From personalisation to prediction, machine learning is transforming elearning

Written by Jonny Anderson on 28 November 2018 in Opinion
Opinion

When it comes to L&D, Jonny Anderson welcomes the rise of the machine (learning).

Reading time: 4 minutes.

Machine learning technology is being used to put learners in control, encouraging 'pull' from learners rather than 'push' from the L&D team. Modern employees expect a higher standard of elearning resources which are on demand, flexible and interactive by default.

Elearning providers are now using pattern recognition to not only personalise learning, but to predict learning requirements, automatically providing timely and relevant content. 

Individual and group personalisation 

L&D platforms must immediately present their usefulness to employees to fuel their curiosity and encourage the desire to seek information and learn. The key to developing a pull from learners is to personalise the experience from the beginning.

User experience can be dramatically improved by employing intelligent algorithms that learn individual habits and trends. When shopping online for example, you’ll be used to seeing lists of items ‘frequently bought together’ amongst other data led suggestions.

The next step is to have algorithms which understand groups of learners.

To be most effective, learning resources should be presented in a similar way, highlighting resources which are identified as being relevant. The next step is to have algorithms which understand groups of learners. A platform that knows which articles are of interest to experienced managers as well as what introductory material should be presented to new joiners.

Employees however do not fit into single discrete categories. A female manager who works in IT must be presented with content which is of particular relevance to both managers, female workers and the IT department to be truly tailored.

Getting this interaction of various data sets right and automating the presentation of relevant resources allows for a platform which feeds from the curiosity of many learners to provide an insightful experience.

Prediction

Over and above personalisation is prediction. Learners don’t always know what they are looking for, so it is the platform’s role to know what a learner may need and present it to them.

 

Two pieces of content may have very little in common but be of critical usefulness if presented together in niche contexts. Understanding these contexts where one piece of content seamlessly flows into another can be truly powerful.

Picture a new manager searching for an article on the subject of ‘performance reviews’. They may read an article, and take something useful tips from it, but miss insight that would greatly improve their outcomes.

Wouldn’t it be better if the platform was able to predict that a new manager looking for an article on performance reviews may also benefit from an article on ‘how to manage a difficult conversation’, or content on ‘imposter syndrome’?

Imposter syndrome (in which an individual, such as a new manager, fears of being exposed as a 'fraud') is unlikely to be searched for by an employee but content on the subject could be of great impact to someone unknowingly suffering from it. A learning platform that can predict which niche content will be useful in specific contexts is providing more than just materials - it is providing critical insight. 

Here are some practical steps to consider: 

  1. Break out of the mould of competency. Look beyond mandatory training and provide learning resources which provide insight and advice, not just core skills like first aid. Employees should be encouraged to learn all the time, not only when a competency module must be completed. The key to this is presenting relevant material which provides a recognisable benefit to employees.
  2. Enable curiosity. When employees are motivated to learn, resources must be provided in a way which is frictionless and encourages delving deeper. Suggesting content through pattern recognition allows for serendipitous discovery by the learner. The employee may not know the toolkit that will help them even exists, it is the learning platform’s role to identify what may be useful and suggest it to them.
  3. Make the most out of data. The learning platform should itself be learning all the time about individuals and groups who share common characteristics. By understanding learning journeys which are impactful in niche contexts, such as preparing a junior manager for their first performance review, a learning platform transforms from a repository into a critical tool.

Putting the learner in the driving seat

There is great opportunity for data to work harder for customers to enable learners to take control of their own development. By deploying machine learning algorithms relevant content ‘pings’ to the surface for easy discovery.

By using data and machine learning algorithms, which analyse who you are and the journey you’re taking, learning platforms can move beyond just looking at searches. Interest in one thing, combined with a second interest, may infer interest in a separate subject entirely - prediction.

By making smart use of machine learning technologies it is possible to create a highly personalised, individual learning experience which puts the learner in control.

 

About the author

Jonny Anderson is technical director at GoodPractice. 

 

 

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