Predicting Student Performance Using Machine Learning Techniques

Introduction

Predicting student performance is a crucial area of research in educational data mining, aiming to identify students at risk of underperforming and provide timely interventions. This project proposal presents a machine learning-based approach to predict student outcomes, leveraging insights from recent studies in the field.

Background

Recent advancements in machine learning have significantly improved the ability to analyze educational data and predict student performance. By utilizing various algorithms such as decision trees, support vector machines (SVM), and neural networks, researchers have been able to uncover patterns and trends that are indicative of student success. These models typically use features derived from academic records, demographic information, and behavioral data.

Project Objective

The primary objective of this project is to develop a predictive model that accurately forecasts student performance. The model aims to assist educators in identifying students who may need additional support, thereby enhancing overall educational outcomes.

Methodology

1. Data Collection and Preprocessing

• Datasets: Use publicly available datasets such as the UCI Machine Learning Repository's Student Performance dataset.
• Data Cleaning: Handle missing values, normalize data, and encode categorical variables to prepare the dataset for analysis.

2. Model Development

• Algorithm Selection: Evaluate various machine learning algorithms including decision trees, random forests, SVMs, and neural networks to determine the best fit for the data.
• Feature Selection: Identify key features that contribute most significantly to predicting student performance using techniques like recursive feature elimination.

3. Training and Evaluation

• Training: Split the dataset into training and testing sets to train the model using appropriate metrics such as accuracy and mean squared error.
• Evaluation Metrics: Assess model performance using cross-validation and metrics like precision, recall, F1-score, and ROC-AUC.

Expected Outcomes

The proposed system is expected to provide educators with a reliable tool for predicting student performance. By integrating machine learning techniques, the system should offer insights into factors influencing student success and help tailor educational strategies accordingly.

Conclusion

This project aims to advance the use of machine learning in education by developing a robust predictive model for student performance. The integration of diverse algorithms and feature selection techniques is anticipated to yield significant improvements in prediction accuracy.

For further details on related research, please refer to the paper "Predicting Student Performance Using Machine Learning Techniques," available at ieeexplore.ieee.org/document/8944954.

For dataset access, please visit the UCI Machine Learning Repository's Student Performance dataset.