Any data, whether it is big or small can provide a useful insight into learner performance and experience. Whilst big data is nothing new and has been a familiar term for a number of years, we will explore whether big data can improve your E-Learning.
In many circles big data has become a buzz word, but what does it mean?
Big data is the gathering of information that is so expansive and complex that it is difficult to manage or organise utilising traditional methods. It is characterised by its ability to quantify events or facts that haven’t previously been traceable in data form. It can uncover correlation between events but not necessarily causation.
So, how can big data be used in E-Learning?
The use of big data relies on the principle that human behaviour has a level of universality and therefore can be predicted through trends.
With just a limited amount of information about a learner, we can predict a wealth of information, including future behaviour. It doesn’t take a huge amount of data mining to gain a fairly accurate insight into an individual learner.
Through data mining and analysing, it is possible to track:
- An individual learner’s knowledge gaps;
- Parts of a course where learners often get stuck;
- Pages or topics that are shared or revisited most often; and
- When learners tend to tune in to E-Learning.
This information in turn means big data will allow:
1) Increased personalisation of learning content
Research and data analysis can allow E-Learning providers to identify correlations. For example, if a learner struggles with one topic, relevant data may be able to illustrate that there is a correlation between those who find that topic difficult and those who find a later topic a struggle. Using this information, learning instructors could identify a knowledge gap that is causing this issue and resolve it at an earlier stage.
2) Instructors to provide timely motivation
If users consistently drop-out of a course or fail at a particular point, learning instructors could recognise this using big data and rectify the course accordingly by providing more encouragement or even making assessments less difficult.
3) The testing and evolution of learning theories and content
Big data allows instructional designers to see what content works in successfully instructing learners. They can then amend content accordingly, using the knowledge they have gained in order to restructure it and remove or improve any weaknesses. In addition, big data allows for the widespread testing of learning theories, allowing learning theorists to draw empirical conclusions about how people learn.
As with all new things, there are things to be wary of:
1) Privacy
The media increasingly report that our digital lives are subject to monitoring of Orwellian proportions. As a learning instructor, you will want your voice to be one of authority and confidence. Therefore, be clear with learners what information you are collecting and why. Learners want to know that they can trust what you have to say and that information they share with you is in safe hands.
2) Relevance
When using big data, setting a good foundation is important. It is vital to capture information that is both accurate and relevant before it can begin to be organised in such a way that creates meaning for E-Learning decision makers and administrators.
It is of no question that big data is set to be a very practical and tangible asset to E-Learning. If learning administrators can capture the appropriate information and organise it effectively, they can personalise learning to suit individual learners and ultimately create better E-Learning.
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