The main aim of this project is to move beyond the one-size-fits-all approach in education by building a personalized education platform that adapts learning content to each student's unique needs. Using decision trees and CART (Classification and Regression Tree) analysis, the system identifies the student's learning style, pace, strengths, and weaknesses. It then dynamically adjusts the learning plan to focus on areas that need improvement while reinforcing existing knowledge. The outcome is a system that delivers personalized educational content and support, resulting in improved engagement and academic performance. This project not only enhances student learning outcomes but also equips future developers with experience in building intelligent, human-centered educational software.
The project will be implemented over a structured twelve-week period. Students will begin by studying the fundamentals of decision trees and CART algorithms and their applications in adaptive learning. A dataset containing learning profiles, topic-wise performance, or interaction histories will be collected or simulated. Next, the system's framework will be built using Python, along with machine learning libraries such as scikit-learn.
The model will be trained to identify and adapt to individual learner patterns. During the middle stages, students will develop conversational and recommendation logic to simulate personalized tutoring interactions. Testing will involve validating how well the system adjusts learning content for different users. In the final stages, the project will be refined, documented, and presented as a working prototype. Ethical design, consistent documentation, and a focus on fairness and transparency in model behavior are expected throughout the development process.
