Week 04 Interviews & Liquid Stain Experiments
Post 10: Peer InterviewsPost 11: Quantifying Consumption
What
  • In Week 4, I conducted qualitative interviews with friends across various academic disciplines to collect primary field data. 

    The focus was to track their weekly consumption of coffee and tea while uncovering the underlying emotional and psychological drivers behind these daily habits.

    • How
  • Interview Scope: I engaged peers from diverse departments (arts, sciences, humanities) to understand if consumption patterns varied across academic fields.

    Data Findings    : The overwhelming majority cited academic pressure—specifically meeting graduation project deadlines, tackling coursework, and chasing higher grades—as their primary reason for consuming tea or coffee.

    Emotional Mapping    : Coffee and tea emerged not just as physical stimulants, but as ritualistic coping mechanisms for managing perfectionism, fatigue, and systemic pressure within higher education.

    • Reflection
    This field research validated the core premise of my project. 

    Coffee and tea are not merely personal beverages; they serve as social indicators of shared academic anxiety. 

    Listening to personal stories behind every cup shifted my project's data source from abstract statistics to genuine human emotion and lived experiences.

    #Week4 #QualitativeResearch #PeerInterviews #AcademicAnxiety #CoffeeCulture

    What
    Parallel to the narrative interviews, I logged and categorized the quantitative data regarding the weekly frequency of tea and coffee consumption among my interviewees. This quantitative dataset serves as the numerical foundation for my generative system.
    How
    Frequency Tracking: I recorded the exact number of cups consumed per person per week, creating individual profile metrics.
    Data Clustering: I grouped the consumption data against project milestones and stress intensity levels (e.g., normal study weeks vs. crunch times before tutorials).

    Data Preparation: These numerical values were structured into data strings, ready to be mapped as input parameters (such as scale, density, and opacity) in my upcoming dynamic generative engine.

    Reflection
    Translating human habits into numerical parameters is a delicate process in generative design. Data alone can feel cold and detached; however, when every integer represents a cup of coffee consumed during an anxious midnight session, the data retains its underlying human story.

    #Week4 #DataCollection #InformationDesign #DataVisualization #QuantitativeData


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    Post 12: Stain Behavior Across MediaVisual Research
    WhatTo prepare for the visual generation phase of my installation, I conducted hands-on material testing to observe, document, and analyze how coffee stains form, spread, and dry across different physical surfaces and substrates.

    How
  • Substrate Testing: I applied liquid coffee onto various paper weights, absorbent textures, smooth coated cards, and non-porous materials to observe physical diffusion.

    Morphological Observation    : I recorded the ring effect (capillary flow pushing particles to the edges), bleed gradients, drying times, and layered transparency when multiple stains overlapped.

    High-Resolution Archiving: I photographed and digitized the resulting organic patterns to build a visual reference library for digital simulation.
  • Reflection
    Observing liquid dynamics in the physical world revealed nuances that pure code often misses. Fluid dynamics follow precise physical laws, yet produce seemingly infinite organic variation. Translating these physical constraints (absorbency, viscosity, edge accumulation) into dynamic algorithmic rules will be critical to making the generative visuals feel authentic rather than artificial.

    #Week4 #MaterialExploration #VisualResearch #TextureAnalysis #GenerativePrep








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