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https://github.com/elachaabane/educational-performance-analysis
analyse student exam performance using nonparametric statistical methods
https://github.com/elachaabane/educational-performance-analysis
dunn-s-test kruskal-algorithm nonparametric r spearman-correlation statistical-analysis student-performance
Last synced: 10 days ago
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analyse student exam performance using nonparametric statistical methods
- Host: GitHub
- URL: https://github.com/elachaabane/educational-performance-analysis
- Owner: ElaChaabane
- Created: 2024-12-25T12:49:51.000Z (18 days ago)
- Default Branch: main
- Last Pushed: 2024-12-25T12:59:44.000Z (18 days ago)
- Last Synced: 2025-01-02T12:12:21.645Z (10 days ago)
- Topics: dunn-s-test, kruskal-algorithm, nonparametric, r, spearman-correlation, statistical-analysis, student-performance
- Language: R
- Homepage:
- Size: 153 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Educational Performance Analysis
## Project Overview
This project analyzes factors influencing student exam performance using nonparametric statistical methods. Based on the research paper "Simplifying Statistical Decision Making: A Research Scholar's Guide to Parametric and Non-Parametric Methods" by Pirani (2024) doi:10.56815/IJMRR.V3I3.2024/184-192 , we implement a comprehensive analysis following the CRISP-DM methodology.
## Table of Contents
- [Business Understanding](#business-understanding)
- [Data Understanding](#data-understanding)
- [Data Preparation](#data-preparation)
- [Statistical Method Selection](#statistical-method-selection)
- [Modeling](#modeling)
- [Evaluation](#evaluation)
- [Dependencies](#dependencies)
- [Installation](#installation)
- [Usage](#usage)
## Business Understanding
The goal is to identify key factors affecting student exam performance to help educators and administrators prioritize interventions and resource allocation.
### Target Variable
- `Exam_Score`: Final exam performance
### Features
#### Numeric Variables
- `Hours_Studied`: Study hours allocated
- `Attendance`: Class attendance percentage
- `Sleep_Hours`: Average daily sleep
- `Previous_Scores`: Prior assessment scores
- `Tutoring_Sessions`: Extra tutoring count
- `Physical_Activity`: Weekly physical activity hours
#### Categorical Variables
- Parental Involvement
- Access to Resources
- Motivation Level
- Family Income
- Extracurricular Activities
- School Type
- Gender
- And others...
## Data Preparation
### Outlier Treatment
- Used IQR method (1.5 × IQR rule)
- Implemented boxplot visualization
- Treated outliers by replacing with NA values
### Missing Value Treatment
- Utilized KNN imputation (k=5)
- Visualized missing data patterns
- Handled both numeric and categorical variables
## Statistical Method Selection
### Normality Testing
1. Visual inspection:
- Histograms with density plots
- Q-Q plots
2. Statistical testing using Jarque-Bera test:
- H0: Data follows normal distribution
- H1: Data does not follow normal distribution
- Results: p-values < 0.05 for most variables:
- Hours_Studied: p = 0.003
- Attendance: p = 0.001
- Sleep_Hours: p = 0.002
- Previous_Scores: p = 0.004
- Exam_Score: p = 0.001
- Only Physical_Activity showed p > 0.05 (p = 0.067)
### Choice of Nonparametric Methods
Based on normality test results (p < 0.05), we rejected the null hypothesis of normality and selected nonparametric methods for analysis:
1. Spearman correlation instead of Pearson
2. Kruskal-Wallis test instead of ANOVA
3. Dunn's test for post-hoc analysis
## Modeling
Implemented nonparametric methods:
1. Correlation Analysis:
- Spearman correlation for numeric variables
- Correlation matrix visualization
2. Categorical Analysis:
- Kruskal-Wallis tests for variables with >2 groups
- Dunn's test with Bonferroni correction for pairwise comparisons
- Selection criteria: p < 0.05 for statistical significance
## Key Findings
1. Strongest predictors of exam performance:
- Attendance (ρ = 0.69)
- Hours studied (ρ = 0.49)
- Parental involvement (Kruskal-Wallis p < 0.001)
2. Moderate impact factors:
- Previous scores (ρ = 0.19)
- Family income (Kruskal-Wallis p < 0.001)
- Access to resources (Kruskal-Wallis p < 0.001)
3. Non-significant factors:
- Gender (Kruskal-Wallis p = 0.4548)
- School type (Kruskal-Wallis p = 0.3271)
## Dependencies
```R
library(ggplot2) # Visualization
library(gridExtra) # Multiple plot arrangement
library(dplyr) # Data manipulation
library(tidyr) # Data reshaping
library(rstatix) # Statistical tests
library(corrplot) # Correlation visualization
library(tseries) # Jarque-Bera test
library(VIM) # KNN imputation
```
## Installation
1. Clone this repository
2. Install R and RStudio
3. Install required packages:
```R
install.packages(c("ggplot2", "gridExtra", "dplyr", "tidyr",
"rstatix", "corrplot", "tseries", "VIM"))