Predicting Student Performance in MOOCs

Introduction

Massive Open Online Courses (MOOCs) have revolutionized education by providing accessible learning opportunities worldwide. However, high dropout rates remain a significant challenge. This project proposal focuses on developing a predictive model to forecast student performance in MOOCs, enabling timely interventions to improve retention rates.

Background

Research has demonstrated the potential of machine learning algorithms in predicting student outcomes and dropout probabilities in MOOCs. Various models, such as decision trees, random forests, and XGBoost classifiers, have been employed to analyze student data and predict performance with considerable accuracy. The study "Predictive Modeling of Dropout in MOOCs Using Machine Learning" highlights the effectiveness of these models, particularly XGBoost, which achieved high accuracy in predicting both pass/fail status and dropout likelihood[2].

Project Objective

The primary objective of this project is to create a robust predictive model using machine learning techniques to identify at-risk students early in their MOOC journey. By doing so, educational institutions can implement personalized interventions to support these students and enhance their chances of success.

Methodology

1. Data Collection and Preprocessing

• Dataset: Utilize the Open University Learning Analytics Dataset (OULAD), which contains comprehensive records of student activities, demographics, and course-related data.
• Data Preprocessing: Clean and preprocess the dataset to handle missing values and normalize features for better model performance.

2. Model Development

• Algorithm Selection: Implement various machine learning algorithms including decision trees, random forests, and XGBoost classifiers.
• Feature Engineering: Extract relevant features from the dataset such as engagement metrics, assessment scores, and demographic information.

3. Training and Evaluation

• Training: Split the dataset into training and testing sets (80:20 ratio) to ensure robust evaluation.
• Evaluation Metrics: Use metrics such as accuracy, precision, recall, and F1-score to assess model performance.

Expected Outcomes

The proposed predictive model is expected to accurately identify students at risk of dropping out or underperforming in MOOCs. By leveraging machine learning techniques, the model should provide actionable insights for educators to implement timely interventions.

Conclusion

This project aims to contribute to the field of educational data mining by developing a predictive model that enhances student retention in MOOCs. The integration of advanced machine learning algorithms will facilitate early identification of at-risk students, ultimately improving educational outcomes.

For further details on related research, please refer to the paper "Predictive Modeling of Dropout in MOOCs Using Machine Learning," available at https://ieeexplore.ieee.org/document/8489099.

Dataset link: Open University Learning Analytics Dataset (OULAD)