Apprenticeship providers are rightly interested in tracking learner progress. But what kind of data and tools do management need to get the facts rather than a well-meaning fiction?

Brian G. Burns is host of the Brutal Truth About Sales podcast. I follow him on LinkedIn because he always takes a refreshing look behind the sales and marketing spin.

In one of his short clips on LinkedIn, he talks about metrics and how management teams, if they are not careful, get the metrics they want to see rather than the actual facts. As Brian puts it:

“The staff you have become actors, people acting like they’re doing what you want them to do. And if you hire actors, it should be no surprise that the audience is you, the management team.”

What he means by this is that staff work to the metric. What managers often think is the metric for progress and achievement usually isn’t. This is true not just for sales but for any role or function. Any business or organisation needs to think about what kinds of metrics lead to actual success or results.

Tracking learner progress

In the apprenticeship industry, income depends in no small degree on learner progress. If learners don’t progress or if they drop out, payment is not forthcoming. So understandably there is a lot of interest in learner progress data. Will the learner get to gateway on time? Are they on track?

It is relatively commonplace to have a manual system or software solution where the trainer rates their learners. Who else in the organisation would have a fine-tuned sense of how the learner is doing?  However, It is human nature not to want to deliver bad news. If you are responsible for putting the information into the metric or populating a RAG rating, then you may certainly not want to deliver the brutal truth because of the consequences this would bring.

No-one wants to deliver bad news to their employer. It has consequences such as increased pressure on the learner or fears of a performance review for the trainer. I suspect it would be a brave – and soon to be unemployed – trainer that would report that a sizeable proportion of their learners are red or behind schedule, meaning that they would not get to gateway on time. That trainer would likely record many of them as amber, mainly out of self-preservation. So, management is essentially getting a well-meaning fiction.

Getting the right data

One way of dealing with this problem is implementing a different way of obtaining the learner data for the metric. One that reduces administration for the trainer and improves accuracy.

The solution is simple and already enjoyed by Aptem users. Aptem not only manages the learner journey, but it also uses non-subjective machine learning, sentiment analysis and automation at the core of its functionality. Machine learning means the use of AI to learn, without being specifically programmed, what metrics are important. Sentiment analysis can analyse communications and identify any language associated with struggling learners. Aptem will perform these tasks automatically. It means that Aptem is collecting learner data, behind the scenes, based on the interactions the apprentice is having with the system.

With these inbuilt features, Aptem can not only report on current learner progress data but can forecast upcoming patterns using its predictive QAR®. So, tutors and management will get alerts if there are any signs of falling behind. Plus, accessing this learner data is easy with Aptem’s integrated Power BI dashboards.

Trainers can, of course, use these features to highlight any problems early. This means they can offer pastoral support. Management can also see real-time data, enabling them to spot problems early and take steps to intervene. And if issues are spotted early enough, then difficulties with a particular cohort are less likely to be seen as a trainer problem. This occurs because the problems identified have not yet impacted on the business or organisation. It’s a win-win for everyone.

If you are interested in seeing how Aptem can help you get the data you need, get in touch.