| dc.description.abstract | In the context of private education, students
face challenges in selecting suitable courses from an ever-
expanding curriculum, often leading to confusion and
suboptimal academic decisions. This study introduces an
AI-powered course recommendation system specifically
designed to assist students in private educational
institutions in Sri Lanka. By leveraging machine learning
algorithms, particularly content-based and collaborative
filtering techniques, along with data prediction algorithms
such as decision trees and regression models, the system
processes student data
including academic performance,
interests, and current course enrollments
to generate
personalized course recommendations. The methodology
involves comprehensive data collection, preprocessing,
and the development of models that are trained and
validated against real-world educational data. The
system's performance has shown a marked improvement in
aligning course selections with student preferences,
resulting in enhanced satisfaction and academic outcomes.
The study also discusses the implications of integrating AI
and predictive analytics in educational decision-making,
emphasizing the potential to improve student guidance and
success rates. Future work will focus on the development
of an accessible user interface and the exploration of the
system's adaptability across different educational contexts.
The proposed system aims to support educators and
students by streamlining the course selection process,
ultimately transforming the educational experience in
private institutions. | en_US |
