Here is the list of my major modulees:
In the first week of the course, I began to realise that digital media is not just about learning technical tools. I used to think technology was simply a neutral instrument, but concepts like technological determinism, the social construction of technology, and the social shaping of technology showed me that technology and society influence each other. I also started to think more critically about algorithms and how they shape what we see online, which made me more aware of the role digital media plays in everyday life.
During Week 2, I moved from theory to practice by building my first webpage using HTML and CSS. I learned that HTML is responsible for the structure and content, while CSS controls the layout and appearance. Creating and uploading my own index.html page helped me understand how websites actually work behind the scenes. At the same time, I realised that even simple pages require a lot of small adjustments, and I still need to improve my confidence with CSS selectors and visual design.
This week was my first real experience with web scraping. Previously, I assumed that scraping simply revealed the data behind a webpage. However, once I followed the steps—opening Inspect, creating a sitemap in the Web Scraper tab, adding selectors and running the scraper—I realised that data is structured and controlled much more carefully than I expected. When scraping a BBC page, I noticed that I could only access visible items such as titles and categories. This made me understand that what we retrieve is not neutral information, but the result of platform design decisions. I also became more aware of copyright, privacy and platform restrictions, which showed me that web scraping requires both technical skills and ethical responsibility.
This week’s focus was on how data is classified and how this process is connected to power. I learned that data collection is never neutral, because algorithms are designed by humans and reflect existing social hierarchies and biases. In the workshop, our group designed a questionnaire about students’ use of generative AI and spent time clarifying the purpose of our data collection. This helped me see that research design shapes what can and cannot be expressed in the data. Reflecting on my own use of platforms like TikTok also made me aware that personalisation can improve user experience but at the same time create information isolation and raise questions about consent and data usage.
This week focused on transforming raw data into meaningful visual narratives. We visualised the dataset collected in Week 4 and I realised that every design decision—such as colour, layout and scale—affects how people interpret the charts. Working with tools like Tableau showed me that a good visualisation should highlight the most important patterns instead of showing everything at once. I also learned that data visualisation is not neutral: it organises information, directs attention and can support particular interpretations. This made me more aware of the need to balance clarity, aesthetics and ethics when presenting data.
This week helped me recognise how digital platforms create algorithmic identities for us. By looking at advertising data and recommendation settings on apps like Instagram, TikTok and Xiaohongshu, I realised that platforms reduce complex individuals into simplified profiles based on clicks, viewing time and topics of interest. These profiles can affect what content we see and may even influence real-life opportunities. I also noticed a gap between how I see myself and how platforms categorise me, which sometimes feels intrusive or inaccurate. Overall, this week made me more conscious of how algorithms do not simply reflect identity but actively participate in constructing it.