Marg

I’m most proud of how much more patient I became during the writing process. Instead of rushing to finish everything at once, I spent more time revising, reorganizing, and thinking about how each section connected back to my main point. I also think I improved at balancing visuals, analysis, and written explanation throughout the project. Compared to earlier assignments, this process felt more intentional and less overwhelming once I broke it into smaller steps.

What I like most about my learning is realizing how much strategy goes into digital communication and content creation. Before this class, I mostly looked at social media and web content from a casual perspective, but now I pay a lot more attention to audience engagement, visuals, wording, and overall presentation. I also liked learning how data and writing can work together to tell a stronger story. It made the assignments feel more practical and connected to real world communication.

One thing I still wonder about is how professional writers balance writing for actual people while also keeping SEO and algorithms in mind. Throughout this course, I realized web writing is way more strategic than I originally thought. I’m curious how companies decide what type of content performs best without making it feel robotic or overly optimized. I also want to learn more about how writers build a personal voice online while still sounding professional and credible. I think those skills will help a lot in future careers because almost every industry relies on digital communication now. @npfannen

I need feedback on how I am interpreting my engagement data, especially when it comes to relational versus aspirational content. In my chart, I noticed relational posts had more comments but not always more likes, and I am trying to figure out if that actually means stronger community building or if I am reading too much into it. Does this seem like a fair conclusion based on the data, or is there something else I should be looking at to support that point? I want to make sure I am not just making assumptions and that my analysis actually makes sense. 

I learned that my writing process works way better when I have some kind of structure or data to guide me instead of just starting with a blank page. For this project, having my content analysis in front of me made it so much easier to figure out what I wanted to say and actually back it up. I also realized I tend to overthink in the beginning, but once I start getting ideas down it flows a lot more naturally. After college, I want to keep using outlines or data points to get started instead of waiting until I feel ready, because that usually just slows me down. 

One thing I learned about drafting across multiple weeks is that your first version is never really your best version and that is kind of the point. When I spaced my work out, I started noticing things I completely missed before, especially when it came to connecting my data back to my main ideas. It also made my writing feel less rushed and more intentional. After college, I know I will not always have weeks to draft something, but I still want to build in time to step away and come back with a fresh perspective because that honestly made the biggest difference for me.

Having a strong revision process helps me think beyond just finishing an assignment. It forces me to look at the bigger structure of what I am writing and whether it actually makes sense. That skill transfers outside this class because most professional writing is about clarity and organization. Learning to revise for logic, audience, and flow will help me in future academic projects and even in professional settings where clear communication matters.

The data elements that matter most in my space are total engagement and content category. I care about engagement because it is a visible signal of audience response. I care about aspirational versus relational coding because that connects directly to my research question. Without those two pieces working together, I would just have numbers without context. The goal is to link measurable performance to intentional content strategy decisions, not just report which videos got the most likes.

I am trying to be more intentional about sounding academic without overcomplicating my writing. I focus on being clear and precise, especially when defining categories like aspirational and relational. Instead of just describing what I see, I connect each section back to my research question. My style is developing through consistency. Clear definitions, repeated terms, and structured headings help build the argument without me having to over explain.

My final revision process is less about fixing sentences and more about checking alignment. I start by rereading my research question and hypothesis and asking if my data actually answers it. Then I review my coding definitions to make sure they are clear and replicable. After that, I look at structure and headings to see if the flow makes sense. Grammar and wording come last. For me, revision is about strengthening logic first and polishing language second.

To answer my research question, I have to compare engagement across three groups: aspirational only, relational only, and blend posts. The coding allows me to sort posts into those categories, and total engagement gives me something measurable to compare. If blend posts consistently show higher average engagement, that supports my hypothesis. If they do not, that is still meaningful. Either way, the data elements directly structure how I test the question.

One thing I am really thinking about is what actually counts as data in my space. On TikTok, data is not just numbers. Yes, I am tracking likes and comments as total engagement, but I am also treating visible content choices as data. Things like aesthetic kitchen backgrounds, direct address to the audience, vulnerability statements, or structured storytelling are all data points in my project. That impacts my analysis because it forces me to be clear about what I can actually see and code, instead of guessing about algorithm reach or hidden metrics.