This project examined the determinants of student performance, analyzing demographic data and exam scores to identify improvement strategies for education. For further details, please refer to the Kaggle Notebook linked here.

Student Study Performance Analysis

Introduction

Welcome to my Student Performance Analysis Project! In this project, I aimed to explore and understand the factors influencing student academic performance. By analyzing a dataset containing information on students’ demographics and exam scores, I sought to uncover insights that could inform strategies for improving educational outcomes.

Dataset Overview

The dataset used in this project consists of students’ performance in math, reading, and writing, along with demographic variables such as gender, ethnicity, parental level of education, lunch type, and completion of a test preparation course.

Dataset Columns:

• gender: Sex of the student (Male/Female)
• race_ethnicity: Ethnicity of the student (Group A, B, C, D, E)
• parental_level_of_education: Highest level of education attained by the student’s parents
• lunch: Type of lunch received by the student (Standard or Free/Reduced)
• test_preparation_course: Whether the student completed a test preparation course (Complete/None)
• math_score: Score obtained in the math subject
• reading_score: Score obtained in the reading subject
• writing_score: Score obtained in the writing subject

Project Steps and Findings

Step 1: Data Exploration and Preparation

• Loaded the dataset into R and explored its structure, summary statistics, missing values, outliers and duplicates.

Step 2: Exploratory Data Analysis (EDA)

• Conducted univariate and bivariate analysis to explore relationships between variables.
• Investigated correlations and compared distributions across different demographic groups.

Step 3: Gender Analysis

• Analyzed performance differences between male and female students.
• Found significant differences across subjects, with males excelling in math and females in reading and writing.

Step 4: Ethnicity Analysis

• Examined performance differences among students from different ethnic groups.
• Identified significant variations across groups, with group E performing the best and group A the worst.

Step 5: Parental Level of Education Analysis

• Investigated the influence of parental education on student performance.
• Found significant impacts across all subjects, with higher-educated parents associated with better academic performance.

Step 6: Lunch Analysis

• Explored the impact of lunch type on student performance.
• Discovered significant differences between students with standard and free/reduced lunch, with standard lunch associated with better performance.

Step 7: Test Preparation Course Analysis

• Analyzed the effect of completing a test preparation course on student performance.
• Found significant differences, with students completing the course achieving higher exam scores.

Conclusion

This portfolio project provided valuable insights into the factors influencing student academic performance. By understanding the impacts of gender, ethnicity, parental education, lunch type, and test preparation courses, we can develop targeted interventions to support students and improve educational outcomes.

Thank you for exploring this project with me!

Author

• LinkedIn: Mohammed Mebarek Mecheter
• Email: mohammedmecheter@gmail.com
• GitHub: Mohammed Mebarek Mecheter