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Chibeza Agley

OBRIZUM

CEO and Co-Founder

Read more from Chibeza Agley

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How to escape the linear learning trap

Replacing rigid linear training methods with adaptive learning helps remove inefficiencies and ensures all unique learning needs are met.
Watercolour painting of two heads sharing brainwaves linear learning

Learning sits at the very heart of business evolution and success. In times of talent shortages with an increasing demand for upskilling, inefficient learning is holding businesses and individuals back from achieving their true potential. 

Training courses and learning programmes are a necessity for businesses, however, organisations working in fast-moving areas often can’t create or manage/curate resources quickly enough to match their skills development needs.

Businesses need to evolve, which means changing their mindsets towards learning

It’s also evident that providing stock training material no longer does the job as industries evolve at an unprecedented pace and each organisation and every individual will have different background knowledge, know-how, experience, interests and objectives. 

When time is money, businesses cannot afford to spend another penny on inefficiency. Human capital costs are normally the highest outgoing expenses in any company, so not having a highly trained workforce that is confident in their roles can cost organisations hundreds of millions of pounds a year.

Businesses need to evolve, which means changing their mindsets towards learning. It’s time to abandon the age-old, one-size-fits-all, linear learning programmes, and replace them with something far more adaptive. 

What is linear learning?

A lot of eLearning today consists of a sequence of content that users must view in a predefined order. This is the basis of linear learning.

It delivers a step-by-step exposition of the topic, usually broken down into chapters, or modules. Typically, the level of complexity might increase as you progress – but this is often programme specific. Most linear programmes today are measured through completion data: have they completed the course, or have they not? Naturally, this delivers a very limited overview of the effectiveness of the programme. 

The perception of linear learning is that providing learners with access to the same resources at the same time will result in all participants developing the same knowledge at the same point in time. But this isn’t realistic. 

Why is linear learning ineffective and costly to businesses?

As we’ve established, the very concept of linear learning is flawed. Learning programmes that are structured to cover everything a beginner needs often result in the more advanced individuals becoming bored and dropping out, causing low completion rates. They can also incite a lack of engagement in the content, with learners quickly clicking through to completion, which is a waste of time for both the learner and the business.

Alternatively, programmes that are structured to find the medium between advanced learners and beginners can cause the latter to be overwhelmed with complexity, further fuelling dropout and failure rates. 

All too often these programmes are constructed against averages. However, there is no such thing as an ‘average’ learner.

Investing money into something that isn’t tailored to each individual user is extremely costly to the business

Expert learners are never going to be served by an ‘average’ journey. They’ll want to get ahead and breeze through the early stages of the course. Whereas those with less confidence will need far more examples and supportive elements throughout the learning process, which a ‘one-size-fits-all’ linear journey will not give them. 

Allocating a budget to this type of learning is essentially money down the drain. There is clear evidence that averages do not work. In the 1950s, the US air force commenced a project to redesign the cockpit, so they employed researchers to measure over 4,000 pilots on 140 dimensions of size (height, arm circumference, the distance between eyes, etc).

Averages were taken across all dimensions and used to build the cockpit model. However, once complete, it was recorded that not a single pilot fit the new measurements. 

Investing money into something that isn’t tailored to each individual user is extremely costly to the business. 

Escaping the linear learning trap

The learning and development market has already seen a number of attempts to move away from the regimented linear learning model. 

One is the recommendation functionality, which usually involves a series of manually tagged programmes and courses that are suggested to a learner based on their roles, skills, peers, and preferences. Another requires the organisers to run a manual pre-assessment of all participants before the individuals begin the course.

Once complete, each learner is then moved into the next course based on how well they scored, creating a ‘decision tree’ style of learning where each course requires predefined labelling. This in turn creates additional decision-making requirements for the programme leaders to decide which participants follow each path, at each stage.  

The ultimate way of avoiding the linear model is to think of learning programmes as a sat nav. It needs to constantly assess where the individual currently is, and calculate the most efficient route to complete the journey. 

Businesses can use the power of artificial intelligence to guide learners through content and assessments based on their learning speeds, patterns and progressing knowledge. Only with this level of data and analytics can teams deliver personalised journeys on such a granular level at scale. This helps learners feel like they’re being supported, rather than lectured. 

Participants are challenged on topics that they have already shown competency in, and strengthened in topics they are less proficient in. The non-linear adaptive learning approach is far more efficient than traditional linear sequenced methods; and unlike conventional digital learning, it allows for greater measurement and assessment of knowledge. 

Businesses can no longer afford to waste time and money on training courses that don’t do the job

It starts with mindsets

Above all else, businesses need a change in mindset when it comes to education. Adaptive learning uses an entirely different process for content creation which is more flexible, agile and accessible, but it has the power to transform old-fashioned approaches into futuristic adaptive learning spaces. 

When the demand for upskilling workers is so high, businesses can no longer afford to waste time and money on training courses that don’t do the job. We all learn differently, so non-linear models that take each participant on a truly tailored learning journey are the only effective way of ensuring each individual’s needs are met. 

Interested in this topic? Read Replace your boring eLearning with adaptive learning.

One Response

  1. This article talks about
    This article talks about skills, but then falls back on the standard tropes that Knowledge = skills. Our focus should not be on more efficient access to educational content but effective access to learning experiences, involving work place activities supported by coaches, peers and line managers.

    If, as Chibeza says, one size does not fit all – a sentiment I heartily agree with – then maybe some of our people may develop their skills without new digital content, however cleverly developed.

    Of course, some content will be required for some people. Furthermore, there is some benefit in developing standard terminology and language to ensure everyone has the base line knowledge to communicate effectively with each other and develop their skills, working within a common framework and to shared standards. This facilitates peer-to-peer learning.

    But that doesn’t mean that having everyone using the same terminology equates to skill development. Upskilling and reskilling needs far more than a smarter, non-linear route through yet more educative content.

Author Profile Picture
Chibeza Agley

CEO and Co-Founder

Read more from Chibeza Agley
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