Exercise 3: Discovering How Real Users Would Group 40 Content Topics — Possible Solution
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Card sorting is the method built specifically for this situation - it
directly asks real users to group real content topics in a way that
makes sense to them (an open card sort), rather than sorting content
into categories the design team already invented. Given the team
wants to learn how users would naturally group the 40 topics before
any navigation menu exists yet, an open card sort is the right choice,
since a closed card sort assumes the categories are already decided
and only tests whether items fit them.

This is more reliable than the team guessing themselves because the
team's own mental model of the content is not the same as an actual
user's - the team already knows the content intimately and organizes
it accordingly, while a real user encountering it for the first time
may group related items very differently, or expect a completely
different set of top-level categories altogether. Card sorting
produces real evidence of how actual users think, the same underlying
principle the chapter's own research chapter (Chapter 2) already
established for interviews and personas - grounding design decisions
in real data, not internal assumption.

ANSWER: Open card sorting fits this need - it asks real users to group
the 40 topics themselves, revealing how they naturally organize the
content before any categories are decided. It's more reliable than the
team guessing because the team's own familiarity with the content
means their mental model doesn't match a first-time user's, the same
reason Chapter 2 emphasized grounding personas in real research rather
than internal assumption.

WHY THIS WORKS AS AN ANSWER
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This correctly identifies open card sorting (not closed, since no
categories exist yet) as the right method for this specific situation,
and explains why real user data beats internal assumption, tying back
to the course's own established research-over-assumption theme from
Chapter 2.