Cloud-Based Student Performance Analytics System Using Predictive Data Models

The objective of this project is to design a cloud-based analytics platform that collects, processes, and analyzes student academic data to predict performance trends. The system leverages cloud infrastructure for scalability and machine learning techniques for predictive insights.

Study existing student performance analysis systems and identify their limitations in scalability and accuracy.

Design system architecture using cloud computing concepts such as virtualization and distributed storage.

Collect and preprocess academic datasets including attendance, grades, and internal assessments.

Implement cloud-based data storage using services like AWS S3 or Google Cloud Storage.

Develop analytical models to evaluate academic performance trends using machine learning algorithms.

Create predictive models to identify students at risk of poor performance.

Build a web-based dashboard to visualize analytical results using charts and reports.

Implement secure authentication and role-based access for students, teachers, and administrators.

Test system performance for large datasets to evaluate scalability and response time.

Document system design, implementation details, results, and future enhancement possibilities.