Educational Data Mining: Using Machine Learning to Predict Students' Academic Performance

This project focuses on educational data mining. Using a dataset of Portuguese middle school students, it analyzes multi-dimensional factors affecting students' grades through machine learning algorithms such as linear regression, random forest, and SVM, and achieves early prediction of final grades to provide data support for educational interventions. The project aims to help educational institutions identify at-risk students, improve educational quality, and increase student retention rates.

Student dropout rate in higher education institutions is a key concern for education managers. The first year of undergraduate study is the peak period for student dropout (the 'critical year of success or failure'). Early grade prediction can help monitor learning progress, identify at-risk groups, and provide a basis for intervention. This project uses student data from two Portuguese middle schools and applies machine learning techniques to model and predict final academic grades.

The dataset contains multi-dimensional information of 396 Portuguese middle school students, covering mathematics and Portuguese subjects. Feature types include:

• Basic student information: school, gender, age, residence type, family size
• Family background features: parents' cohabitation status, education level, occupation, guardian
• Learning behavior features: commute time, weekly study time, number of past failures, extracurricular activities, going-out frequency
• Target variables: G1 (first semester grade), G2 (second semester grade), G3 (final academic year grade) It is worth noting that G3 has a strong correlation with G1 and G2. Predicting G3 without using the first two semesters' grades is more challenging and practical.

The project analyzes the following key questions:

1. Does age affect final grades?
2. Urban-rural difference: Do urban students perform better than rural students?
3. Impact of past failures: The correlation between the number of historical failures and final grades
4. Family education background: The impact of parents' education level on students' grades
5. Higher education intention: The relationship between the willingness to continue higher education and grades
6. Social activities: The balance between going-out frequency and academic performance

The project uses multiple machine learning algorithms:

• Regression models: Linear regression (baseline model), Elastic Net regression (to handle multicollinearity)
• Tree models: Random forest (improves stability via ensemble decision trees), Extra Trees (increases randomness), Gradient Boosting (trains weak learners sequentially)
• Other algorithms: Support Vector Machine (finds optimal classification hyperplane), Baseline model (for comparative evaluation)

The project uses various visualization techniques to explore data:

• Distribution analysis: KDE plot (probability distribution), box plot (outliers and distribution range), histogram (G3 grade distribution)
• Category comparison: Count plot (number of male/female and urban/rural students), grouped count plot (gender distribution across age groups)
• Relationship exploration: Relationships between age and grades, urban-rural difference and grades, number of past failures and G3, family education background and grades, higher education intention and grades, social activity frequency and academic performance

Practical significance of the project results:

• For students: Early awareness of academic risks, adjustment of learning strategies, and seeking additional tutoring
• For teachers: Identifying students who need attention, formulating personalized teaching plans, and improving retention rates through early intervention
• For educational institutions: Optimizing resource allocation, improving retention rates, and providing data support for educational policies

Student grade prediction is an important application of educational data mining, which can early identify at-risk students and provide a time window for intervention. The value of the project lies in revealing the complex network of factors affecting grades (family background, learning behavior, etc.). Future exploration directions:

• Introduce real-time learning behavior data (such as online platform logs)
• Try deep learning models
• Develop more interpretable models
• Build a real-time warning system to dynamically monitor students' status The ultimate goal of educational data mining is to let technology serve education and help students get opportunities to succeed.