Personalized Learning & Adaptive Education Dataset – Description
This dataset contains 500 records of students enrolled in online courses, capturing their demographic details, learning behaviors, engagement levels, and performance metrics. It is useful for analyzing student engagement, predicting dropout rates, and optimizing adaptive learning experiences.
Get Dataset Here : https://github.com/slidescope/data/blob/master/Personalized_Learning_DatasetSS.csv
Column Descriptions
| Column Name | Description |
|---|---|
| Student_ID | Unique identifier for each student (e.g., S0001, S0002). |
| Age | Student’s age, ranging from 15 to 50 years. |
| Gender | Gender of the student (Male, Female, Other). |
| Education_Level | The highest education level attained (High School, Undergraduate, Postgraduate). |
| Course_Name | The online course the student is enrolled in (e.g., Machine Learning, Python Basics). |
| Time_Spent_on_Videos (mins) | Total minutes spent watching educational videos. |
| Quiz_Attempts | Number of times a student attempts quizzes. |
| Quiz_Scores (%) | Percentage score achieved in quizzes (ranges from 40% to 100%). |
| Forum_Participation (posts) | Number of posts or discussions participated in the course forum. |
| Assignment_Completion_Rate (%) | Percentage of assignments completed by the student. |
| Engagement_Level | Student engagement level categorized as Low, Medium, or High. |
| Final_Exam_Score (%) | Percentage score in the final exam (ranges from 30% to 100%). |
| Learning_Style | Student’s preferred learning style (Visual, Auditory, Reading/Writing, Kinesthetic). |
| Feedback_Score (1-5) | Student’s rating of the course (1: Poor, 5: Excellent). |
| Dropout_Likelihood (Yes/No) | Whether the student is likely to drop out of the course (Yes/No). |
Use Cases of This Dataset
- Student Performance Prediction
- Identify students who are at risk of failing based on engagement and assessment scores.
- Predict final exam scores using video-watching time, quiz performance, and assignment completion.
- Dropout Prediction & Retention Strategies
- Analyze factors influencing student dropout rates.
- Implement personalized interventions for at-risk students based on engagement and learning styles.
- Adaptive Learning & Course Optimization
- Customize content recommendations based on individual learning styles.
- Adjust course difficulty based on quiz attempts and assignment completion rates.
- Engagement & Behavior Analysis
- Understand the impact of forum participation on student performance.
- Measure the correlation between video-watching time and final exam scores.
- Feedback-Driven Course Improvement
- Use feedback scores to identify courses needing improvement.
- Analyze common characteristics of highly engaged students to design better learning strategies.