This project analyzes instructor effectiveness using student engagement, performance, and feedback data. The goal is to identify key factors that influence teaching effectiveness using data analysis and machine learning.
The dataset contains various metrics related to student engagement and course performance, including:
- Completion rate
- Dropout rate
- Average score improvement
- Quiz scores
- Watch time
- Assignment submission rate
- Forum activity
- Feedback score
- Feedback response rate
The analysis includes:
- Data preprocessing and cleaning
- Exploratory Data Analysis (EDA)
- Feature engineering
- Machine learning modeling
- Model evaluation and interpretation
- Random Forest
- Gradient Boosting
- Logistic Regression
The most important factors influencing instructor effectiveness include:
- Completion Rate
- Average Score Improvement
- Assignment Submission Rate
These features strongly reflect student engagement and learning outcomes.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
Atul Kumar
B.Tech – Artificial Intelligence & Data Science
IIITDM Kurnool