Interaction Design: Examinable Syllabus and Topic Map

What the exam tests

The Part IA Interaction Design paper (Paper 3, Questions 5–6) is set by Professor Hatice Gunes. The course covers 12 lectures across Lent term. The exam questions are multi-part (a–e), later parts often building on earlier ones. The mark allocation roughly indicates how much to write — roughly 1 mark per substantive point with named-theory justification.

The paper rewards named, applied theory over generic common sense. Every substantive point should reference a named heuristic, law, framework, or principle.

Syllabus Checklist

1. Requirements and Stakeholder Analysis

• The six categories of requirements (Functional, Data, Environmental, User Characteristics, Usability Goals, UX Goals)
• Phrasing requirements as testable statements with a one-line rationale
• CUSTOM stakeholder analysis (Primary, Secondary, Tertiary, Facilitating)
• “Users ≠ stakeholders” — naming at least one non-user stakeholder
• Personas from research data, not guesswork
• Three pitfalls in requirements gathering, with solutions
• Critiquing weak evidence vs strong claims

2. User Research Methods

• Data-gathering methods: interviews, questionnaires, observation, focus groups, ethnography, card sorting, diary studies, contextual inquiry
• Matching methods to project stage (early/exploratory → mid-stage → summative)
• Matching methods to participant populations (visual impairment, emotional vulnerability, time-pressured experts)
• Self-report vs observed behaviour and the gap between them
• Qualitative data analysis: categorical coding (and $\kappa$), grounded theory, thematic analysis, affinity diagramming
• Triangulation, member-checking, inter-rater reliability
• Bad evaluation: leading questions, unevidenced assertions, HiPPO method

3. Task Analysis

• Hierarchical Task Analysis (HTA): goals, sub-goals, operations, plans
• Constructing an HTA from a task description
• Limitations: fixed sequence, ignores cognitive state, poor error modelling, misses tacit knowledge, cannot model novel tasks

4. Prototyping

• Lo-Fi vs Hi-Fi prototypes — when each is appropriate and why Lo-Fi encourages better feedback
• Named methods: paper prototyping, Wizard of Oz, wireframing, interactive prototype, vertical prototype, horizontal prototype
• Exam sketching: labelled screens with captions, explicit links to requirements

5. Cognitive Foundations

• Human memory: STM/working memory and Miller’s $7 \pm 2$; LTM and schemas; recognition vs recall
• Design implications: chunking, carry state forward, consistent terminology, autocomplete
• Human attention: selective, divided, sustained, pre-attentive processing
• Design implications: visual hierarchy, reserving pre-attentive cues, avoiding attention competition
• Gestalt theory: Proximity, Similarity, Continuity, Closure, Figure/Ground, Common Fate
• The three-step exam method: name → locate → explain effect

6. Design Principles and IA

• Norman’s principles: affordance, signifier, feedback, mapping
• Visual representation conventions: typography, maps/graphs, schematics, icons, the desktop metaphor
• Cognitive Dimensions: 14 dimensions, 6 notational activities, selecting 3–4 relevant dimensions and discussing trade-offs
• Information Architecture: organisation, labelling, navigation, search
• IA diagrams as trees; common IA issues

7. Mathematical Laws

• Fitts’ Law: $\text{MT} = a + b\log_2(2D/W)$
• Hicks’ Law: $T = b\log_2(n+1)$
• Comparing layouts using both laws
• KLM operators: $K, P, H, M, R$ with typical times
• GOMS: Goals, Operators, Methods, Selection Rules
• Three limitations of GOMS/KLM (expert-only, no errors, requires fixed task)

8. Evaluation

• Nielsen’s 10 heuristics full reference and severity ratings (0–4)
• HE procedure: 3–5 independent evaluators, aggregate, prioritise
• Cognitive Walkthrough: the four questions, procedure, HE vs CW comparison
• Formative vs summative, analytical vs empirical, quantitative vs qualitative
• RCTs and A/B testing; sign test; internal/external/ecological validity; Hawthorne Effect
• Card sorting: open vs closed; similarity formula; interpreting a similarity matrix; sample-size caveats

9. Designing Meaningful Systems

• Three waves of HCI (efficiency → socio-technical → emotion/meaning)
• Design ethnography: what happens + how things matter
• Wicked problems: Rittel & Webber’s characteristics; why goal-based models fail
• Interaction as search; bounded rationality and satisficing
• Prospect theory, heuristics and biases (availability, affect, representativeness, loss aversion, expectation, bandwagon)
• Attention Investment Theory; persuasive design and dark patterns

Past Paper Topic Map (2017–2025)

Year	Q5 Topics	Q6 Topics
2017	Balancing automated/human control; Winograd’s interaction spaces; task models	Requirements-gathering problems; Gestalt (6 principles); HE on a music player
2018	Functional/Non-functional/Data requirements; HE on an order-search page; GOMS/KLM	CUSTOM stakeholders; Lo-Fi/Wizard of Oz prototyping; Fitts’/Hick’s Law comparison
2019	Iterative UCD cycle; HE on a clothing site; card sorting & similarity analysis	Primary stakeholder & data-gathering techniques; HE vs CW choice criteria; Gestalt (4 figures)
2020	CUSTOM stakeholders; task-analysis limitations; evaluating quantitative findings; HE vs CW for a novel device	Stakeholders & task model (Weather App); data collection for visually impaired users; Gestalt (6 principles)
2021	Timepiece requirements; Lo-Fi prototype alternatives; HE of two prototypes	CW on weather app (5 tasks); redesign suggestions from CW; further data gathering
2022	Gestalt (dashboard); CW instruction sheet; mobile vs desktop redesign	Data-gathering methods (COVID app); stakeholders & requirements; design principle; usability/security trade-off
2023	Human memory; human attention; information architecture & IA issue; evaluation study design (CamCORS)	User research methods (mindfulness app); stakeholder analysis; requirements; design principle; prototyping methods
2024	Stakeholder groups (ChatGPT); HE of ChatGPT; stakeholder interview questions; interview-data analysis	Requirements; sketch design screens; user personas; Gestalt applied to own design
2025	Requirements; sketch app screens; IA diagram; CW on own design; redesign from CW	HE violations (Teams); human attention (Teams); human memory (Teams); design principle; low-adoption problem

Key Formulae

Formula	Use
$\text{MT} = a + b\log_2(2D/W)$	Fitts’ Law — predict pointing time
$T = b\log_2(n+1)$	Hick’s Law — predict decision time
$\text{Sim}(A,B) = \frac{\text{participants co-grouping } A,B}{\text{total participants}} \times 100\%$	Card sorting similarity analysis
$\text{Error Budget} = 100\% - \text{SLO}$	(Not ID, but appears cross-paper for reference)

High-Frequency Topics

• Heuristic Evaluation: 2017, 2018, 2019, 2020, 2021, 2022, 2024, 2025 — almost every sitting
• Stakeholders / Requirements: every year without exception
• Cognitive Walkthrough: 2019, 2020, 2021, 2022, 2025
• Gestalt Theory: 2017, 2019, 2020, 2022, 2024
• UCD Process / Prototyping: 2018, 2019, 2020, 2021, 2023, 2025
• Cognitive Foundations (Memory / Attention): 2023, 2025 — new syllabus emphasis