Liudan Wang's Personal Reflection

A simple record of five weeks of digital media learning and development.

Week 2: Creating and Hosting a Website

This week's workshop, 'Creating and Hosting a Website,' was a key starting point for my practical learning. It helped me move from just learning code to actually "publishing a website online".

Key Learnings and Deployment:

  • My biggest learning was definitely mastering the "publishing pipeline". I successfully activated my `leedsnewmedia.net` hosting account and learned how to use "SFTP (like FileZilla)".
  • This tool acts as a bridge, linking my computer files to the public "public_html" folder on the remote server.

Code Practice and Standards:

  • Regarding the code itself, I focused on basic HTML structure. I practiced "semantic organization" by using correct "heading levels" (H1, H2, etc.).
  • I also learned the simple but vital step of using the `` tag to connect my HTML file with an "external CSS stylesheet".

Insights and Conclusion:

At the beginning, setting up the FTP connection and making sure all file paths were exactly right was a bit confusing. But the feeling of success when I saw my simple page live online for the first time was huge.

Overall, the main lesson this week is clear: a website must be not only written well but also successfully uploaded and stable. This project has immediately changed from just homework into the start of my "continuous online portfolio", which I will use to show my digital media skills over the course.

Week 3: Web Scraping

Key Insights on Data Extraction:

  • My primary learning was grasping that "Web Scraping" is the process of identifying, collecting, and saving specific data from a website's underlying code. This skill is crucial for converting non-structural web information into structured data for digital media research.
  • The successful scraping process relies heavily on the "consistency of the HTML/CSS structure". This highlighted the deep connection between web design and data analysis.

Reflections on Tools:

  • The "WebScraper.io Chrome extension" proved to be an excellent tool for quick entry, offering a low technical barrier and a visual selector. It effectively served as a learning platform to understand "selector logic" and the concept of data transformation.

Conclusion:

Week 3 functioned as a "dual-directional learning experience". While Week 2 focused on publishing digital content "to" the web, this week taught me how to systematically and analytically "retrieve" data "from" the web.

Week 4: Data and Data Analysis

This week’s course shifted our focus from coding and content publishing to data itself—specifically, how to design and examine the data collection process from a critical perspective.

Shifting Perspectives: World-Building

Our group task was to design a survey based on a company-led scenario, aiming to find market gaps in how students use AI to create web pages. This exercise taught me that data collection is never neutral; we had to constantly align our questions with the "commercial goal" of developing new products.

  • "Clarity of Purpose": Every choice we made in the survey was shaping a specific picture of user needs, directly influencing the final product design.
  • Understanding "World-Building": As emphasized in the course, designing a survey is an act of "world-building". Thinking in this outcome-oriented way was more challenging than simply writing code.

Practical and Critical Challenges

While designing the questionnaire, we faced complex considerations that reinforced the importance of critical and ethical thinking:

  • "Constraints of Quantification": We were required to collect at least five "numerical variables". This pushed us to translate subjective experiences (like 'discipline' or 'frequency of use') into measurable data using defined scales (such as Likert scales).
  • "Awareness of Ethics and Data Gaps": Discussions covered ethical issues (like informed consent) and selection bias. We realized that focusing only on certain majors might misrepresent the actual situation of the broader student population.
  • "Asking Beyond Surface-Level": To find valuable market gaps, we learned that simply asking whether students use AI is insufficient. We needed to dig deeper: What are their "biggest challenges"? What can’t current AI tools do? These pain points define new opportunities.

Key Insight: The Responsibility of Data Design

In summary, Week 4 marked a significant shift from a "technical mindset" to a "data mindset." We didn’t just learn how to design a survey—more importantly, we learned that data collection is a "critical and ethically responsible practice". Every piece of data recorded reflects the purpose and perspective of the collector. As digital media students, we must be aware of this power and ensure our data design is fair, transparent, and considers its potential impact.

Sample Survey Questions:

  1. “What level of education are you in right now?” (Single choice: High school, Undergraduate, Graduate student, Others)
  2. “What's your discipline?”
  3. “How often do you need to create websites for your studies or projects?” (Scale: Very often, Sometimes, Rarely, Never)

Week 5 Data Visualisation

This week's course advanced our data practice journey, shifting focus from questionnaire design to exploring collected data through visualisation.

Reflecting on Last Week's Dataset

Before beginning visualisation, we revisited the dataset created in Week 4. This reflection built a crucial bridge between data collection and interpretation:

  • "Challenges in data collection": We discussed difficulties in questionnaire design, such as ensuring unbiased questions and quantifiable variables.
  • "Learning objectives": We reflected on what we hoped to learn from the data and whether collected responses successfully met our initial goals.
  • "Potential improvements": We considered what we could have done differently with more resources, such as expanding sample size or reaching more diverse student groups.
  • "Data and power": This session reinforced that "data is never neutral." How we collect and organise data directly influences which stories get told and which remain hidden.

The Role of Visualisation in Research

Data visualisation extends beyond creating charts - it constitutes a research method in itself. It helps reveal patterns and relationships hard to detect in raw data. Through exploratory visualisation, we can form hypotheses and identify trends before conducting statistical tests.

Audience-Centred Design

A key takeaway was developing "audience awareness." We were guided to consider:

  • Who are we visualising for?
  • What should they think, feel, or do after viewing?
  • What key messages must they understand to take these actions?

In summary, Week 5 helped me recognise data visualisation as a form of "critical storytelling". Excellent visualisation does more than display numbers - it guides audiences toward understanding and action.