Improve e-learning outcomes using big data


The most common definition of Big Data is known as the 3Vs, Volume, Velocity & Variety.

Volume – large quantities of data coming in, much more than would normally be expected.

Velocity – the data is coming in quickly, updates may be many a second, rather than longer time periods.

Variety – there is a multitude of streams of data coming in of different types, this can make it hard to analyse.

Although a lot of systems generate a large amount of data, utilising this is still in its infancy, particularly in the learning technology sector. A lot of large corporations are using big data to their advantage. One of the most common are web retailers and store loyalty cards. These systems store data which then shows offers which relate to products you (or people like you) buy. Another example is when you log in on sites such as, you will usually be shown products you might be interested in based on your viewing or purchasing history.

Google Trends can also show you popular search terms, or compare search terms over time. This is an excellent use of big data and can help your business predict upcoming trends and inform your SEO strategy.

You can also use the data generated by your LMS to examine staff and client learning patterns. This can help you to examine how your users use the LMS, which can have a number of useful benefits.

Your IT department will find it easier to plan downtime for upgrades as you will be able to see when your LMS has the lowest amount of traffic. You will be able to examine how your learners complete a course e.g. do they follow a linear pattern or jump around the course.

You’ll also be able to look at users attitudes to learning, this could be done via an ATTLS test, but it is also possible to view this indirectly by seeing how they perform when particular topics are taught in different ways. Once you can see which teaching methods are working the best for a group of learners, you could also compare this to demographic data to see if there are patterns (for instance younger users performing better on modules taught by video).

In a similar way, you can see which modules are the most popular or successful, as well as seeing which have the highest user failure rate or drop out rate. This is incredibly useful for both internal and external training, as it can feed it’s strategic direction.

Big data is very useful for testing innovations in learning. You could test a module taught with a new method and compare it to the old. For example one term, you could teach a module using a powerpoint presentation, and another term using Prezi, and see which method helps your learners acquire knowledge. The data can also introduce student led developments, which may not be in a direction that you originally planned.

The detailed information about how individual demographics learn makes it much easier to created personalized learning plans, to respond to users needs. The ability to stay responsive will ensure that your training is at its most effective.

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