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2.13. Research Methods and Emerging Technologies (RES)
Research Methods and Emerging Technologies equips students with the methodologies to conduct systematic inquiry in architecture. It covers research design, quantitative and qualitative methods, evidence-based design, neuroscience approaches, AI in research, speculative futures, IoT and sensing, and post-occupancy evaluation. Students learn to frame research questions, collect and analyze data, and apply findings to design practice.
2.13.1. RES/Research Design and Methodologies (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Covers the design of architectural research: formulating research questions, selecting methodological approaches, constructing research protocols, and addressing validity and reliability in architectural inquiry.
Topics:
Core
- Formulating research questions in architecture: descriptive, relational, causal; turning design problems into researchable questions
- Choosing a research methodology: quantitative (hypothesis testing, statistical), qualitative (interpretive, case study), mixed methods,
- Validity and reliability in architectural research: internal validity, external validity (generalizability), construct validity,
- Research ethics in architectural research: informed consent, privacy (anonymization of building users), institutional review board (IRB) protocols,
- Mixed methods research: sequential (qualitative then quantitative) and concurrent designs; integrating qualitative findings (user interviews) with quantitative data (occupancy
Learning Outcomes:
Core:
- Describe the main research methodologies in architecture - quantitative, qualitative, mixed methods, practice-based - and explain their appropriate applications [Familiarity]
- Develop a research design for an architectural inquiry, including research question, literature review, methodology selection, data collection protocol, and analysis plan [Usage]
- Evaluate the research design of a published architectural study for validity, reliability, and ethical soundness, identifying methodological strengths and weaknesses [Assessment]
2.13.2. RES/Quantitative Methods in Architectural Research (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Covers quantitative research methods in architecture: surveys, experiments, quasi-experiments, statistical analysis (descriptive and inferential), and the use of instrumentation for data collection.
Topics:
Core
- Survey design for architectural research: sampling methods (probability, convenience), questionnaire design (Likert scales, semantic differential), response rate optimization,
- Experimental designs: true experiments (random assignment, control group), quasi-experiments (non-randomized, pre-post, time series),
- Descriptive statistics: measures of central tendency (mean, median, mode), dispersion (standard deviation, range, interquartile range),
- Inferential statistics: hypothesis testing (t-test, ANOVA, chi-square), correlation and regression (linear, multiple), and interpretation of p-values
- Instrumentation for quantitative data collection: environmental sensors (temperature, light, \(CO_{2}\)), occupancy counters, objective performance measures (task completion time
Learning Outcomes:
Core:
- Describe the main quantitative methods in architectural research - surveys, experiments, quasi-experiments - [Familiarity]
- Design a survey or experiment to test a hypothesis about the effect of a design variable (lighting, color, layout) on user satisfaction, task performance, or behavior [Usage]
- Evaluate a quantitative architectural study for sampling validity, statistical analysis appropriateness, and correct interpretation of p-values and effect sizes [Assessment]
2.13.3. RES/Qualitative and Ethnographic Methods (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Covers qualitative research methods for architecture: interviews, focus groups, participant observation, ethnographic fieldwork, content analysis, and grounded theory for understanding user experience and design processes.
Topics:
Core
- Interviews and focus groups: structured vs. semi-structured vs. unstructured interviews, focus group dynamics, questioning strategies (probing, laddering), and recording
- Ethnography and observation in architecture: participant observation (researcher as user), non-participant observation (fly-on-the-wall), time-sampling behavior mapping,
- Content and thematic analysis: coding qualitative data (open coding, axial coding, selective coding), identifying themes
- Grounded theory approach in architectural research: generating theory from data rather than hypothesis testing; constant comparative method
- Case study methodology in architecture: single vs. multiple case design, case selection (typical, extreme, revelatory), data triangulation (documents, interviews, observations)
Learning Outcomes:
Core:
- Describe the main qualitative methods in architectural research - interviews, focus groups, ethnography, grounded theory - [Familiarity]
- Conduct a qualitative study (interview, observation, or focus group) to investigate how users experience a built space, and analyze the data using thematic coding [Usage]
- Evaluate the rigor of a qualitative architectural study based on credibility, transferability, dependability, and confirmability criteria (Lincoln & Guba) [Assessment]
2.13.4. RES/Evidence-Based Design (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Covers the application of research evidence to design decision-making: systematic reviews, practice guidelines, knowledge translation, and the integration of best available evidence with design expertise and client values.
Topics:
Core
- Evidence-based design (EBD) framework: formulating a clinical or design question, searching for best evidence (hierarchy of evidence), critically appraising evidence
- Hierarchy of evidence in architecture: systematic reviews and meta-analyses, randomized controlled trials (RCTs) in healthcare design, quasi-experimental studies, case studies
- Systematic reviews and meta-analysis in EBD: steps (protocol, search strategy, inclusion criteria, data extraction, synthesis), using PRISMA guidelines,
- Practice guidelines from evidence: AIA Design Guidelines for Healthcare Facilities, WELL Building Standard evidence documentation,
- Post-occupancy evaluation (POE) as evidence feedback: comparing design intent to measured outcomes (satisfaction, productivity, safety), feeding results into future projects,
Learning Outcomes:
Core:
- Describe the evidence-based design process: question formulation, evidence search, critical appraisal, integration, and outcome evaluation - [Familiarity]
- Apply an evidence-based design approach to a specific design problem (e.g., patient room, classroom, open office), citing peer-reviewed studies to support key design decisions [Usage]
- Evaluate a design claim or guideline for its evidence base, assessing the quality of cited studies (design, sample size, statistical significance) [Assessment]
2.13.5. RES/Neuroscience and Cognitive Approaches ↑ Back to top
Covers the emerging field of neuroscience and cognitive science applied to architecture: measuring brain responses to built environments, cognitive load, emotional response, and the neural basis of spatial experience.
Topics:
Non Core
- Neuroscience methods for architecture: EEG (electroencephalogram) for real-time cognitive load and emotional arousal
- Spatial cognition and wayfinding: cognitive maps, landmark-based navigation, grid cell and place cell activity in the hippocampus, and the neural mechanisms of orientation
- Neuroscience of stress and restoration: the effect of nature views, windows, daylight, and ceiling height on cortisol reduction and prefrontal cortex activity
- Neuroaesthetics in architecture: brain response to curvature vs. angularity, complexity vs. simplicity, symmetry vs.
- Cognitive load and environmental design: the impact of clutter, noise, lighting flicker, and distraction on cognitive resource allocation (attentional capacity, memory
Learning Outcomes:
NonCore:
- Describe the neuroscience methods (EEG, fMRI, eye-tracking) used to study the brain's response to built environments [Familiarity]
- Design a research protocol using neuroscience methods to test a hypothesis about the effect of a design variable (ceiling height, curvature [Usage]
- Evaluate the validity and generalizability of a neuroscience study in architecture, assessing sample size, control of confounds, [Assessment]
2.13.6. RES/AI and Machine Learning in Architectural Research (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Covers the application of artificial intelligence and machine learning to architectural research: generative models for design exploration, computer vision for urban analysis, natural language processing for building codes, and reinforcement learning for spatial optimization.
Topics:
Core
- Machine learning on architectural data: image classification of architectural styles (CNN), clustering of floor plans (PCA, t-SNE)
- Generative design using ML: GANs (generative adversarial networks) for floor plan and facade generation
- Computer vision for urban and building research: semantic segmentation of street view images for urban form analysis (walkability, skyline, green view index)
- Natural language processing for architectural research: automated extraction of design requirements from client briefs, classification of building code clauses
- Reinforcement learning for spatial layout: training RL agents to generate floor plans that satisfy constraints (area, adjacency, circulation)
Learning Outcomes:
Core:
- Describe the main AI and ML methods - computer vision, generative modeling, NLP, reinforcement learning - applied to architectural research problems [Familiarity]
- Train or fine-tune a machine learning model (CNN for classification, GAN for image generation, or RL for layout) on a modest architectural dataset [Usage]
- Evaluate the performance and limitations of an AI model in architectural research, assessing data quality, overfitting, generalization to unseen conditions, [Assessment]
2.13.7. RES/Speculative and Future-Oriented Research ↑ Back to top
Covers speculative and critical design methods used to explore possible futures, challenge assumptions, and provoke debate through design research artifacts.
Topics:
Non Core
- Speculative design methodology: creating artifacts (objects, narratives, scenarios) that depict alternative presents or futures; distinguishing speculative from commercial design
- Design fiction as research: writing future press releases, creating fictional product catalogs, building prototype artifacts from imagined futures
- Critical design (Dunne & Raby): using design to critique social, technical, and cultural norms; examples in architecture (critical interiors, provocations on housing
- Futures research methods: scenario planning (alternative futures), backcasting from desired future to present actions, horizon scanning for emerging signals,
- Ethics of speculative research: avoiding fear-mongering or dystopian pessimism without agency; engaging stakeholders in co-speculation
Learning Outcomes:
NonCore:
- Describe the methods of speculative and critical design, design fiction, and futures studies, and explain their role in architectural research beyond problem-solving [Familiarity]
- Produce a speculative design artifact (scenario, prototype, narrative) that investigates a future condition for architecture - climate change, AI, demographic shift - [Usage]
- Evaluate a speculative architecture project for its critical rigor, plausibility of its future scenario, [Assessment]
2.13.8. RES/IoT, Sensing, and Smart Building Research ↑ Back to top
Covers the use of Internet of Things (IoT) sensors and data analytics in architectural research: occupancy monitoring, environmental sensing, building performance tracking, and the ethical challenges of data collection in occupied spaces.
Topics:
Non Core
- IoT sensor types for buildings: occupancy (PIR, \(CO_{2}\), camera, seat counters), environmental (temperature, humidity, light, noise, air quality), energy (plug-load monitors
- Data pipeline for building IoT: sensor placement (density, calibration), data collection (edge vs. cloud), time-series databases (InfluxDB, Timescale),
- Occupancy modeling from sensor data: using \(CO_{2}\) and motion to estimate real-time occupancy patterns, dwell time, and space utilization ratios
- Performance dashboards for buildings: visualizing real-time energy use, IEQ, and occupancy; using dashboards for occupant feedback (feedback loops) to reduce energy,
- Ethical issues in building IoT: informed consent for data collection in public and semi-public spaces, anonymization of occupant data, data ownership (building owner vs.
Learning Outcomes:
NonCore:
- Describe the types of IoT sensors used in building research and the data they collect (occupancy, environment, energy) for post-occupancy evaluation and performance monitoring [Familiarity]
- Design an IoT sensing plan for a building research question (e.g., space utilization, thermal comfort), specifying sensor type, placement, data collection duration, [Usage]
- Evaluate the ethical and technical adequacy of an IoT sensing deployment in a building, assessing privacy protections, data quality, [Assessment]
2.13.9. RES/Post-Occupancy Evaluation and Building Science (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Covers the systematic evaluation of building performance after occupancy: methods for assessing technical performance (energy, thermal comfort, IAQ) and user satisfaction (comfort, well-being, productivity), and feeding findings back into design practice.
Topics:
Core
- Post-occupancy evaluation (POE) methods: walkthroughs, surveys (POE questionnaire, BUS Methodology), focus groups, building diagnostics (energy metering, blower door),
- User satisfaction metrics: thermal satisfaction, perceived air quality, acoustic satisfaction, lighting quality, visual comfort, privacy, control over environment,
- Building performance gap: difference between predicted (energy model, design intent) and measured (energy use, comfort, IAQ) performance
- Building science measurement techniques: thermal imaging for envelope performance, data loggers for T/RH, \(CO_{2}\) monitors for IAQ, light meters for illuminance,
- Closing the feedback loop: integrating POE findings into design guidelines, updating building energy models with measured data,
Learning Outcomes:
Core:
- Describe the methods of post-occupancy evaluation - surveys, walkthroughs, building diagnostics - and the concept of the building performance gap [Familiarity]
- Conduct a post-occupancy evaluation of a building (survey, walkthrough, limited data logging), analyzing user satisfaction data alongside measured performance data to identify [Usage]
- Evaluate a POE report for methodological rigor, representativeness of respondents, alignment of questions with design intent, and actionable recommendations for future design [Assessment]
2.13.10. RES/Theoretical Framework and Literature Review (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Systematic process of identifying, analyzing, and synthesizing existing scientific and technical literature to build the theoretical and conceptual framework underpinning a research project, including bibliographic reference management and the identification of knowledge gaps.
Topics:
Core
- Systematic search of scientific and technical literature: specialized databases (Scopus, Web of Science, ASCE Library), keywords, and Boolean operators
- Critical reading and synthesis of scientific articles, technical standards, and prior research reports
- Building the conceptual and theoretical framework: defining key concepts, relationships between variables, and reference theoretical models
- Identifying knowledge gaps (research gap) and justifying the originality of the proposed contribution
- Bibliographic reference management and citation styles (APA, IEEE) using tools such as Zotero or Mendeley
Learning Outcomes:
Core:
- Explain the structure and purpose of a theoretical framework within a civil engineering research project [Familiarity]
- Conduct a systematic search of scientific and technical literature on a chosen research topic using specialized databases [Usage]
- Synthesize the state of the art of a research topic into a coherent theoretical framework that identifies a knowledge gap [Assessment]
2.13.11. RES/Research Hypotheses and Variables (Core Tier1: 1 hr, Core Tier2: 1 hr) ↑ Back to top
Formulation of research hypotheses and the identification, classification, and operationalization of the variables involved in an engineering study, establishing the link between the theoretical framework and the experimental or data-collection design.
Topics:
Core
- Types of hypotheses: null, alternative, directional, and non-directional, and their relationship to research questions
- Classification of variables: independent, dependent, control, and confounding
- Operationalizing variables: defining measurable indicators and measurement scales (nominal, ordinal, interval, ratio)
- Relationship between hypotheses, variables, and statistical significance tests: significance level, Type I and Type II error
- Graphical representation of the relationship between variables through conceptual or operational models
Learning Outcomes:
Core:
- Distinguish between null and alternative hypotheses, and between independent, dependent, and control variables, in a civil engineering research problem [Familiarity]
- Formulate a testable research hypothesis and operationalize its variables for a specific problem in the chosen specialization area [Usage]
- Evaluate the consistency between the theoretical framework, the stated hypothesis, and the proposed data-collection design [Assessment]