Student Name: name Student Email: email
Reflection is a key activity of learning. It helps you build a strong metacognition, or "understanding of your own learning." A good learner not only "knows what they know", but they "know what they don't know", too. Learning to reflect takes practice, but if your goal is to become a self-directed learner where you can teach yourself things, reflection is imperative.
- Now that you've completed the assignment, share your throughts. What did you learn? What confuses you? Where did you struggle? Where might you need more practice?
- A good reflection is: specific as possible, uses the terminology of the problem domain (what was learned in class / through readings), and is actionable (you can pursue next steps, or be aided in the pursuit). That last part is what will make you a self-directed learner.
- Flex your recall muscles. You might have to review class notes / assigned readings to write your reflection and get the terminology correct.
- Your reflection is for you. Yes I make you write them and I read them, but you are merely practicing to become a better self-directed learner. If you read your reflection 1 week later, does what you wrote advance your learning?
Examples:
- Poor Reflection: "I don't understand loops."
Better Reflection: "I don't undersand how the while loop exits."
Best Reflection: "I struggle writing the proper exit conditions on a while loop." It's actionable: You can practice this, google it, ask Chat GPT to explain it, etc. - Poor Reflection "I learned loops."
Better Reflection "I learned how to write while loops and their difference from for loops."
Best Reflection "I learned when to use while vs for loops. While loops are for sentiel-controlled values (waiting for a condition to occur), vs for loops are for iterating over collections of fixed values."
--- Reflection Below This Line ---
This project forced me to return to many of the lessons we have done this semester, including visualizations, datasets, and many of the assignments we worked on. The biggest struggle I had was finding a dataset or coming up with an idea that was doable and interesting. I considered scraping the New York Times website for articles, using the Spotify API, and scraping restaurant reviews from Yelp. Ultimately, I landed on this Rotten Tomatoes dataset because I thought it already had a lot of interesting information and had some obvious choices for analysis. If I had more time, I would have liked to use an API. I tried using the Azure Keyword API to pull keywords from the movie descriptions, which I thought would be a cool additional filter, but I quickly reached my API limit and realized that wasn't feasible for this project. I learned a lot about having to clean datasets and working with outliers and errors. I also learned about plotly express plots and how to best apply them to the data I was working with. I definitely think there is more that I could do with this dataset, and I would in the future consider adding filters by directors and cast members and create visualizations that show ratings by genre, directors, cast members, etc.