The ‘Find a’ service at the start of alpha

Building on discovery

For young people considering their ongoing educational options, there’s a fragmented and confusing digital landscape. This includes provider websites, charities, government support from the National Careers Service and individual “find a” services (like Find a T Level and Find an apprenticeship).

The aim of this project is to provide a single trusted starting point for young people that allows them to focus on finding suitable courses rather than navigating a confusing user journey across multiple services and websites.

The discovery provided a range of insights about our users. The scope was broad - covering both young people and adults. For alpha, we decided to focus on more vulnerable users aged 15 to 18. These users are more likely to be NEET (not in education, employment, or training).

By designing for those users with the most additional needs, and with the most to gain from our service, we felt we’d be most likely to meet the needs of all our users.

Data science approach

Historically, there have been challenges for Department for Education (DfE) “Find a” type services, as they’ve often been created based on policy rather than user needs. There’s also a reliance on providers themselves uploading information to DfE systems.

We could help overcome this problem of incomplete datasets by scraping data directly from provider websites. This would enable us to provide a fuller picture of the courses available to users. We could also potentially include additional information that we wouldn’t otherwise be able to, such as travel times, open days and additional support offers.

An illustration of the problem that the data science approach is trying to address.

We also wanted to explore whether we could use AI to enable a user to search semantically rather than by specific keywords. For example, if someone searched for “designing buildings” the search would return courses relating to architecture even if the course description did not include this particular wording.

These possibilities were explored in a technical proof of concept by our data science colleagues. The findings were extremely encouraging, which gave us the confidence that AI could add significant value for users and make our service stand out from others in this space.

User research

The discovery included 2 surveys to help learn more about topics like:

• how our users felt about AI
• how they approached making educational choices at 16 and at 18
• what was important to them when using online services
• how our users felt about switching courses if they realised they’d made the wrong choice

There was also one in person UR session which tested some very early wireframe concepts. Whilst not with our exact target user group, this testing did help us learn more about young people in general and helped us to further think through our design ideas.

This early research identified 3 broad mindsets related to young people’s information-seeking behaviours, which we want to explore further in alpha. These are:

• explorer – young people who actively seek out opportunities
• direct – these young people have a clear vision of their career goals and how to achieve them
• un-engaged – these people feel disconnected and disinterested, perhaps due to other challenges they have in life (there is an interesting question about whether we can ever reach these users through a solely digital service)

We also completed desk research on best practice design for young people. This reinforced research from other DfE projects that found that teenagers are more likely to be “app-savvy” (that is, they have expertise on using specific apps or websites) rather than being generally “tech-savvy” (which is often the perceptions of teens).

We identified how useful codesign could be – that is, designing directly with young people themselves. While we have not yet been able to carry out any codesign, we hope to include this in the future.

Risky assumptions

As a team, we identified our riskiest assumptions so we could explore them through alpha to make better informed design decisions. We found 4 overarching themes, which were:

• adding value in the landscape – this is around our service adding more value to users than what’s in the existing digital landscape
• prioritising our users and meeting their needs – we expect our primary users for beta will be 15 and 16 year olds with additional needs thinking about their post-GCSE options; designing a service for these users could be different to designing for adults and it’s likely that a digital service by itself will meet all their needs
• making better choices – this is around supporting decision-making in a non-partisan way
• impacting behaviour – this relates to whether once they’ve found our service young people will trust it, see its value and want to use it

We will test these assumptions as rigorously as we can throughout alpha.

Problem statement

Having narrowed down our user group and identified our assumptions to test, we used a lean UX approach to look at the:

• current state of service landscape
• current focus of existing services
• gaps existing services fail to address
• initial focus area for our proposed service

After sharing ideas across the team, iterating and refining, we agreed upon a finalised problem statement:

Young people face significant choices about education and training around the ages of 16 and 18. They are not always getting the support they need to make these choices, though, due to:

• inconsistent skills and careers support
• inconsistent skills and careers support
• fragmented, difficult to access and poorly tailored information about options
• historic governmental silos

For individuals, these might contribute to confusion and anxiety, limited awareness of opportunities, dropping out or changing course (churn) and lower long-term income. For the government, the economic impact is around duplicated service delivery and inefficient use of funds.

There is currently no centralised, unbiased and user-centred approach to providing comprehensive help for young people to explore and navigate their options.

There is an opportunity to create a single service that:

• empowers young people to explore all their education and training options
• helps those facing additional barriers
• assists parents, teachers and careers advisors to support these decisions
• maintains relevance regardless of the direction of future policy and organisational delivery

Ideation

Following on from agreeing our problem statement as a team and identifying our riskiest assumptions, we held an ideation session to help generate ideas to take forward for further user testing. We based the session on the assumptions that we identified, looking at:

• what would we need to know to prove or disprove each assumption
• how could we test these in the form of wireframes
• what design ideas could help address those questions

After working through the assumptions list, we deduplicated ideas and came up with a wireframe shortlist. we initially prioritised 2 ideas:

1. A search tool aimed at someone of a direct mindset who knew what they wanted to do.
2. A guided journey, where we’d present questions in a different way to help more exploratory users narrow down their options.

Wireframing

We took our top ideas and thought about what tasks are users would need to complete at each stage. This helped us come up with a rough progression of screens for each option. Once we had a rough outline of screens we took a content first approach – thinking about what we wanted to convey on each screen. We focussed on using a reassuring tone and trauma-informed language to reduce the risk of alienating our target users.

We then started to build out the screens into wireframes. We shared ideas with the data science team to get an early view of technical feasibility. We also held a design crit with the wider DfE design community and incorporated their feedback into our designs, including how we could add a browse option to aid more exploratory mindsets.

We are currently building out these wireframes into our first interactive prototypes.

The first homepage design for the new service.

Planning for the rest of alpha

As a relatively short alpha, we want to make the best possible use of our time. We will therefore be as agile as possible, regularly reviewing our progress as we move towards an alpha service assessment and changing our plans if we need to.

Throughout alpha we will:

• document our design decisions (with posts like this one) as we go
• keep in touch with other DfE teams to share and learn collaboratively
• use hypothesis-based design to learn from UR findings, iterate and re-test
• design in the open, with regular crits both without and outside of the delivery team
• use as wide a range of approaches to understand and engage with our users as we can, including qualitative and quantitative approaches, wireframes at different levels of fidelity and potentially usability testing with more advanced prototypes
• keep our users at the forefront of our minds and be aware of their needs, challenges and opportunities as young people
• document anywhere these needs take us away from GDS design principles or components

We look forward to publishing further design history posts over the coming weeks.