Student-Progress-Analysis-A-Web-Based-Approach-for-Academic-Performance-Visualization

1. Student Progress Analysis:
A Web-Based Approach for
Academic Performance
Visualization
Priyanshu Chaudhary
Mangalayatan University, Aligarh
April 17, 2025

2. Introduction
This presentation details the development of a web-based platform.
The objective is to track and visualize student academic progress.
This facilitates data-driven decisions for educators. It empowers students to take charge of their learning.
Data Tracking
Visualization
Empowerment

3. Literature Review (1/2)
Existing tools such as Strides have demonstrated positive impacts on student behavior.
Piedmont City Schools successfully enhanced math proficiency by using data-driven strategies during the COVID-19
pandemic.
Strides
A gamified app that monitors attendance, GPA, and
engagement metrics.
Piedmont City Schools
Successfully improved math proficiency rates during the
pandemic through data analysis.

4. Literature Review (2/2)
Schools analyzing data can tailor educational strategies.
Graphical representations enhance understanding of academic progress.
1
Analyze Data
2
Tailor Strategies
3
Enhance Understanding

5. Methodology
The platform uses Streamlit for the frontend and Python for the backend.
MySQL and SQL are used for the database.
Frontend
Streamlit
Backend
Python and its Libraries
(Matplotlib, Pandas, NumPy)
Database
MySQL, SQL

6. System Design
The architecture follows a client-server model.
RESTful APIs are used for data interaction.
Admin, teacher, and student roles are defined.
1
2
3
Admin
Manages data
Teacher
Inputs grades
Student
Views progress

7. Data Analysis & Results
Academic performance metrics and attendance records are used.
Line charts show grade trends. Bar graphs display subject-wise
performance.
Grade Trends Subject Performance

8. Discussion
There is a correlation between attendance and academic performance.
Subjects requiring additional support can be identified.
This informs decision-making for educators and personalizes learning.
1 Personalized Learning
2 Informed Decisions
3 Identified Subjects

9. Conclusion
We developed a platform for tracking and analyzing student
progress.
Future work will integrate AI for predictive analytics.
AI can provide personalized learning recommendations.
1 Comprehensive Platform
2 AI Integration
3 Personalized Recommendations

10. Thank You
Any Questions?