Using Data to Identify and Address Barriers to Learning

Who is missing? Using inclusion data to identify barriers to learning.

Creating genuinely inclusive education requires more than policies and good intentions. Further education providers need to use learner data to identify barriers to learning, understand where inequalities exist and measure whether their inclusion strategies are improving learner experiences and outcomes.

Recent conversations with members, working groups and delegates at our inclusion events suggest that the sector is embracing the importance of inclusion like never before. Providers increasingly recognise that meeting evolving expectations requires more than policies and good intentions; it requires a clear, evidence-informed strategy that demonstrates how inclusion is embedded across the organisation and, most importantly, the impact it is having on learners.

But once these strategies have been implemented, how do we know how successful they have been? The impact is not always instant and can come from within, they can be seen as changes in attitude, feelings of belonging, better mental health. The symptoms of poorly inclusive provision diminishing as inclusive culture evolves. But how do we capture this in data, and then
what?

Knowing what questions to ask, what data to collect, when it feels like there are a million different things we could track, can feel overwhelming. Knowing how many learners have support plans or how many staff attended inclusion training is just telling us what activities we have undertaken. Impact needs to look at learner engagement, achievement gaps, learner independence, and the ultimate test of inclusion, do our learners feel like they belong? Do they have the psychological safety to speak up when they are struggling?

We are all familiar with Assess, Plan, Do, Review as a way of supporting individual learners as part of the SEND code of practice. But what happens when we apply the same principles to our whole organisation level inclusion strategy?

By applying the Assess, Plan, Do, Review framework to our whole level provision of inclusion we can create a quality improvement cycle that really works to address the evolving needs of our learners on a cohort basis. Alongside individual reviews this can lead to enormously impactful and measurable improvements.

Assess – This starts with knowing our current context, who do we have in front of us now? Then we can understand their biggest barriers. Asking open questions to capture as much information as possible that could indicate a barrier to learning and recording in a way that we can track these learners as they progress, or don’t, through our courses. Think about the biggest barriers to learning affecting your learners and start by tracking factors associated with these. As part of our assessment, we also want to understand our current gaps…you want data that tells you who is presented, who isn’t? Who achieves, who doesn’t? Who is engaged, who isn’t? To identify the biggest gaps in those who are included and those who aren’t.

Plan – Here you are designing more inclusively by knowing your context. What will you put in place to remove or reduce the impact of the barriers you identified? Leaders, the key step missed here, talk to those on the front line, the ones who know the learners the best, the people who see their struggles and get to know their back stories. Talk to your delivery team, take on
board their concerns and suggestions and build their consensus for change.

Do – Progress takes time and consistency. Are teaching teams consistently embedding inclusive approaches?

Review – Has our new approach improved attendance, engagement, achievement and belonging for different groups of learners? We should use both qualitative and quantitative data to answer this. A well-designed questionnaire, alongside attendance and attainment data, can tell us a great deal, but the real value comes from capturing the voices of those directly affected: our learners and staff. This evidence should then inform what we do next.

Using learner data to identify barriers to learning

When we drill down into our quantitative data, we should see data that for example, indicates; improved attendance, reduced achievement gaps and increased engagement with online resources. But there are more questions we can ask of our data to really identify the why. Are there patterns when we differentiate by course, employer, teaching team? Are there any
similarities in the learners with patterns of persistent absence that would help you identify why? Who is completing, and who isn’t? Your headline data may say, our attainment is high, but are your learners with barriers disproportionately on the wrong side of this data?

Our qualitative data is just as important, if not more. Focus groups, survey responses, reviews, and learner feedback all show us how the changes have affected the actual people, not just the numbers. Do our learners feel safe, valued, and like they belong? Do they feel their strengths are recognised, not just their challenges?

When we zoom out, we can start looking at who is accessing the provision in the first place. Our access and participation data can give us clues where we need to improve. For example, who is applying and who isn’t applying for these opportunities? Do we need to assess our recruitment process to ensure we have equitable representation from the start? Once they are on board, who is accessing enrichment, progression, promotions?

Ultimately, the purpose of collecting inclusion data is not to produce more reports or satisfy external scrutiny. It is to understand whether the experiences we are creating for learners match the intentions behind our strategies.

The most inclusive providers are not those with the longest list of initiatives, but those that continually use evidence to identify barriers, measure impact and refine their approach. Inclusion is not a destination; it is a continuous cycle of listening, learning and improving.

If we are truly inclusive, our data should tell a positive story. We should see more learners participating, more learners progressing, smaller gaps in outcomes and, perhaps most importantly, more learners telling us they feel safe, valued and that they belong. What story is your data telling?