Students Performance Data

Introduction

In this project, we’re exploring the Student Performance Dataset, which includes information about over 2,300 high school students—their backgrounds, study habits, and academic results. Especially focused on how things like how much time students spend studying, whether they take part in activities outside of class, and how involved their parents are can affect their GPA and grades. Our big question is: What can this data tell us about the kinds of knowledge, habits, and support that actually help students do well, and are those benefits available to everyone equally? Behind this, there's a bigger issue: how data shapes the way we define "success" in education, and whether that definition is fair to all students.

Methods

Reading Datasets: I examined the relationships among variables such as StudyTime, ParentalSupport, Extracurricular, and GPA. I then used my findings to identify patterns and potential disparities in performance across gender, ethnicity, and parental education levels.

Format Anatomies: I examined the dataset’s structure, particularly focusing on which variables were included and which were omitted. I then started to come up with other categories that might play a factor such as, emotional state, peer influence, and whether if they had a good teacher or not.

Work Performed

I went through and checked the data set for any missing/ incorrect information, making sure everything was in its place, which it was. I then took a closer look at key variables like study time, parental support, and GPA to understand how these factors relate. Averaging out each category respectively, I found that students studied ~9.8 hours (range of 0-20), parental support was only ~2.2 or "moderate" on a 1-4 scale, and GPA hovered around ~1.91, well below the midpoint of the 4.0 scale. That said, some students far exceeded the average. These statistics helped me to see general patterns in how students spend their time and how much support they receive, offering a foundation for analyzing how these trends might connect to their academic success. 

Findings

Through my analysis, I found that students who studied more hours per week and had greater parental support, generally earned higher GPAs. For example, while studying 10–15 hours weekly tends to boost GPA, going beyond that doesn’t always lead to better grades. Students who had involved parents and regularly took part in extracurriculars like music or volunteering often performed better, suggesting that support outside the classroom matters. The biggest direct correlation had to be at a number of absences to GPA, consistently, the more a student missed,d the lower their respective GPA. However, I'd also noticed differences stemming from gender and ethnicity. Students from some demographic groups had lower average GPAs and less access to supportive resources, revealing deeper issues of inequality. The format anatomy analysis helped to realize that this dataset focuses mostly on what’s easy to measure, like study time and participation, but ignores important emotional, social, and structural factors like mental health, quality of instruction, or peer influence. So while the data can point us in the right direction, it also risks oversimplifying what student success really means.