INF385C, HCI Human Computer Interaction

The lectures below follow Hornbæk, Kristensson, and Oulasvirta, Introduction to Human-Computer Interaction (Oxford University Press, 2025), available free online. They replace the earlier lectures built around Hartson and Pyla (2019).

The syllabus is the course’s main document. Expect it to be followed to the letter.

Lecture

01 Intro introduces the instructors, the syllabus, and the textbook.

02 People covers perception, motor control, and cognition: saliency and Gestalt grouping, Fitts’ and Hick-Hyman laws, working memory, mental models, and decision-making heuristics.

03 People covers needs and motivation, behavior change, dark patterns, experience, collaboration, and communication, including computers as social actors.

04 Research introduces user research: why you are not the user, validity and reliability, interviews, contextual inquiry, field research, ethnography, and surveys.

05 Research covers unobtrusive research (log files, instrumentation, archival data) and representations of user research: personas, scenarios, journeys, task analysis, and requirements.

06 Interaction treats interaction itself: information and control, throughput, closed-loop feedback, dialogue and the two gulfs, tool use, automation, bounded rationality, and appropriation in practice.

07 Interfaces surveys user interfaces: interaction styles, input devices and sensing, displays and haptics, interaction techniques, commands and navigation, GUIs and direct manipulation, and reality-based interaction.

08 Design covers design as product and as change, design cognition and fixation, ideation and sketching, critique and participatory practice, and design processes from waterfall to agile.

09 Engineering is the first of two engineering lectures: building the right thing versus building the thing right, systems thinking and system mapping, and design engineering from requirements through concept selection and verification.

10 Engineering continues with safety and risk, human error, reliability and security, software architecture and end-user development, and computational methods including modeling, optimization, and machine learning.

11 Evaluation is the first of three evaluation lectures: formative versus summative evaluation, yardsticks, and analytic methods — heuristic evaluation, cognitive walkthrough, keystroke-level modeling — plus think-aloud studies and the evaluator effect.

12 Evaluation covers experiments and walks through analyzing one in R and RStudio.

13 Evaluation covers field evaluations: ecological validity, pilot studies, probes and minimum viable products, and deployment (in-the-wild) studies.

14 Conclusion closes the course.

hciExperiments is the companion R material for quantitative evaluation, used with lecture 12.