Stay in School, Kids (But Do They?)

Intro

"Stay in school, kids." It's something students hear from teachers, parents, and society all the time. But do students actually stay in school? and what factors might influence whether they succeed or drop out? Moreover, how can some categories be presented in the dataset regarding bias and equality? To explore this, we looked at variables that could influence student outcomes. These factors help us understand what may contribute to a student’s success in school.

Methods

The method we used is Lindsay Poirier's methodology of denotative, connotative, and deconstructive readings in order to answer the question, "What factors might influence whether they succeed or drop out?" Moreover, how can some categories be presented in the dataset regarding bias and equality? We aimed to see what the strongest correlations were based on the dataset categories.

Work Performed

We first cleaned up the dataset to an Excel spreadsheet. After we found what data correlates best to bias and equality to make the strongest correlation for student dropout or success. Then we picked first and second semester performances, student admission grades, debt, tuition payment, and scholarship holders. Next we made multiple graphs to analyze the predictors of student graduation and dropout outcomes. Lastly, we aimed to understand the graphs and add these to our slides. 

Findings

We found that there are success predictors that can play a role in students' success rates. Moreover, students who graduated had slightly higher admission grades on average than students who dropped out. This suggests that students with higher admission grades may have a better chance of succeeding in school. Lots of factors play a role, such as first and second semester performances, student admission grades, debt, tuition payment, and scholarship holders. However, while datasets like this help identify patterns, they also raise ethical concerns because predictive models can reinforce bias or overlook systemic barriers. Because of this, data should be used carefully to support students and improve resources rather than limit their opportunities.