Eye Tracking the User Experience: From Design to Analysis Course

Lesson 1 • History of Eye Tracking Research

Traces eye tracking from early ophthalmology to modern UX labs. Contextualises current tools within a century of methodological evolution.

Lesson 2 • How the Human Eye Works

Covers ocular anatomy, the fovea, and peripheral vision limits. Establishes the biological basis for interpreting gaze data accurately.

Lesson 3 • Types of Eye Movements

Distinguishes saccades, fixations, smooth pursuit, and microsaccades. Each movement type carries distinct UX implications covered in later chapters.

Lesson 1 • Core Tracking Technologies Explained

Explains pupil-corneal reflection, dark-pupil, and bright-pupil methods. Grounds hardware selection decisions in the physics of each approach.

Lesson 2 • Remote Desktop Eye Trackers

Covers screen-mounted and monitor-integrated trackers used in lab settings. Establishes the baseline hardware context for most UX studies.

Lesson 3 • Webcam-Based and Remote Tracking

Introduces browser-based and webcam eye tracking for large-scale remote studies. Addresses accuracy limitations and appropriate use cases.

Lesson 4 • Mobile and Wearable Eye Trackers

Examines glasses-based and head-mounted trackers for real-world studies. Prepares students for field research beyond the controlled lab environment.

Lesson 1 • Participant Recruitment and Sampling

Guides sample size estimation, inclusion criteria, and diversity considerations. Ensures findings are generalisable to the intended user population.

Lesson 2 • Stimulus Preparation and Presentation

Addresses screen resolution, refresh rate, and stimulus timing for reliable gaze capture. Prevents common confounds introduced by poor stimulus setup.

Lesson 3 • Ethics and Informed Consent

Covers data privacy, consent procedures, and participant rights in gaze research. Prepares students to meet institutional and professional ethical standards.

Lesson 4 • Defining Research Questions and Hypotheses

Translates UX goals into testable gaze-based hypotheses. Ensures every design decision is anchored to a clear research question.

Lesson 1 • Participant Onboarding and Task Briefing

Covers scripted instructions, practice trials, and managing participant anxiety. Standardises the session start to reduce behavioural variability.

Lesson 2 • Lab Environment Preparation

Specifies lighting, seating, and monitor positioning for optimal tracking quality. Reduces environmental noise before any participant arrives.

Lesson 3 • Recording and Session Monitoring

Explains live gaze preview, data quality indicators, and mid-session interventions. Enables researchers to catch and correct data quality issues in real time.

Lesson 4 • Calibration Procedures and Validation

Walks through standard 5-point and 9-point calibration routines and validation checks. Establishes the accuracy baseline required for trustworthy data.

Lesson 1 • Saccade and Transition Metrics

Examines saccade amplitude, direction, and AOI transition matrices. Reveals navigation patterns and visual search strategies across interface elements.

Lesson 2 • Defining Areas of Interest

Teaches manual and automated AOI drawing, including dynamic AOIs for video stimuli. AOIs are the spatial units that make metric calculation possible.

Lesson 3 • Fixation-Based Metrics

Covers fixation count, duration, and time to first fixation as primary attention indicators. Connects each metric to specific UX questions about usability and salience.

Lesson 4 • Aggregating and Comparing Metrics

Demonstrates how to aggregate metrics across participants and compare conditions. Builds the quantitative summary layer needed before statistical testing.

Lesson 1 • Scanpath Similarity and Comparison

Introduces string-editing distance, Levenshtein, and MultiMatch algorithms. Enables quantitative comparison of viewing sequences across participants or conditions.

Lesson 2 • Heatmap Generation and Interpretation

Explains fixation-based and dwell-time heatmaps, colour scales, and Gaussian smoothing. Teaches correct reading of hot zones and cold zones in UX context.

Lesson 3 • Overlay and Contextual Visualisation

Demonstrates overlaying gaze data on wireframes, prototypes, and video frames. Connects visual outputs directly to design artefacts for stakeholder communication.

Lesson 1 • Comparing Conditions and Groups

Covers t-tests, Mann-Whitney, ANOVA, and Kruskal-Wallis for gaze metrics. Builds the toolkit for answering the most common UX comparison questions.

Lesson 2 • Correlation and Regression with Gaze Data

Examines relationships between gaze metrics and usability outcomes such as task time. Enables predictive modelling of user performance from attention data.

Lesson 3 • Reporting Statistical Results Clearly

Provides templates for writing up gaze statistics in APA and plain-language formats. Ensures findings are communicated accurately to both technical and non-technical audiences.

Lesson 4 • Choosing the Right Statistical Test

Maps gaze metric types to parametric and non-parametric tests. Prevents common errors from applying inappropriate tests to non-normal gaze distributions.

Lesson 1 • Identifying Usability Issues from Gaze

Maps gaze patterns such as long fixations and missed AOIs to specific usability problems. Builds diagnostic skill for translating data into problem statements.

Lesson 2 • Triangulating Gaze with Other UX Data

Combines eye tracking with think-aloud, surveys, and task performance metrics. Produces richer, more credible findings than gaze data alone can provide.

Lesson 3 • Prioritising Design Recommendations

Applies severity rating and impact-effort frameworks to rank gaze-identified issues. Ensures design teams act on the most critical problems first.

Lesson 4 • Presenting Findings to Stakeholders

Covers slide deck structure, live demo techniques, and handling stakeholder questions. Prepares students to advocate for data-driven design changes confidently.