The use of machine learning in e-learning

machine learning

Just as we expect employees to learn continuously to improve their work, machine learning enables learning management systems to improve in a similar way. Machine learning is a form of artificial intelligence (AI) that enables computers to learn without specific programming.

Machine learning works by analysing data and actions to discover patterns which can be used to create models or intelligent algorithms.

In an LMS, these algorithms are applied to data to recognise and understand the actions of individual users so their likes and dislikes, learning styles and preferences, and career aspirations can all be profiled.

This can be used to create learning paths personalised for the individual to support progress in their desired career direction. Machine learning can also be used to recommend the most suitable learning content and materials to improve an individual’s job performance.

Instead of everyone being presented with same training choices or having to search for what interests them, machine learning algorithms can tailor a constant stream of relevant content that will match an individual’s present job requirements and long-term career interests.

Machine learning enables an LMS to learn about users continuously, so each profile updates to reflect the individual’s progress and changes in their needs, giving a current assessment which is more use to line managers or HR and L&D than a historic annual assessment liable to date fast.

When fully integrated with an organisation’s HR systems, an LMS using machine learning can also track all the additional data they supply, which may not have been previously monitored, to identify important trends. This can improve activities such as recruiting, where LMS onboarding tools can filter out unsuitable candidates with targeted task-based assessments which can only be completed successfully by candidates with the right skills. And by keeping up to date with existing employees’ development needs, it can improve staff retention by suggesting career paths that match their current aspirations.

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