Danny’s Learning Journal

Week 10 — Interactive Fiction, Twine, and Procedural Storytelling

This week introduced interactive fiction and Twine as a platform for building multilinear stories. We explored how hyperlinks, branching choices, and conditional logic transform a narrative into an interactive system rather than a static text. Twine allowed us to prototype story structures for our Kirkgate Market project while experimenting with mood, pacing, and player decision-making.

Through hands-on work, I learned how the writer becomes a system designer: every link, variable, and passage frames what the reader can and cannot do. Twine makes visible how narrative is organised procedurally, not just linguistically.

Reflection：
This week helped me understand interactive stories as a form of procedural rhetoric—the argument is made through structure, choice, and consequence, not only through words. Designing my own Kirkgate Market story made me confront how agency is always crafted: players feel free, but their freedom is shaped by the pathways I author. This revealed the politics of interactive design and the responsibility of structuring meaningful choices. I now see Twine as a cybertext environment where narrative emerges from the negotiation between system logic and player imagination.

Figure. Visualising the multicursal structure of my Twine interactive story.

Week 9 — Creative Hacking, Senses and Bodies

This week we were introduced to creative hacking through the Arduino environment. Working in the Helix space, we built the “Love-O-Meter” circuit from the Arduino classroom kit, using a temperature sensor, LEDs and a microcontroller to measure changes in our body temperature. The workshop showed how simple hardware, code and sensors can be combined to engineer a small human–body sensing system.

As we uploaded the example code, read serial data and watched the LEDs respond, we saw how the body is translated into digital data: analogue voltage becomes numbers, thresholds, and visual feedback on screen. The task didn’t always work perfectly, but the glitches and errors were part of learning how creative hacking operates as an experimental, improvisational method rather than a polished engineering pipeline.

Reflection：
This workshop helped me connect embodied experience with datafication. Feeling my own skin temperature while seeing the Arduino plotter update made it clear that “the body” in digital systems is always a mediated construction—what counts is what the sensor can read and the code decides to display. Creative hacking framed making and tinkering as a critical practice: by wiring, debugging and adjusting the Love-O-Meter, I started to question how other sensing systems (wearables, health apps, surveillance devices) also measure, normalise and classify bodies in everyday life.

Week 8 — Digital Ecologies at Kirkgate Market

This week we worked with digital ecologies at Kirkgate Market, using our bodies and senses to explore human–food relations. Guided by Turnbull et al. (2024), we approached the market as a dynamic ecology of humans, foods, materials, infrastructures and data, rather than just a shopping space. In groups we followed our senses — sight, smell, touch, sound and even taste — while documenting stalls, ingredients and atmospheres through photos, audio, and notes on our phones.

We also discussed entanglement (Barad) and more-than-human worlds (Abram), imagining the wider multispecies and labour networks behind each ingredient. This made visible which foods, bodies and stories are amplified or marginalised within the market’s mediated environment.

Figure. Sensory exploration at Kirkgate Market — observing textures, colours, and multispecies entanglements.

Reflection：
This week reframed Kirkgate Market for me from a familiar everyday space into a more-than-human digital ecology. Using my phone as a sensing device made me aware of how digital mediation both amplifies and filters what I notice: some sounds, colours and textures become data, while others fade into the background. Thinking with entanglement helped me see foods not as isolated objects but as nodes in a web of landscapes, workers, species and technologies. The workshop showed me that “doing digital practice” can mean cultivating situated, ethical forms of attention, not just collecting content.

Week 7 — AI, Identity, and Negative Prompting

This week we examined how generative AI models construct and reproduce identities through patterns in training data. The workshop introduced negative prompting as a method for uncovering hidden biases—by asking the model what something is not, we reveal the assumptions embedded in its representational logic.

Through a series of AI tasks, I learned how prompts do not simply retrieve information but actively shape identity categories. The AI’s responses reflected broader cultural assumptions about gender, race, ability, and class, showing how computational identity is produced through statistical associations rather than lived experience.

Reflection：
Negative prompting made visible the defaults that AI systems rely on, the stereotypes they normalise, and the exclusions built into their training data. I realised that AI does not “mirror” identity—it performs identity through prediction, compression, and categorisation. This highlighted the politics of generative models, where representational harms can emerge even from seemingly neutral prompts.

Week 6 — Algorithmic Identity

This week’s workshop explored algorithmic identity (Cheney-Lippold, 2017): the idea that who we “are” online is continually produced by the data we generate and the classifications applied to us by platforms. We worked through three steps — Input, Output, and Process — to understand how platforms see us, how they classify us, and how categories shape our experiences online.

Task 1 – INPUT: I listed the types of data I share, intentionally and unintentionally, on my most-used platforms, including:

• Content I upload
• Demographic data
• Location data and search queries
• Browsing behaviour and engagement patterns
• Biometric or device-level data

Reflection： I realised that even when I am not actively posting, platforms still generate data about me through log files, tracking pixels and metadata.

Task 2 – OUTPUT: I checked how Google and other platforms categorise me in their ad settings dashboards, revealing labels such as “Parents of toddlers” that do not match my real life.

Reflection： These categories show how platforms simplify complex identities into a few data-driven labels. Some of them made sense, while others felt inaccurate or overly general, reminding me that these labels are operational hypotheses rather than truths.

Task 3 – PROCESS: To “un-black-box” the categorisation process, I applied David Sumpter’s 13-category model to manually classify 32 friends × 15 posts each.

Figure. Manual classification using Sumpter’s 13 categories.

Reflection： I realised how subjective manual classification is. Many posts could fit multiple categories, but I had to force them into only one. This raised questions about how platforms interpret far more complex data at scale using rigid taxonomies.

🌟 Overall, I learned that algorithmic identity is not a fixed description but a dynamic construction shaped by data traces, platform logics, and predefined categories.

Week 5 — Data Visualisation and the Politics of Seeing

This week we moved from designing datasets to visualising them. Using the numerical data we had created in Week 4, we experimented with different chart types — bar charts, distributions and comparisons — to see how each form highlights or hides particular patterns. I learned that visualisation is not a neutral translation of numbers into images, but a design practice that frames how data becomes knowable.

Reflection:
Working with charts made me realise how easily visual choices can steer interpretation. As D’Ignazio and Klein argue, data visualisation often performs authority while obscuring the labour, uncertainty and exclusions behind the data. Seemingly small decisions — where to cut the y-axis, how to group categories, which colours to emphasise — can exaggerate tiny differences or flatten important variations. This week taught me to read charts critically and to see visualisation as a site of representational power, not just technical skill.

Week 4 — Data, Power, and Designing a Dataset

This week we explored how data is never neutral but shaped by social, political, and epistemic assumptions. Through a feminist data lens, we examined how “the way data is understood, captured, and classified is an act of world-making” (Crawford, 2021). Our group designed a numerical dataset about how university students use generative AI in their daily study, which made me more aware of how variables, measurement choices, and omissions construct particular representations of student behaviour.

Reflection： I realised that creating a dataset is not simply a technical procedure but a methodological and ethical act. Choosing what to count—and what remains uncounted— revealed structural biases and data gaps (Gitelman & Jackson, 2013). The exercise highlighted how power operates through datafication.

Week 3 — Data and Data Analysis

This week we learned how data collection and analysis can reflect power relations and ethical choices in research. We explored feminist approaches to data and discussed how datasets can both represent and shape the world. In groups, we designed surveys to collect numerical data — my group focused on how university students use generative AI tools in their daily study. This activity helped me understand the importance of ethical design, consent, and potential data gaps when gathering information.

Week 2 — Understanding Web Scraping

This week we learned how web pages can be understood as structured data and explored the concept of web scraping — extracting information from online sources for research. We inspected website code using browser tools and tried scraping examples with tools such as OutWit Hub, WebScraper.io, and ScrapeHero. I now understand how HTML structures enable data collection, and how these tools can support digital media research.

Week 1 — First Steps

We started our digital practice journey by installing Phoenix Code and FileZilla. I learned how to edit a basic HTML page and upload it to my Leeds New Media web space. It was my first time building something that can go online.