Academic Cognitive Engineering SYDE 642

Google Maps Interface Navigation and Cognitive Mapping

A research-driven set of recommendations designed to reduce navigation errors by addressing interface clutter, ambiguous controls, and hidden actions that disrupt novice users' mental models.

Overview

This academic project examined Google Maps as a complex interactive system, focusing on how novice users interpret navigation options, build mental models, and recover from confusion while completing common route-planning tasks. Rather than evaluating the interface only at the surface level, I approached it through a cognitive engineering lens to understand how the design supported or disrupted attention, decision-making, and spatial reasoning in real use.

The goal was to identify where the interface created unnecessary friction and translate those findings into a clear set of UX priorities.

Problem Statement

Google Maps is powerful, but its depth and feature density can make navigation harder for novice users, especially when important actions are hidden, icons are unclear, or system feedback does not align with user expectations. These breakdowns can slow task completion, increase backtracking, and create uncertainty during route planning.

The challenge was to understand not just what users struggled with, but how expertise shaped performance and what that revealed about the interface itself.

Key Challenges

Distinguishing between issues caused by user inexperience and issues caused by the interface design itself
Translating detailed cognitive observations into practical UX recommendations that could improve clarity without oversimplifying a feature-rich system

Research Process

Step 01

Contextual inquiry during realistic navigation tasks

I began by observing how users interacted with Google Maps while completing realistic navigation and route-planning tasks. Through contextual inquiry, I focused on where users hesitated, how they interpreted controls, and what they expected to happen at each step of the interaction.

Step 02

Cognitive task analysis to uncover why errors occurred

I used cognitive task analysis to break down the mental steps involved in navigation decisions, especially in moments where novice users became uncertain or made errors. This helped uncover not only where tasks slowed down, but why. In many cases, the issue was not lack of effort, but mismatches between the interface and the user's mental model.

Step 03

Comparative observation across expertise levels

A key part of the analysis involved comparing novice behavior with more fluent interaction patterns to understand how expertise compensated for interface weaknesses. This made it possible to distinguish between features that were merely unfamiliar and design choices that consistently created ambiguity, hidden actions, or recovery problems.

Step 04

Synthesis into a prioritized UX roadmap

I synthesized the findings into a prioritized set of UX recommendations focused on high-impact issues affecting navigation clarity: ambiguous icons, inconsistent back affordances, and hidden route options that interrupted flow and increased user error. Rather than proposing broad redesigns, the recommendations targeted the specific moments where cognitive load became unmanageable.

Impact

UX Roadmap

Translated expertise gaps into a clear UX roadmap for improving navigation clarity in a high-complexity interface, demonstrating how cognitive engineering methods surface problems that surface-level usability review misses.

Academic Recognition

Received 100/100 in SYDE 642 (Cognitive Engineering Methods) for the strength of both the analysis and the practical relevance of the recommendations.