NICKI WISE, PHD STUDENT, UNIVERSITY OF HULL, UK; HEAD OF DIGITAL LEARNING, GEORGE HERIOT’S SCHOOL, EDINBURGH, UK
Across the UK, the number of students identified with additional needs continues to rise, with policy frameworks emphasising inclusion for all. While information about these needs is often fragmented or absent altogether, support structures frequently remain tied to formal diagnosis, meaning that some students can be overlooked. Research also suggests that categorising students through labels can negatively influence teachers’ efficacy beliefs (Gibbs and Elliot, 2015) and expectations of learners (Kashikar et al., 2023), reinforcing group-based assumptions that can limit what is seen as possible for individual students (Florian, 2015).
This article draws on a design-based research (DBR) doctoral study conducted in a Scottish secondary school. It explored how executive function (EF) profiles could be used to support teachers in responding to learner variability in mainstream classrooms.
Everyone has an executive function profile
All students (and teachers) draw on EF processes to engage with and manage learning (Guare, 2014). EF is often attributed to a set of cognitive processes that enable us to manage tasks and work towards goals, with three core components underpinning this: working memory, inhibitory control and cognitive flexibility. Working memory involves holding and manipulating information, inhibitory control supports attention and emotional regulation, and cognitive flexibility enables us to shift between tasks, ideas and expectations.
EF overlaps with cognitive load but extends beyond it, offering a more holistic view of how learners manage demands. This is particularly relevant in secondary settings, where students are expected to move constantly between subjects, tasks and expectations. Research suggests that EF is closely linked to academic success, yet it often remains implicit in classroom practice (Pascual et al., 2019; Guare, 2014).
Making executive function visible
To make these processes visible, I designed and iteratively refined a set of screening tools to generate individual and class-level EF profiles. Drawing on existing validated measures alongside a novel ranking tool, these were piloted, adapted and tested at scale to produce accessible summaries of patterns across core EF components. For students with ASN (additional support needs), the ranking tool also captured explanations, allowing student voice to be included in the results.
The focus was not on diagnosis, but on assessing EF in authentic classroom contexts. Obradović et al. (2017) demonstrate that gathering EF data in relation to everyday classroom activity strengthens validity, grounding assessment in how EF is enacted in practice.
Through two-year iterative cycles of classroom-based work, these representations were refined into formats that teachers could interpret and use in collaborative discussion and planning.
What happened when EF became visible?
Interpreting difficulty
The introduction of EF profiles at individual, class and cohort levels disrupted teachers’ assumptions about learner difficulty. For example, students identified with ADHD did not consistently present with weaker inhibitory control (attention), but often experienced difficulty with tasks linked to working memory and cognitive flexibility. What had previously been interpreted as inattention was, in many cases, better understood as difficulty in managing multiple EF demands. When these patterns were considered alongside classroom activity, teachers began to recognise that the task design was contributing to difficulty.
Seeing tasks differently
This became evident in a geography lesson, where a teacher designed an interactive starter requiring students to stand or sit depending on whether statements about weather were correct. Initially conceived as an engaging way in which to assess student knowledge, reflection through an EF lens revealed a far more complex set of demands.
The teacher noted: ‘There’s so much going on here – they have to work out the definition, relate that to “stand” or “sit”, and also be aware of what others in the class are doing.’ What appeared straightforward actually required students to hold and process information, inhibit responses and monitor others simultaneously. Retrospective analysis of EF data showed that those who appeared most uncertain had lower working memory scores, suggesting that the difficulty lay not in understanding, but in managing cognitive demand.
Seeing beyond the tool
A similar pattern emerged when digital technology was used, often positioned as a ‘leveller’ for learners with additional needs. When viewed through an EF lens, however, commonly used accessibility tools were found to introduce additional cognitive demand.
For example, speech-to-text was initially assumed to reduce barriers for students with writing difficulties. In practice, it required students to hold sentence structures in mind, monitor output and switch between composing, editing and using the technology. This placed substantial demands on working memory and cognitive flexibility, particularly for those already experiencing processing difficulties.
As Sweller (2020) argues, the element of interactivity involved in learning tasks must be carefully considered; when digital tools introduce additional steps, decisions and modes of processing, they can increase cognitive load rather than reduce it.
Teachers began to rethink accessibility tools, experimenting with ways to externalise aspects of EF demand – for example, through visual planning, multimodal scaffolds and ensuring instructions remained visible. These approaches helped to reduce unnecessary cognitive load and enabled students to engage more effectively with learning.
From reflection to anticipation
As teachers became more familiar with EF concepts in practice, the focus shifted from reflection to anticipation. A history teacher redesigned a note-taking task in which students watched a video and extracted key information. Rather than assuming that this was a straightforward activity, they identified the EF demands, consulted class-level data and anticipated potential pressure points.
As a result, they modelled how to combine visual and written notes, paused the video to reduce processing load and incorporated structured peer discussion and note-sharing to support consolidation. These adjustments did not change the purpose or intended outcomes of the task, but they altered how students were supported to engage with it, resulting in higher-quality and more consistent outcomes across the class.
From interpretation to design
With EF made visible, teachers began to shift their focus. Rather than asking ‘What does this student need?’, they started to ask, ‘What is this task demanding?’. This subtle change reframed planning around cognitive demand rather than individual deficit.
Over time, this led to a move away from reactive support towards anticipatory design, as shown in Table 1.
Table 1: Moving from reactive support towards anticipatory design
| Before | After |
| Focus on learner | Focus on task and embedded EF demands |
| Labels/behaviour | Cognitive demand |
| Individual support | Whole-class design |
| Differentiation | Anticipatory planning using EF data |
What this means for practice
Rather than positioning inclusion as differentiation, it became better understood as a question of design, challenging assumptions rooted in ‘bell curve’ thinking, where what is ordinarily provided is expected to meet the needs of most, with additional provision for some (Florian, 2015).
Instead of creating multiple versions of tasks, teachers adjusted how the main activity was structured and supported for the whole class. Inclusion became less about individual accommodation and more about designing learning experiences that accounted for cognitive variability from the outset, addressing individual difficulty through a consideration of ‘everybody’ (Spratt and Florian, 2015).
Output of the research
DBR produces both practical tools and theoretical insights that can be adapted across contexts (McKenney and Reeves, 2019). In this study, this included the development of EF screening tools to generate usable profiles and a framework to support teachers in translating EF data into classroom practice. While outlining these in full is beyond the scope of this article, selected principles are shared below to illustrate how EF visibility can inform inclusive design:
- Make EF demand visible: Identify where tasks place pressure on working memory, inhibitory control or cognitive flexibility, rather than focusing only on skills such as planning
- Design for the whole class: Use this information to adjust how tasks are structured, rather than relying on individual adaptations
- Externalise key information: Reduce reliance on students holding multiple pieces of information in mind by making core concepts accessible
- Reduce unnecessary task-switching: Be mindful of how often students are required to shift between concepts and processes, particularly when using digital tools.
Conclusion
EF provides a holistic lens on learning, capturing not only cognitive processes but also how students regulate, respond to and engage with classroom demands. When these typically hidden aspects of learning are made explicit, teachers are better able to interpret difficulty and respond in more purposeful ways. In this sense, inclusion is not about doing more but about seeing learning more clearly.
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