“The behaviours required to prosper in a new world are different.” Aptem CEO, Richard Alberg
Although we might not yet be living in what we can call a ‘post-pandemic’ world, the pandemic has already significantly changed the behaviours and skills we need to thrive. Behavioural science can help us to pinpoint and understand these changes, as well as offer useful insights that can be applied to service delivery, and ultimately support people into work.
Behavioural science is a collection of different scientific disciplines that deal with predicting and influencing human actions. Behavioural science includes psychology, economics, political science, and social and cultural anthropology, among others. Using this range of disciplines we can understand key human traits and the nuances that are present in different countries and cultures, to positively influence behaviour.
As reported in New Scientist, behavioural science is a key tool in the fight against the pandemic: “Clearly, the hard biomedical sciences such as virology, epidemiology, immunology and pharmacology matter. But unless we also factor in the science of human behaviour – how real humans in the real world act and think – our understanding is incomplete, and our attempts to defeat the virus will fail.” That goes not just for designing messaging around health protocols that people will pay attention to, but also for rebuilding our economy.
Everything from how to arrange and display information, to the reminders and signals present in employment technology platforms, can be improved and made more effective by applying behavioural science knowledge. And in apprenticeships, while it is imperative that the curriculum is carefully formulated to cover essential subject information, it’s also important that learning journeys are designed in ways that maximise an apprentice’s motivation. Behavioural and educational sciences can be applied hand-in-hand to enable learners to get the most out of their experience, and to ensure the best possible results.
So, which behavioural science insights can we usefully apply to support apprentices on their apprenticeship journey? Here, we examine some typical behaviours and apply them to apprenticeship delivery.
The Cognitive Bias Codex
The Cognitive Bias Codex includes around 180 different heuristics, or thinking traps, that humans are commonly susceptible to. We are naturally selective, and humans have developed automatic processes that enable us to take in information at speed and produce a response. We are flawed, and by identifying some of the ways in which we access and understand information, we can identify ways to positively influence behaviour.
There is a really comprehensive graphic, with definitions, that you can explore if you’d like to see the full set of identified human behaviours. It places cognitive biases into four categories: too much information, not enough meaning, need to act fast and, what should we remember. Obviously, 180 behaviours is too many to meaningfully explore here, and not all are useful in the context of apprenticeship delivery. Instead, we’ve isolated a few that can impact results.
Humans are social animals and often make decisions based on the perceived opinions of others. We’re always seeking to improve our status or avoid embarrassment, and will act accordingly. Narratives around apprenticeships as socially acceptable and desirable routes to employment, alongside stories – case studies or quotes from students that show how similar people have enjoyed and found success following the apprenticeship route – can be beneficial in motivating learners to take up an apprenticeship.
In terms of motivation it’s also useful to consider optimism bias. This causes people to believe they are less likely than others to experience a negative event. This means that shocking people with negative statistics about unemployment, for example, or the likelihood of failure, is unlikely to motivate them into the behaviour that you want to see. This is perhaps most relevant before an apprentice signs up to a programme. Positive factors about the outcomes of an apprenticeship are much more likely to motivate an apprentice than any negative ideas about the impact of not taking that route.
Memory and information retention
When it comes to processing and retaining information, there are some very useful biases that can be applied to improve subject materials.
Something that is bizarre or funny, includes engaging imagery or is somehow related to us will be better remembered. Similarly, something that stands out from a homogenous group will be more memorable. This can inform how information is presented and the types of exercises used in the classroom. Selecting the ‘odd one out’, for example, and using funny or unusual examples to illustrate a learning point, capitalise on known human behaviours to enhance learning and memory.
The generation effect shows that information generated from our own minds is better remembered than information we have simply read. This can be applied to designing the types of activities included in an apprenticeship. Apprentices’ observations and insights from time spent in the workplace can be called upon to answer questions and give responses to theoretical scenarios in the classroom, leading to a higher retention of what has been learnt.
Many of the cognitive biases that can be usefully applied to apprenticeship delivery can be found in the ‘need to act fast’ quarter of the codex. A collection of heuristics in this category show that humans tend to complete things we’ve invested time and energy in. These include unit bias, which is the tendency to want to finish a given task or item; loss aversion, which leads us to act to avoid losing time spent on a task already started; and the sunk cost fallacy, which means once we have invested in something, it is hard to abandon it.
Understanding these patterns of behaviour can help improve apprenticeship programme completion rates. Knowing that apprentices are more likely to complete something they’ve already spent time on highlights the importance of motivating them at the start of a course; towards the end, once time and energy have already been invested, the apprentice is more likely to persevere.
The same behaviours also indicate that progress updates can be helpful. If the learning modules of an apprenticeship are being accessed digitally, including a progress bar that shows students how far they have come, and how many hours they have spent learning already, can encourage them to continue. Alternatively, in a physical environment, teachers can use verbal reminders to let students know which module they’ve reached, how much they’ve already covered, and what remains to come.
These tactics can also support effort justification, a bias which leads people to generally ascribe a greater value to an outcome they’ve invested a lot of effort in achieving. If students can see they’ve put a lot of time and effort into their programme, they will feel even better about succeeding. This could lead to positive reviews and word-of-mouth feedback about an apprenticeship programme, which can boost the reputation of a provider and encourage would-be apprentices to follow that same route. These verbal or visual prompts are simple to incorporate into existing programmes and can have significant impact.
Nudges offer encouragement to make what has been deemed (by policymakers, for example, or educators) the ‘right decision’. That could be anything from snacking on fruit rather than chocolate, (a nudge would be placing healthy options at supermarket checkouts for example), to organ donation (the nudge is that people are asked to opt out, rather than opt in).
Nudges take into account insights from behavioural science, and offer ways to influence behaviour without restricting freedom of choice. They are designed to help humans bypass cognitive biases that lead to negative outcomes and have successfully been applied to a range of policy fields. The many successful applications of nudge theory show that: “Positive reinforcement and indirect suggestions can influence the motives, incentives and decision-making of groups and individuals”. A case study of text messages sent by Jobcentre show that including specific details and personalising communications enhances the likelihood of a positive response. Text messages that included a time, date, relevant job title, named contact and a good-luck message from the job coach resulted in far better outcomes.
The same approach can be applied to apprenticeship delivery. For example, if an apprentice is struggling and is not attaining the required results, a personalised message of support and encouragement from their tutor could provide some much-needed motivation. People are more likely to take action if they believe they have the tools they need to make a change; knowing someone else believes in them is motivating in itself.
Alternatively, the apprentice could be opted in to an additional study or revision session. Letting them know with a personalised message detailing when and where the session will take place, and allowing them to opt out if they prefer, would increase the likelihood that the apprentice attended the session and subsequently improved their results. What’s more, knowing that the expectation was that they attend applies a social pressure which humans are typically susceptible to.
The COM-B model for behaviour change
The COM-B framework offers a model for applying behavioural science insights to create behaviour change. COM stands for capability, opportunity and motivation. The B stands for behaviour. The implication is that capability, opportunity and motivation are three intrinsic factors that need to be addressed to influence, improve and change the behaviour of an individual.
According to The Decision Lab: Capability refers to an individual’s psychological and physical ability to participate in an activity. Opportunity refers to external factors that make a behaviour possible. Lastly, motivation refers to the conscious and unconscious cognitive processes that direct and inspire behaviour.
This model is applicable particularly when seeking to change an existing behaviour. Therefore, it could be usefully applied to the development and delivery of adult apprenticeship programmes. In the context of the pandemic, many people have been furloughed for long periods of time or made redundant as their job function ceases to exist. For these people who may be far into their career, returning to work – whether in the same field or in a new one – will be something of a shock. The world of work has changed, and with it the behaviours and skills needed to thrive.
Long-term unemployment and periods of long-term economic inactivity have been found to have similarly negative effects on people’s confidence and motivation. Behavioural science insights, and the COM-B model, can be applied to support people to return to work. Adult apprentices might be moving to a new field of work or to a new position at the same company. Either way, this can be a daunting process. Similarly, taking on an apprenticeship at school-leaving age can feel like a big step, especially for those who have to forge a career in the mindset of the economic chaos induced by the pandemic. Ensuring that techniques that build confidence in capability, such as regular positive feedback, that motivate, such as demonstrating progress made already, and that reinforce knowledge of the opportunity(ies) available, will enhance the success of all apprentices, no matter what their age or circumstances.
It’s all about the data
The unpredictability of behavioural science means that it is essential to rigorously test all applied interventions. Demographic differences, such as age, gender, culture and context (the person’s own situation and level of motivation, for example) will alter the way that person responds to particular interventions and nudges. To successfully apply behavioural science, data is needed.
Dr Darren Coppin recently joined Aptem CEO Richard Alberg to discuss the role of behavioural science during the pandemic. The webinar (which can be viewed here) gave an overview of behavioural science and explored how it can play a part in post-pandemic recovery.
It’s Dr Coppin’s opinion that, in the future, data science and behavioural science will need to merge. And to underline the importance of measuring outcomes, he gave several examples where the effect of interventions had been strikingly different to the intended effect. One such example was the introduction in 2008 of calorie counts at fast-food outlets in an attempt to nudge people towards healthier choices, while also placating policymakers. The practice was introduced in upper New York State and then spread throughout the world, despite the fact that it had the exact opposite of the intended effect. People actually ate more, taking an ‘in for a penny, in for a pound’ approach to calorie consumption.
This example, and the others Dr Coppin shares, are a useful reminder that when considering how to apply the insights we’ve explored here, it’s essential to also consider what the measures of success will be, and to put in place from the get-go systems that will track and collect the necessary insights to reveal whether your interventions are having the desired effect.