Learner Variability is the Norm

Just as your teaching values shape your design, so does your understanding of your learners. Learner variability is the norm, not the exception. Research shows that differences in knowledge, skills, interests, identities, and contexts systematically shape how students engage, persist, and succeed (Meyer, Rose, & Gordon, 2026). This means variability is not random—it's predictable enough to design for.

At the University of Toronto, institutional survey data reinforce this reality. Recent findings from the National Survey of Student Engagement (NSSE 2023) show that our students represent a wide range of identities, backgrounds, and experiences. For example, 25% of students identify as 2SLGBTQ+, nearly one in five identify as Trans, Nonbinary, or Gender Diverse, and 24% are international students. The majority live off campus, often balancing commuting, work, or caregiving responsibilities. Students also report frequent engagement with peers from diverse racial, economic, and political backgrounds. There is no single "typical" U of T student.

Importantly, variability is not a problem to be solved. It is a natural and positive fact of learning—every student brings unique strengths and perspectives. Barriers arise when course design assumes an "average learner" and fails to account for this diversity. For example, a lecture delivered only orally may disadvantage students who learn better visually, students who are Deaf or hard of hearing, or students processing information in different ways. Designing to the edges of variability ensures that more students can participate and thrive. This includes visible and invisible disabilities, which are part of the natural variability present in every learning context.

Kat Holmes's "The Myth of 'Average' and 'Normal'" video (4:16, select "CC" for captions) illustrates this point: designing for an "average" student often excludes most learners.

What Shapes Learner Variability?

Students come with a mix of characteristics that influence how they learn. These include:

Prior knowledge and skills: Academic preparation, disciplinary background
Motivation and interest: Curiosity, career goals, personal relevance
Cultural and social identities: Language, race, Indigeneity, gender, socio-economic background
Disability and neurodivergence: Visible and invisible disabilities, chronic health conditions, sensory differences, and neurodivergent ways of processing
Contextual factors: Work, caregiving responsibilities, commuting, technology access
Cognitive and affective profiles: Strengths in memory, attention, self-regulation, emotional resilience

No single learner is "average" across these dimensions. Instead, students present jagged learning profiles—uneven strengths and challenges across domains (Rose, 2015). You might imagine each learner as a landscape with its own unique peaks, valleys, and history. By learning how to read the landscape, you can begin to anticipate where barriers might appear and design more flexible learning experiences. Designing for this variability is what makes UDL powerful.

Image Caption: Learner variability can be imagined as a jagged profile of strengths and challenges across different domains—no two learners have the same pattern.

Pause for Planning: Identify Learner Variability in Your Context

Choose one course or learning context to focus on for this activity. In your U Design Learning Action Planner docx, respond to the prompts below:

Who are your students? (e.g., undergraduates, graduate students, professional learners)
What variability do you anticipate in their backgrounds, skills, and experiences?
What contextual factors (work, family, access to resources) might shape their learning?
Where might students encounter barriers in your current course design?
Where might you be designing for a "mythical average" rather than to the edges of variability?

Summarize 3–4 key dimensions of learner variability that are most relevant in your teaching context.

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How Will You Get to Know Your Students?

Understanding variability in the abstract is helpful, but UDL works best when it is grounded in your actual learners. Research emphasizes that no student arrives as a blank slate—each brings experiences, strengths, challenges, and identities that shape how they engage with learning (e.g., Merry, 2024; Artze-Vega, 2023; Addy et al., 2021; Hogan, 2018). When we assume an "average student," we risk designing for no one.

Some strategies for getting to know your students include:

Start-of-term surveys or info sheets: Short, open-response surveys can capture important details often missed in official rosters (e.g., pronouns, Wi-Fi access, caregiving responsibilities, goals). Tools like "Who's in Class?" forms (Artze-Vega, 2023; Addy et al., 2021) make variability visible.
Values reflections: Prompts such as "This I Believe" essays help students articulate their values and experiences, deepening trust and belonging (Artze-Vega, 2023).
Early diagnostic activities: Simple assessments of prior knowledge, skills, or confidence can uncover hidden strengths and anxieties (Merry, 2024).
Classroom interactions: Icebreakers, "ask six questions" activities, or discussion boards let students share who they are and what motivates them.
Informal check-ins: Minute papers, anonymous polls, or mid-term feedback provide real-time insights.
Learning analytics: Quercus dashboards can highlight patterns of engagement with course resources.
Monitoring attitudes and needs: Merry (2024) suggests gathering information in four broad categories:
- Learning attitudes and mindsets (motivation, confidence, ambitions)
- Interests and learning needs (preferred tasks, curiosity, assessment preferences)
- Social and emotional learning (comfort with collaboration, sources of anxiety)
- Cultural relevance and perspectives (representation in curriculum, identity, belonging)

The goal is not to capture everything about every student, but to notice patterns that can guide your design choices. UDL practice is iterative: check in, adapt, and refine as student needs evolve.

Before the course begins, instructors may also have access to institutional information that can inform proactive design. For example, accommodation letters from Accessibility Services or general information from the class list may prompt reflection on potential barriers in your design. While UDL encourages designing for variability from the outset rather than reacting to individual cases, these early indicators can prompt reflection on potential barriers and help ensure that essential course elements are accessible to as many learners as possible.

Sample Getting-to-Know-You Questions

Below are a few sample questions you can adapt for a first-day survey or reflection. For more examples, refer to Merry (2024), Artze-Vega (2023), Addy et al. (2021), and Hogan (2018).

What is one thing you're excited about in this course?
What is one challenge you anticipate?
How do you prefer to contribute in class (speaking, writing, group work, online, other)?
What commitments outside of class (work, family, commuting) might affect your learning?
What is one value or interest that shapes how you approach learning?
Is there anything else you would like me to know that could support your learning in this course?

Tip: Keep surveys and reflections short (5–7 questions is plenty). Students are more likely to respond thoughtfully when the activity feels manageable.

Caution: Don't ask questions you aren't prepared to act on. Gathering information without following up can undermine trust. Even a brief acknowledgement ("Here's what I heard from you, and here's how I'll use it") shows students their input matters.

Pause for Planning: Get to Know Your Students

In your U Design Learning Action Planner docx, identify 1–2 strategies you will use to learn more about your students at the start of the term. Examples include:

Embedding a short anonymous "Who's in Class?" survey in Quercus
Assigning a low-stakes reflection (e.g., "This I Believe" or a learning goals statement)
Using an icebreaker or "ask six questions" activity in Week 1
Adding an early diagnostic quiz or self-assessment of prior knowledge

Record the strategy (or strategies) you will use, why you chose them, and how you will use the insights to inform your course design.

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Looking Ahead

By mapping your learners' variability, you set the stage for intentional design choices that reduce barriers and welcome more students into meaningful learning.

In the next step, you'll identify a pinch point—a specific place in your course where learner variability and barriers are most visible. That's where your focused UDL work begins.

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University of Toronto, Centre for Teaching Support & Innovation