This project analyzes how the Student Performance Dataset, constructed of high school students' personal information and GPA based variables, is displayed as numerical values. Instead of using the dataset to predict GPA or the likelihood of academic success, we should exemplify how the data variables and categories define limits, format, and what courses are relevant to measurable student activityUsing Koopmans' concepts of format anatomy, we work to analyze how the structure of spreadsheets, rows, columns, and other variables shape, influence, and constrain complex educational and personal variables into quantifiable data points. Through the Poiriers dataset reading method, we can interpret how variables such as gender, ethnicity, extracurricular activities, and study habits encourage responsibility and success.
This project analyzes how the Student Performance Dataset, constructed of high school students' personal information and GPA based variables, is displayed as numerical values. Instead of using the dataset to predict GPA or the likelihood of academic success, we should exemplify how the data variables and categories define limits, format, and what courses are relevant to measurable student activity and history.
Using Koopmans' concepts of format anatomy, we work to analyze how the structure of spreadsheets, rows, columns, and other variables shape, influence, and constrain complex educational and personal variables into quantifiable data points. Through the Poiriers dataset reading method, we can interpret how variables such as gender, ethnicity, extracurricular activities, and study habits encourage responsibility and success.
Our centralized question for this project is: How do the standardized formats and variable categories used in the Student Performance Dataset (extracurricular activities, study habits, and other home, personal and school life variables) construct particular information about a person, and what forms of identity, bias, or lived experience are excluded, simplified, or unseen, or distorted by the dataset’s technical design? Additional question: Can we predict students' personal and GPA based on variables such as study habits, extracurricular activities, academic performance, ethnicity, age, gender, parental education level, other methods of support, and details about the student's school and home life? How?
While investigating this dataset, it quickly became clear how seemingly neutral educational data and statistics can reproduce and encourage unspoken assumptions or beliefs about groups of people based on biology, identity variables, achievements, and failures.
Analyzing how the Student Performance Dataset on personal information of high school students and GPA based variables. Using Koopmans' concepts of format anatomy, we work to analyze how the structure of spreadsheets, rows, columns, and other variables shape, influence, and constrain complex educational and personal variables into quantifiable data points. Through the Poiriers dataset reading method, we can interpret how variables such as gender, ethnicity, extracurricular activities, and study habits encourage responsibility and success. While investigating this dataset, it quickly became clear how seemingly neutral educational data and statistics can reproduce and encourage unspoken assumptions or beliefs about groups of people based on biology, identity variables, achievements, and failures.