Project Overview This project focuses on developing an adaptive learning system that segments students based on their interactions with online course reviews. By leveraging machine learning techniques and natural language processing (NLP), the system provides personalized learning experiences, aiming to improve student engagement and learning outcomes.
Features Student Segmentation: Implemented K-means clustering on preprocessed student interaction data, achieving a Silhouette Score of 0.5575, Calinski-Harabasz Index of 8.6742, and Davies-Bouldin Index of 0.6090. Machine Learning Model: Developed and validated a machine learning model for adaptive learning systems, achieving an AUC score of 0.9216 and a mean cross-validation accuracy of 0.8675. Performance Evaluation: Achieved a test set accuracy of 0.8550, with precision and recall scores of 0.80 and 0.91 for class 0, and 0.91 and 0.80 for class 1. Evaluated the model using a confusion matrix showing 85 true negatives, 8 false positives, 21 false negatives, and 86 true positives. Dataset The dataset contains Coursera reviews with the following columns:
reviews: The text of the review. reviewers: The ID or name of the reviewer. date_review: The date the review was posted. rating: The rating given by the reviewer. course_id: The ID of the course being reviewed. Methodology Data Collection and Preprocessing:
Handled missing values and converted date formats. Cleaned text data and performed sentiment analysis. Feature Engineering:
Extracted features such as review length and sentiment score. Aggregated review data for each reviewer. Clustering for Student Segmentation:
Scaled features and applied K-means clustering. Evaluated clustering using Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. Machine Learning Model Development:
Developed a classification model and validated it using cross-validation. Evaluated model performance using AUC score, confusion matrix, and classification report.
#Getting Started Prerequisites Python 3.7 or above Required libraries: pandas, numpy, scikit-learn, nltk, matplotlib, seaborn Installation Clone the repository: bash git clone https://github.com/hritaku/ADAPTIVE-LEARNING-SYSTEMS-AUTOMATIC-STUDENT-SEGMENTATION-BASED-ON-ONLINE-COURSE-INTERACTION.git Navigate to the project directory: bash cd adaptive-learning-system Install the required libraries: bash pip install -r requirements.txt Usage Preprocess the data: bash Copy code python preprocess_data.py Perform feature engineering: bash Copy code python feature_engineering.py Run the clustering algorithm: bash Copy code python clustering.py Develop and evaluate the machine learning model: bash Copy code python model_development.py Results Clustering achieved a Silhouette Score of 0.5575, Calinski-Harabasz Index of 8.6742, and Davies-Bouldin Index of 0.6090. The machine learning model achieved an AUC score of 0.9216 and a mean cross-validation accuracy of 0.8675. Test set accuracy was 0.8550, with detailed performance metrics provided in the classification report and confusion matrix. Future Work Dynamic Content Adaptation: Integrate real-time data to dynamically adjust learning content. Multimodal Data Integration: Incorporate additional data sources such as video engagement and forum participation. Advanced AI Techniques: Explore deep learning models for more nuanced analysis and personalization. Enhanced Personalization: Develop virtual tutors and adaptive assessments for more personalized learning experiences. Ethical Considerations: Implement robust data privacy measures and ensure fairness in algorithms. Contributing Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.
License This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments Thanks to Coursera for providing the dataset. Special thanks to all contributors and collaborators. Feel free to customize the content further based on your specific project details and requirements.