I don’t think it would be nearly so useful as just getting stuck in with some open data sets and trying things out, getting feedback from the dataviz community and iterating your work. Active and social learning at its best!

Rule-based vs AI adaptive learning

Reading Time: 7 minutes

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Adaptive learning uses competence, behavioural and demographic data to tailor a digital learning experience around each learners unique needs. There’s a lot of hype around this area which might have you thinking its all about Artificial Intelligence (AI), but that’s not the case and there are two types of adaptive learning approaches: AI-based and Rule-based. Each will afford you different features, benefits and outcomes.

A vendor view of Learning Technologies 2017

Reading Time: 5 minutes

I spent two days last week at the Learning Technologies 2017 exhibition, working on the LEO stand (below). This annual event is split over two floors, with a paid conference upstairs and free exhibition downstairs. The stand was really busy for both days and the whole team came away absolutely exhausted, but I did manage to wander around the exhibition looking to see what the trends were this year and seeking out interesting new products.

Algorithms and echo chambers in the world of learning

Reading Time: 3 minutes

There has been lots in the news this past year about social media bias and echo chambers, which started gaining prominence when algorithms started meddling in your news feed. The major web companies collect a huge amount of data about you and in doing so are building a detailed profile comprising demographic data, likes and purchases and other data that has been captured and purchased. As you ‘like’ posts and pages, so the algorithm delivers similar content back to you. Your friends like certain things, or ‘people like you’ like certain things, and the algorithm delivers more of that content to you too. You search for and purchase certain things, and you get delivered content related to that. Maybe you even give away valuable data via an innocuous-looking Facebook quiz,  which is then sold to highest bidder and fed into yet more algorithms to target you with stuff you might ‘like’.

The quantified cyclist: analysing Strava data using R

Reading Time: 6 minutes


This post was edited on 09 May 2017 to add some clarity around authenticating with the Strava API.


In 2016 I made a commitment to myself to record every cycle ride I made. As both a leisure cyclist and cycle commuter, I was keen to know how far I rode in a year, what was the accumulated distance of my daily commute, what distance did I cover on my leisure rides. I already recorded my weekend rides in a phone app called Strava, so it was pretty easy to get into the habit of clicking a button on my phone every time I set off on a cycle commute too.

Building a learning analytics platform

Reading Time: 4 minutes

As learning analytics continues to rise up the agenda in the corporate learning & development (L&D) sector, one thing is becoming glaringly apparent: we should not expect a one-size-fits-all, off-the-shelf approach to learning analytics.  This is a specialist discipline that cannot be bottled up into a single product. Sure, there are products such as Knewton, a Product as a Service platform used to power other peoples’ tools. There are also LMS bolt-ons like Desire2Learn Insights or Blackboard Analytics but even they are not sold as off-the-shelf products, for example the Blackboard team “tailors each solution to your unique institutional profile”.  There are just far too many organisational factors at play for an L&D practitioner to be able to implement a learning analytics programme using an off-the-shelf tool.

xAPI Barcamp – a Learning Technologies fringe event

Reading Time: 3 minutes

The xAPI Barcamp at the end of the first day of the Learning Technologies conference attracted around fifty people, eager to talk xAPI over a few free drinks at the local pub! I was one of five invited experts alongside Andrew Downes from Rustici (@mrdownes), Mark Berthelemy from Wyver Solutions(@berthelemy), Ben Betts from Learning Locker (@bbetts) and Jonathan Archibald from Tesello (@jonarchibald). Moving around five tables in turn, each expert began by talking for a few minutes about what they were doing with xAPI, then the table held an open discussion.

Data science: the new skillset for learning technologists

Reading Time: 3 minutes

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For all the talk of big data being the next big thing in learning technology, few people mention that in workplace learning there just aren’t any examples of big data to speak of. The data collected just isn’t at the same scale. However, big data has led to an explosion in data analysis tools and techniques that learning technologists can use in their work. Throughout 2014 I’ve been dipping into data science MOOCs, learning the basics of R programming, and thinking about how to apply this within learning and development. These are some of my initial thoughts and notes.