Blog 7 – Final Project Proposal

Group Members: Alex, Lucas, Jiao Jiao, Shalim, Jeremy

Topic: Acceptance Rate (First Year Admission) From 1999-2024 as well as the total number of applicants. We aim to explore the changes in these trends, as well as possible reasons for these changes.

Methodology:

  • Sources: Common Data Set (CDS) – Institutional Research and Assessment – Carleton College from 1999-2024
  • Processes: Each member takes 5 years. Scraping data and manually inputting them into spreadsheets (or using AI to automate). Once the data collection is done we will start analyzing the data and then start creating our presentation. We will use tools like Flourish or other data visualization software.
  • Presentation: We will present our project on a website with various graphs and explanations of why we chose to present the data in the way that we did. We may add other tabs to our site for data visualizations depending on which data points we choose to include.

Timeline:

  • Week 8: Data collection and cleaning
  • Week 9: Data analysis and start presentation
  • Week 10: Finish presentation, submit, and present to class

Example DH Projects: Carls Around the World & Carleton’s Major Majors

Our messages:

Alex – This project brings me back to when I started applying for colleges. I remember feeling pressured by seeing the acceptance rates, and this notion that the lower rates correlate to better schools. As a college student now, I am interested to see how these rates have changed over the past couple of decades, and what could have caused these shifts. I have enjoyed making graphs and visualizing data in this class so it should be fun.

Lucas – As everyone in my grade started applying to colleges, everyone kept talking about which schools they were applying to, always talking about the different acceptance rates, and bragging about getting into schools with lower acceptance rates than others. Many people used this as a measure of how prestigious and good a school was instead of considering what the school actually had to offer. Since I am at Carleton now, I think it would be interesting to see how the acceptance rates have varied in the last couple of decades.

Jiao Jiao – While growing up and going through the college application process, my family greatly stressed the importance of the acceptance rates of the colleges I was applying to. It is not something I think about day to day, but it is something that crosses my mind when new students are admitted or when I hear about students in the years below me starting the college application process. I think it would also be interesting to see if there are any dips or rises in acceptance rates that correspond with larger national or world events.

Shalim – This topic allows for the use of graphs to visualize data which has been my favorite tool thus far. I am interested in using dynamic graphs to model change over time and highlight trends in the data. I also think it would just be interesting to see how many people have applied and been admitted to Carleton in the past 25 years.

Jeremy – As an international student, when I first applied to colleges in the US, the first thing I saw was each college’s acceptance rate. There was this assumption that the lower the acceptance rate the better the school. And so as a current Carleton student, I’m always curious about Carleton’s acceptance rate.

1 thought on “Blog 7 – Final Project Proposal

  1. Team Acceptance Rates,

    I like this concept and it looks like you’ve got plenty of data to work with in the CDS reports. I’m curious why you are focused solely on acceptance rates, however. Won’t other data be needed to interpret any patterns you find there? What are your hypotheses for why rates may have changed over time? Can you test those with other data points in the report?

    You also mention comparisons to other schools in your personal statements. Are there any national data sources you could look at for context? Are the acceptance rate data for Carleton already available there? If so, what can you extract from these reports that is not to add to the conversation?

    For extracting the data, there are a number of lessons on the Programming Historian for working with OCR and extracting data from PDFs. I would suggested perusing those, and trying a few different ones either by following the lessons, or working with an AI to peform the steps. If different team members attempt different strategies, that will be a great thing to discuss in your methods section.

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