by Zizheng Zhou
Hello! I'm Zizheng Zhou. This website documents my learning journey in Digital Practices, including weekly reflections and my group project.
Claire’s Day is our group project: an interactive text-based dialogue game designed in Figma. It simulates a day in the life of a Western working woman and explores workplace inequalities through user choices.
The project highlights issues such as gender bias, emotional labour, workplace pressure, and unequal opportunities. Different choices lead to different endings, showing how structural and everyday inequalities shape women's experiences.
This week reframed my assumptions about technology. Rather than treating tools as inevitable forces, the lectures stressed social shaping: technologies arise from specific contexts and, in turn, reorganise practice. I tried to read arguments, not hype, and to note claims, evidence, and limits. The main shift was methodological humility—trace situated use and avoid simple causal stories. Practically, I slowed down and focused on core concepts before touching tools. That approach made the material feel more manageable and honest.
This week’s materials highlight how algorithms shape visibility and reinforce power structures. Bucher (2012) shows how users risk invisibility through algorithmic control, while Benjamin (2019) reveals embedded racial bias. Combined with the platform society concept, I realise digital systems are not neutral but deeply political and socially constructed.
This week deepens my understanding of platform power. Gillespie (2018) shows content moderation is not neutral but shaped by hidden decisions, while Zhang et al. (2021) highlight how algorithms influence content production. Combined with web scraping practices, I realise data and visibility are actively constructed, not simply collected.
Scenario 1 made “no raw data” concrete. Our initial survey questions were too general to capture how students actually use generative AI. After feedback that items lacked specificity and direction, our group met to discuss fixes. We agreed to focus on behaviours, such as tasks, frequency, and course context, and to clarify time frames and response options. We have not piloted anything yet; the priority is tightening wording so any later analysis reflects meaningful, comparable constructs rather than loose opinions.
We continued refining the Scenario 1 survey. I revised several vague items into single-focus questions and added brief examples to reduce ambiguity. We also noted clear limits, including self-report bias and disciplinary differences, so expectations stay realistic. I am not designing visuals in advance; if data are collected later, I would start with simple distributions and cross-tabs. The overarching lesson remains: categories are consequential. Our choices should be justified, transparent, and easy to revisit as our understanding develops.
This week reshapes my understanding of identity as data-driven and algorithmically constructed. Hearn (2017) shows self-presentation is shaped by platform logics, while Noble (2018) exposes embedded bias. The workshop highlights how platforms categorise us through data, making identity fluid yet constrained by hidden systems and power relations.
Negative prompting means guiding AI by telling it what to avoid. It shows me that outputs are not fully creative, but shaped by probabilistic filtering and constraints, similar to how our identities are shaped by platform rules rather than complete freedom.
This week makes me rethink digital media as part of ecological systems. Turnbull et al. (2024) show technologies are entangled with environmental and social processes, while Gabrys (2013) reveals their hidden material impacts. I realise digital practices are not immaterial, but deeply connected to resource extraction and ecological harm.
This week makes me curious about how much of my “self” is actually data. Lupton (2016) shows how everyday tracking turns life into measurable data, while Whitson (2013) suggests we even start “playing” with these systems. It makes me wonder: am I shaping my data, or is my data shaping me?
Cybertext, according to Aarseth, is a form of text where the structure and medium matter, and it works like a “machine” that produces different expressions depending on how the user interacts with it. Ergodic literature means the reader must make non-trivial effort, such as choices or actions, to move through the text, not just read passively. Compared to conventional narratives, cybertexts feel more active: I am not just reading, I am actually shaping the story path. An everyday example could be a choice-based game or Netflix’s Bandersnatch, where my decisions change what happens next.
This week makes me reflect more on collaboration in a slightly uncertain way. The brief shows our project is not just making a story, but questioning how narratives are constructed and whose perspectives are missing. At the same time, group work feels a bit challenging. Different roles and coordination are needed, so collaboration is not automatic but something we have to actively build.
Over these two weeks, I realise our group was still in the early stage of defining our topic. We had a general interest in women’s workplace inequality, but no clear focus yet, so discussions felt a bit uncertain. However, this helped me understand that defining a topic is more about finding a direction than having a fixed idea.
I also became more aware of how I research. I usually rely on Google, but I now see that search results are shaped by algorithms and may limit perspectives. This made me think more critically about sources and what might be missing.
This week made me think more practically about how we will collect data for our project. As a group, we are planning to use questionnaires, literature data, and semi-structured interviews to support our topic on women’s workplace inequality. The workshop highlights that we need to carefully plan what data to collect and how to manage it.
The readings also made me more aware of ethical issues, especially around what counts as “public” data. Even if data seems accessible, we still need to consider consent, privacy, and how we represent participants. This made me realise that data collection is not just technical, but also ethical and responsible.
This week made me think differently about how our project could go beyond just data and analysis. Pink (2015) shows that digital media are multi-sensory, not just visual, and the workshop encourages us to consider sound, feeling, and experience in research.
For our topic on women’s workplace inequality, this made me think about how we could represent emotions like stress or pressure, not just describe them. Digital media could help us do this through interactive storytelling, making the experience more immersive rather than purely informational.
Over these two weeks, our project became much clearer. We decided to create an interactive narrative about a Western woman’s working day, focusing on workplace inequalities, and chose to use Figma to design and prototype it. This also connects to the brief’s idea of storytelling as a way of representing data and experiences, rather than just presenting information.
The readings on affordances, such as Davis (2021), and interfaces, such as Stanfill (2015), made me realise that the tool we use will shape how the story is experienced. Using Figma means thinking carefully about layout, interaction, and user pathways, which will influence how players understand and engage with these inequalities.
This week made me reflect more on our project through feedback and reflexivity. Presenting our idea helped me see how our interactive narrative communicates women’s workplace inequality, but also what might be unclear to others.
The idea of reflexivity made me realise that our choices—what stories we include and how we design them—are shaped by our own perspectives. At the same time, some uncertainty in our project actually feels useful, as it pushes us to rethink and improve our narrative rather than assuming it is already complete.
This week, I feel our group made solid progress through active collaboration. We developed clear outlines for each storyline and even presented them to others, which helped us see how our ideas were coming together. Each character’s personality, the challenges they face, and the meaning behind different endings are now much clearer.
At the same time, the workshop reminded me to connect our narrative back to our research question and data. I realise our project is not just storytelling, but using structured narratives to communicate real workplace inequalities.