Why learning analytics?
Learning Analytics has leapt in at Number 1 on Donald Taylor’s International Survey. What’s interesting is the next four positions, as they are all related:
1 Learning Analytics
2 Personalised/adaptive learning
3 Collaborative/social learning
4 LXP (Learning Experience Platforms)
5 Artificial Intelligence
I’ve been working solely on this cluster of things for the last five years and want to tease out a simple point – they have to be seen as a whole as they are all intimately related:
1 fuels 2/3/4/5
4 is the delivery platform for 1/2/3/5
Why now?
Beyond numbers of people taking courses, literally ‘bums on seats’, which is to measure the wrong end of the learner, learning has never been particularly analytic. Few collect detailed data to describe learner behaviour with even basic analysis. Fewer still delve deeply into that data for insights to inform, predict or prescribe future decision making and action. It is not clear that dashboards improve the situation much. It is still a descriptive ‘bums on seats’ approach.
The learning world attracts ‘people’ people, with an interest in the development of others, rather than many from a scientific or analytic background, with an interest in systems and data. This, in a sense, pushes learning professionals away from learning analytics. We must overcome this reliance on qualitative perceptions and judgements, including the old and laboured Kirkpatrick schema, which is a statistical mess.
It is time to move the client towards a more data-led approach. This does not mean becoming an expert in data or statistics, as the technology does all of the computation and heavy lifting on the maths and stats. Learning professionals will not be analysts but consumers of data and data-driven automation. The analysis will largely be done for them.
The world is becoming more data-driven, organisations more data-driven and even at the level of the individual, personalised service is an expectation. This puts pressure on the learning world to respond by being sensitive to this need for data as fuel and algorithms as the rocket that will allow us to boldly go to places we have never been before.
Data is everywhere. You are a mass of data points, your face is data for face recognition, your body a mass of data points for healthcare, your behaviours area data points for online organisations, you network online with other people and information which are all data points, your car is a data point for GPS. You are and live in a sea of data. This is also true of learning. What you know, when you learnt things, how well you know things, your performance. Like it or not, you are all masses of data. This doesn’t diminish your humanity, it informs decision making and can make life easier and more productive.
If you have a LMS (Learning Management System) you will, most likely, have been gathering data under the SCORM specification. Unfortunately, this has an old and severely limited capability, really focusing on who did what, when and did they complete courses. If you have been using Kirkpatrick, you will most likely have been gathering the wrong data with little analysis. It’s time for a rethink.
Moving beyond this specification, xAPI has been defined by the same people who gave us SCORM. This is a new specification more suited to the current landscape of multiple sources for learning and a more dynamic view of how people learn, along with a need for many more types of data than in the past. Similarly with then shift from the LMS to LXP. That’s why LXPs have appeared. The world has moved on, organisations have moved on, the technology has moved on and so learning professionals should move on.
This means leveraging data to be more focused, efficient and aligned with your organisation’s strategy. It should lead to better decision making, more action, more automation and provable impact on the business.
How to start?
Don’t think just dashboards. They trap you in a world of reading what IS the case, rather than deciding what SHOULD be the case. We need to derive an OUGHT from an IS and push them beyond dashboards to decisions, actions and the automation of process.
What we need is strategic view of data use. Here’s the good news, a schema has existed for a long time in data science, classifying data use into four areas:
Describe
Analyse
Predict
Prescribe
Innovate
This allows you to think ambitiously about data, moving beyond metre description (dashboards) towards using to help improve and shape teaching and learning.
Describe
Data that describes what is the case, describing learners, their behaviour and the technology is descriptive. That’s what dashboards do. Don’t get stuck with dashboards only – they are merely descriptive.
Analyse
Analysis gives you deeper insights into data and may, even at a simple level, provide useful insights in terms of informing decisions and action. Don’t worry, the software should do the analysis for you – you don’t have to become a data scientist!
Predict
Data that predicts what your organisation or group or individual learner needs can be used to recommend action. Recommendation engines drive most of what you do online (Google, Social Media, Amazon, Netflix). This allows you to deliver personalised learning.
Prescribe
This is where data makes things happen. A real recommendation is provided or nudges pushed, spaced practice applied, adaptive learning applied. The software literally uses data to enact something for real.
Innovate
Beyond this lies the use of data for more innovative uses in learning such as sentiment analysis, content creation, curation and chatbots.
Conclusion
Having spent the last few years doing all of the above, I think we are about to enter a new era, where smarter software (AI/data-driven) will deliver smarter solutions. I now see real clients use data, not just on dashboards, but to drive engagement, learner support, content creation, curation, assessment, sentiment analysis, chatbots and so on. Happy to help with any of this stuff… DM me or contact me on the form here…
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