Muster: University Dashboard System with AI-Driven Analytics

Overview

Muster is a web-based university dashboard developed as a graduation project at Misr University for Science and Technology. It enhances educational decision-making by integrating academic data with AI-powered insights through customized interfaces for professors, students, and parents.

The system provides visualizations and predictive analytics on grades, attendance, assignments, and course data, leveraging cutting-edge AI techniques such as:

• Logistic Regression: Predicts student dropout risk
• LSTM-based RNN: Forecasts future GPA
• K-Means Clustering: Categorizes student performance
• Content-Based Filtering: Recommends personalized courses

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Professor Interface

Professors can manage courses, track performance, and act on predictive insights with dashboards tailored to academic engagement:

• Course Management: View/manage course data including enrollment and schedules.
• Attendance Dashboard: Charts for attendance rates by course and section type.
• Grades Dashboard: Searchable tables for all types of assessments.
• Assignments Dashboard: Track student submissions and grades.
• Predictive Analytics:
  • Dropout and failure risks using logistic regression.
  • GPA predictions using LSTM-RNN (79.6% accuracy).
  • Performance clustering (high, average, at-risk).
• Student & Course Metrics: Aggregated views for strategic decisions.

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Student Interface

Students receive a personalized dashboard to monitor academic progress and receive AI-backed recommendations:

• Grade Summary: Breakdown of grades by semester/course.
• Assignment Tracker: Completion charts and score trends.
• Attendance Record: Weekly and overall attendance visualizations.
• Course Recommendations: AI-based elective suggestions tailored to strengths and progress.
• Course Details: Full info on enrolled courses and schedules.

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Parent Interface

Parents are offered an intuitive dashboard to stay engaged with their child’s academic journey:

• Grade Summary: View course grades by semester.
• Assignment Completion: See pending/submitted assignments and deadlines.
• Class Attendance Rate: Charts showing attendance performance.
• Child Profile: Overview of academic major, year, and key metrics.

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Technical Details

Technologies

• Backend:

  • PHP Laravel (core logic)
  • Flask API (AI model integration)
  • MySQL (data storage)

• Frontend:

  • JavaScript + Bootstrap (responsive UI)
  • Chart.js (interactive charts)

• AI Models:

  • Python with scikit-learn and TensorFlow
    • Logistic Regression (Dropout prediction)
    • K-means Clustering (Performance segmentation)
    • Content-Based Filtering (Course suggestions)
    • LSTM RNN (GPA forecasting)

• Data:

  • Synthetic datasets generated using Laravel Faker (users, grades, courses, assignments, etc.)

Architecture

• Modular, role-based structure with secure authentication
• Laravel handles user/session logic and calls Flask for AI predictions
• Dashboards rendered dynamically using Chart.js and AJAX requests

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Testing and Validation

• Security & Auth: Tested using PHPUnit, Selenium, and OWASP ZAP
• AI Models:
  • Logistic Regression: 99% accuracy on synthetic data
  • LSTM GPA Predictor: 79.6% accuracy (±0.3 GPA points)
• Interface: Functional testing for dashboard filters, chart responsiveness, and data accuracy

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Conclusion

Muster redefines academic monitoring through AI-powered dashboards, enabling proactive decisions and personalized learning. Built with scalability, privacy, and usability in mind, it's adaptable for universities worldwide.

Future Enhancements:

• Mobile app version
• Real-time data support
• Expanded AI features (e.g., behavioral analysis, anomaly detection)

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