Heini Utenen OpenWHO confusion about methods and learner preferences

Why asking learners what they want is a recipe for confusion

Reda SadkiGlobal health, Theory

A survey of learners on a large, authoritative global health learning platform has me pondering once again the perils of relying too heavily on learner preferences when designing educational experiences.

One survey question intended to ask learners for their preferred learning method.

The list of options provided includes a range of items.

(Some would make the point that the list conflates learning resources and learning methods, but let us leave that aside for now.)

Respondents’ top choices (source) were videos, slides, and downloadable documents.

At first glance, this seems perfectly reasonable.

After all, should we not give learners what they want?

As it happens, the main resources offered by this platform are videos, slides, and other downloadable documents.

(If we asked learners who participate in our peer learning programmes for their preference, they would likely say that they prefer… peer learning.)

Beyond this availability bias, there is a more significant problem with this approach: learner preferences often have little correlation with actual learning outcomes.

And learners are especially bad at self-evaluating what learning methods and resources are most conducive to effective learning.

The scientific literature is quite clear on this point.

Bjork’s 2013 article on self-regulated learning emphatically states that: “learners are often prone to illusions of competence during learning, and these illusions can be remarkably compelling.”

The study by Deslauriers et al. (2019) provides a compelling demonstration that while students express a strong preference for traditional lectures over active learning methods, they actually learn significantly more from the active approaches they claim to dislike.

This disconnect between preference and efficacy is not surprising when we consider how learning actually works.

Effective learning requires effort, struggle, and sometimes discomfort as we grapple with new ideas and challenge our existing mental models.

It is not always an enjoyable process in the moment, even if the long-term results are deeply rewarding.

Furthermore, learners (like all of us) are subject to various cognitive biases that can lead them astray when evaluating their own learning.

The illusion of explanatory depth, for example, can cause us to overestimate how well we understand a topic after passively consuming information about it.

None of this is to say we should ignore learner perspectives entirely.

Motivation and engagement do matter for learning.

But we need to be thoughtful about how we solicit and interpret learner feedback.

Asking about preferences for specific content formats (videos, slides, etc.) tells us very little about the actual learning activities and cognitive processes involved.

A more productive approach might be to focus on understanding learners’ goals, challenges, and contexts.

What are they trying to achieve?

What obstacles do they face?

What constraints shape their learning environment?

With this information, we can design evidence-based learning experiences that truly meet their needs – even if they don’t always match their stated preferences.

As learning professionals, our job is not to give learners what they think they want.

It is to create the conditions for transformative learning experiences that expand their capabilities and perspectives.

This often means pushing learners out of their comfort zones and challenging their assumptions about how learning should look and feel.

Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417-444. https://doi.org/10.1146/annurev-psych-113011-143823

Deslauriers, L., McCarty, L.S., Miller, K., Callaghan, K., Kestin, G., 2019. Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences 201821936. https://doi.org/10.1073/pnas.1821936116