Adaptive Learning

One of the hottest trends tipped for 2019 was in the area of adaptive learning, that is, where computer algorithms are used to orchestrate the interaction with the learner and deliver customised resources and learning activities to address their unique needs. Not that this topic is new as it has been under discussion for a number of years. However, despite that, it still appears that our technology has been lagging behind the market’s aspirations.

Although e-learning systems have developed some limited Adaptive functionality, they fall short in comparison to the intelligence-lead shopping experiences we all take for granted online. There are small signs of artificial intelligence (AI) at work across most LMS software but much of that work only scratches the surface. Therefore, more research and development is most certainly needed to enable e-learning environments to adopt the mantle of being truly Adaptive.

So what’s the rush?

LMS users expect their business IT systems to have the same AI capabilities of your average online store or YouTube. Their time is precious, and they expect to have delivered what they need rather than having to guess what’s available. They expect a business tool to understand and adapt to the career paths on offer and core competencies required by staff. Lastly, they rightly expect that a learning management system has the intelligence to learn from the activities of its own users. Against this list it becomes clear that most modern LMS software cannot make a credible Adaptive claim.

So are we, the LMS suppliers and e-learning content creators, guiding the software developers? Are we telling them clearly what our clients are asking for? I don’t hear them asking and I’m not seeing much evidence of us telling them. So here are just some of my personal ideas and I’d welcome you to add your thoughts.

Intelligent Learner Profiling

I believe that all learners should be entered into an LMS via a profiling system. This process would be completed by both themselves and their managers. The input system would provide sections allowing them to enter their interests and their business aspirations. Ideally it would include a short online assessment to establish their knowledge starting point and to self-score against a list of competencies. My thought process being that a more enlightened LMS would then automatically display the most pertinent courses. The data provided by the learner, used in conjunction with their manager and the assessment, would ensure that the courses shown match their personal needs.

Data Tracking

All systems should also intelligently track a learner’s path through the available content, using logic algorithms to continually offer up associated or connected content. At its most basic the system would decrease a learner’s need to search out topics of interest and help maintain momentum. Beyond that it could provide reports to HR departments and managers, helping identify when learners reach a knowledge plateau, or recognising emerging talent and those within the business ripe for reward or promotion. Reports of that nature could also show when learners embark on a particular knowledge path, only to run out of connected content, therefore, giving guidance on what new courses need to be purchased or commissioned.

Deeper Understanding

Taking a learner’s interaction data recording to the next level, the findings could provide reports on other key parameters, e.g. time spent taking a module (“consumption speed”), assessment attempts and errors. This data could be vital in providing information on areas that need further investigation or a positive intervention. The data would ideally allow future courses to be created from a variety of subsections which are known to work best for that specific learner. Given that level of deeper learning, future courses could be created in subsections. Presenting the same core information but using different styles and methods to match with learner preferences. For instance, information displayed graphically or spoken rather than read, so that when a user with known preferences starts a course the product is generated “on the fly” using those subsections that match the style that has previously worked best for that user. Research has shown that different people with various aptitudes and skills learn very differently when exposed to the same content. Our New World Adaptive LMS would overcome any disadvantages by always selecting the most appropriate and effective content by learner.

In Conclusion

So, it is obvious that Adaptive functionality could provide great benefits to our industry and our consumers. Offering tailored content from the point of enrolment and throughout the learners’ journey within the system. Helping them to select courses based on their own profile but adding content drawn from the wider understanding provided by managers or even the HR department. Offering alternate learning paths that match their own needs but which are aligned to the company’s goals, delivering courses blended from elements which match their natural learning styles and therefore maximise results and returns on investment.

For this level of Adaptive learning functionality to become a reality developers need to focus their activities in new areas, but it is up to all of us to guide them. So whether you agree with me or hold other strong views on the matter, make them known! Join in the conversation and add to the comments.

To find out more about what can already be achieved with Adaptive Learning why not call us now on 0330 024 2881. Alternatively, if you are a little shy, use our website chat feature or complete our online form.

Original Article by Cortexa

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