Artificial intelligence, or the rise of the smart machine, is the stuff of science fiction. But applying machine learning to apprenticeships can deliver a better learning experience, argues Mark Abrahams, Head of Research at MWS Technology.

The late, and great, Stephen Hawking once told the BBC that “The development of full artificial intelligence could spell the end of the human race.”

Dystopian fears are widespread when it comes to ‘intelligent’ machines. People perceive a loss of control or the replacement of humans by machines in the workplace. Increasingly, however, we are coming to understand that smart machines can help human societies improve what they do.

What implications does this have for the way we manage apprenticeship programmes?

Running apprenticeships involves a lot of administration – time that could be put to better use teaching apprentices, whether face to face or online. Resources are scarce. So how can artificial intelligence help workplaces deliver these schemes more efficiently?

 

How we use artificial intelligence

Organisations across many business sectors are now using machine learning or artificial intelligence (AI) to improve efficiency and their understanding of their clientele.

Computing technology has advanced so rapidly over the last few years that data analysis that was previously time-consuming or needed expert input can now be generated automatically. Powerful new techniques can now make predictions more consistently and accurately than humans.

Many readers will be familiar with virtual assistants such as Alexa or Siri, which can readily listen to voice instructions and act on them. These applications rely on ‘big data’ techniques which convert speech to text, which is then analysed, ‘understood’ and responded to.

Another less known use is voice analysis in call centres to highlight the emotional state of customers on the phone – presenting this information back to customer service representatives improves the effectiveness of customer interaction.

Within the recruitment industry, algorithms have been developed which can interpret and screen applicants’ CVs. Similar methods are being used to score short exam and test answers – for example, the SAT written test in the USA.

Some businesses use machine learning to automatically score video answers provided by job applicants to measure their job suitability.

These are all applications of readily available machine learning techniques which can process and make sense of unstructured data, for example, speech, video, images and free text. The features of the data are compared to existing data and used to make robust predictions.

 

Predictive analytics and apprenticeships

Predictive analytics (PA) is about helping us make projections about future events through uncovering patterns and relationships (though it cannot necessarily be 100% accurate).

It sounds like traditional statistics, but it isn’t. Computer algorithms can utilise hundreds, even thousands, of data points to find the particular data patterns which predict an outcome.

Unlike traditional statistics, predictive analytics identifies the various multiple profiles or combinations of factors which might lead to, for example, the likelihood that a learner will drop out. Being able to identify these trends and quickly highlight potential causes for concern allows for timely and appropriate intervention – and hopefully, more positive results.

For apprenticeship schemes, training providers and employers need to be able to measure trends in learner engagement and motivation so they can assess the likelihood of a learner remaining in the programme. We can solve this problem with predictive analytics. PA can also provide high-level reporting of retention risks across groups of apprentices.

PA can also be used to support tutors and other team members, such as managing workloads. It can look at patterns in the responsiveness of tutors, how quickly work is assessed and returned to learners and whether particular criteria are difficult to adequately evidence.

For larger schemes, PA could potentially be used to match tutors to learners.

 

Aptem, machine learning and predictive analytics

MWS Technology’s apprenticeship delivery system, Aptem, is built using the Azure platform with built-in machine learning functionality. Our data research team and psychologists, which includes Dr Sarah Owens, have been developing powerful analytics to improve the efficiency of apprenticeship delivery.

With Aptem, managers can generate reports for any scenario. Users are alerted to potential risks with the Early Warning System. Predictive QAR® allows users to see how their QAR scores might develop and intervene in time.

Predictive analytics, applied to apprenticeship schemes, generate rich data. Structured data is captured over a 12-month, or longer, time-frame. And Aptem is an ‘end-to-end’ system, meaning that it manages the apprenticeship programme from start to finish – therefore, all the information that is processed and analysed is in one place. This is important for both assessing the pace and progress of personalised learning and making this information readily available to Ofsted.

Ginni Rometty, chair, president, and CEO of IBM, said of AI that “this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.”

I’d argue that using AI in apprenticeship learning shows how AI can enhance the delivery of the programme and the learning experience, to make sure we deliver the highly-skilled workforce the world needs.

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