Educator
Input student data to generate performance predictions
Responsible for entering and managing student data
LandingLoginDashboard
agile-student
by
Rahul Vasi
Rahul VasiStudent academic performance prediction in Machine learning using technology flask python and html css js bootstrap js
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System Requirements
Page 1 of 4
agile-student System Requirements Document
1. Introduction
The "agile-student" project aims to develop a system for predicting student academic performance using machine learning technologies. The system will be built using Flask, Python, HTML, CSS, JavaScript, and Bootstrap JS.
2. System Overview
The agile-student project is designed to leverage machine learning algorithms to predict the academic performance of students. This system will provide educators and administrators with insights into student performance trends, allowing for timely interventions and support.
3. Functional Requirements as Story Points
- As an Educator, I should be able to input student data to generate performance predictions.
- As an Administrator, I should be able to view aggregated performance reports for all students.
- As a Student, I should be able to view my predicted academic performance.
- As a Developer, I should be able to integrate the system with existing educational platforms using APIs.
- As a Data Analyst, I should be able to access prediction models and their accuracy metrics.
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4. User Personas
- Educator: Responsible for entering and managing student data.
- Administrator: Oversees the system and accesses comprehensive reports.
- Student: Views personal academic performance predictions.
- Developer: Maintains and integrates the system with other platforms.
- Data Analyst: Evaluates the performance of prediction models.
5. Core User Flows
- Educator inputs student data -> System processes data -> Prediction results are generated.
- Administrator accesses dashboard -> Views aggregated reports -> Identifies students needing intervention.
- Student logs in -> Views personal performance prediction -> Receives feedback or recommendations.
- Developer accesses API -> Integrates with educational platform -> Ensures seamless data flow.
- Data Analyst reviews model performance -> Adjusts parameters -> Improves prediction accuracy.
6. Visuals Colors and Theme
- primary: #007bff (Bootstrap Blue)
- primary_light: #66b2ff
- secondary: #6c757d (Bootstrap Gray)
- accent: #28a745 (Bootstrap Green)
- highlight: #ffc107 (Bootstrap Yellow)
- bg: #f8f9fa (Bootstrap Light)
- surface: #ffffff (White)
- text: #212529 (Bootstrap Dark)
- text_muted: #6c757d (Bootstrap Gray)
- border: #dee2e6 (Bootstrap Border Gray)
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7. Signature Design Concept
The homepage will feature an interactive "Performance Dashboard" that visualizes student data in real-time. Users can hover over different sections to see detailed insights and predictions. The dashboard will use motion/react for smooth transitions and animations, providing an engaging and dynamic user experience. The design will incorporate a "data river" animation where student data flows into a central prediction engine, transforming into visual performance metrics.
Landing Hero Motion Brief
The landing page will showcase a continuous loop animation of student data being transformed into performance insights. Using motion/react, the animation will depict data points entering a central processing hub, which then outputs visual graphs and charts representing student performance. This animation will be responsive and interactive, allowing users to pause and explore specific data points for more detailed information.
8. Interaction Model & Motion Direction
- Interaction Model: Animated
- The landing page will feature moderate scroll-triggered reveals and hover transitions. Interactive elements will have spring physics for a polished effect.
- Internal pages will maintain a static layout for clarity and ease of use.
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9. Non-Functional Requirements
- The system must be responsive and accessible on various devices.
- The prediction model should have an accuracy rate of at least 85%.
- The system should handle concurrent users efficiently without performance degradation.
10. Tech Stack
- Frontend: HTML, CSS, JavaScript, Bootstrap JS
- Backend: Flask, Python
- Database: MySQL or MariaDB
11. Assumptions and Constraints
- The system will primarily be used in educational institutions in India.
- User data privacy and security must comply with local regulations.
- The system will integrate with existing educational platforms via APIs.
12. Glossary
- Flask: A micro web framework for Python.
- Bootstrap JS: A front-end framework for developing responsive and mobile-first websites.
- Prediction Model: A machine learning algorithm used to forecast student performance.
- API: Application Programming Interface, a set of tools for building software applications.
AI-Powered Academic Insights
Predict Student Performance Before It Happens
agile-student uses machine learning built on Flask and Python to turn attendance, grades, and engagement data into clear, actionable predictions — helping educators intervene early and students stay on track.
85%+ model accuracy target
5 supported user roles
Real-time prediction insights
Student Data
Performance Insights
Prediction Engine
Attendance92%
GPA Trend3.4 / 4.0
Study Hours14 hrs/wk
Assignment Score88%
Quiz Average79%
ParticipationHigh
Prior Term GradeB+
Engagement Score85%
Data flows continuously into the prediction engine and emerges as performance metrics.
Who It's For
One platform, five journeys
agile-student adapts to every role in the academic ecosystem — from data entry to deep model analysis — each with a clear path through the platform.
Administrator
View aggregated performance reports for all students
Oversees the system and accesses comprehensive reports
LandingLoginReports
Student
View predicted academic performance
Views personal academic performance predictions
LandingLoginPredictions
Developer
Integrate the system with existing educational platforms
Maintains and integrates the system with other platforms via APIs
LandingLoginIntegration
Data Analyst
Access and evaluate prediction models
Evaluates the performance of prediction models and adjusts parameters
LandingLoginModels
Platform Capabilities
Everything agile-student brings to your institution
From real-time predictions to model evaluation, agile-student equips every role with the tools they need to act on academic insight.
Real-Time Performance Prediction
Instantly generate academic performance predictions as educators input student data, powered by a trained machine learning model.
Interactive Dashboards
Explore student trends, scores, and risk indicators through responsive, hover-driven visual dashboards built for quick insight.
Aggregated Reporting
Administrators can view comprehensive, aggregated performance reports across classes and cohorts to identify students needing intervention.
Student Self-Service Portal
Students log in to view their own predicted academic performance along with personalized feedback and recommendations.
API Integration
Developers can integrate agile-student with existing educational platforms via a documented API for seamless data flow.
Model Performance Evaluation
Data analysts review prediction model accuracy metrics and adjust parameters to continually improve forecast reliability.
System Administration
Manage users, roles, and platform configuration with administrative controls built for secure, efficient oversight.
Key Capabilities
A Closer Look at How agile-student Works
From raw student data to actionable, secure predictions — here is what powers every recommendation on the platform.
Predictive Analytics
Know Who Needs Help, Before It’s Too Late
Our core prediction engine applies trained machine learning models to historical academic records, attendance, and engagement data to forecast student outcomes with 85%+ accuracy.
Educators receive early warning signals well before a student falls behind, enabling timely, targeted interventions instead of reactive catch-up support.
Supports timely interventions and improved student outcomes
85%+accuracy
Real-Time Processing
Insight That Updates as Fast as Your Data
New grades, attendance entries, and assessment results are processed the moment they are submitted, keeping every prediction and dashboard metric current.
This continuous processing pipeline means administrators always act on live, up-to-date insight rather than stale end-of-term summaries.
Supports timely interventions and improved student outcomes
live
Institutional Integration
Fits Into the Systems You Already Use
agile-student exposes a documented REST API so developers can connect existing school information systems, gradebooks, and LMS platforms without disrupting current workflows.
Data flows in both directions through secure, authenticated endpoints, so predictions and recommendations stay synchronized across every connected platform.
Supports timely interventions and improved student outcomes
Security & Compliance
Built on Trust, Privacy, and Local Compliance
Student records are sensitive by nature, so every layer of agile-student is built around strict access control and encryption to protect personal and academic data.
The platform is designed to align with local data protection regulations for educational institutions in India, giving educators, administrators, and students confidence that their information stays private.
Supports timely interventions and improved student outcomes
Built for Indian educational institutions
Ready to transform academic outcomes with agile-student?
Join educators, administrators, and institutions across India already using agile-student's machine learning predictions to identify at-risk students early, personalize interventions, and improve academic outcomes at scale.
Trusted by schools, colleges, and universities across India•Actionable insights, not just numbers
AI-Powered Academic Insights
Predict Student Performance Before It Happens
agile-student uses machine learning built on Flask and Python to turn attendance, grades, and engagement data into clear, actionable predictions — helping educators intervene early and students stay on track.
85%+ model accuracy target
5 supported user roles
Real-time prediction insights
Student Data
Performance Insights
Prediction Engine
Attendance92%
GPA Trend3.4 / 4.0
Study Hours14 hrs/wk
Assignment Score88%
Quiz Average79%
ParticipationHigh
Prior Term GradeB+
Engagement Score85%
Data flows continuously into the prediction engine and emerges as performance metrics.
Who It's For
One platform, five journeys
agile-student adapts to every role in the academic ecosystem — from data entry to deep model analysis — each with a clear path through the platform.
Educator
Input student data to generate performance predictions
Responsible for entering and managing student data
LandingLoginDashboard
Administrator
View aggregated performance reports for all students
Oversees the system and accesses comprehensive reports
LandingLoginReports
Student
View predicted academic performance
Views personal academic performance predictions
LandingLoginPredictions
Developer
Integrate the system with existing educational platforms
Maintains and integrates the system with other platforms via APIs
LandingLoginIntegration
Data Analyst
Access and evaluate prediction models
Evaluates the performance of prediction models and adjusts parameters
LandingLoginModels
Platform Capabilities
Everything agile-student brings to your institution
From real-time predictions to model evaluation, agile-student equips every role with the tools they need to act on academic insight.
Real-Time Performance Prediction
Instantly generate academic performance predictions as educators input student data, powered by a trained machine learning model.
Interactive Dashboards
Explore student trends, scores, and risk indicators through responsive, hover-driven visual dashboards built for quick insight.
Aggregated Reporting
Administrators can view comprehensive, aggregated performance reports across classes and cohorts to identify students needing intervention.
Student Self-Service Portal
Students log in to view their own predicted academic performance along with personalized feedback and recommendations.
API Integration
Developers can integrate agile-student with existing educational platforms via a documented API for seamless data flow.
Model Performance Evaluation
Data analysts review prediction model accuracy metrics and adjust parameters to continually improve forecast reliability.
System Administration
Manage users, roles, and platform configuration with administrative controls built for secure, efficient oversight.
Key Capabilities
A Closer Look at How agile-student Works
From raw student data to actionable, secure predictions — here is what powers every recommendation on the platform.
Predictive Analytics
Know Who Needs Help, Before It’s Too Late
Our core prediction engine applies trained machine learning models to historical academic records, attendance, and engagement data to forecast student outcomes with 85%+ accuracy.
Educators receive early warning signals well before a student falls behind, enabling timely, targeted interventions instead of reactive catch-up support.
Supports timely interventions and improved student outcomes
85%+accuracy
Real-Time Processing
Insight That Updates as Fast as Your Data
New grades, attendance entries, and assessment results are processed the moment they are submitted, keeping every prediction and dashboard metric current.
This continuous processing pipeline means administrators always act on live, up-to-date insight rather than stale end-of-term summaries.
Supports timely interventions and improved student outcomes
live
Institutional Integration
Fits Into the Systems You Already Use
agile-student exposes a documented REST API so developers can connect existing school information systems, gradebooks, and LMS platforms without disrupting current workflows.
Data flows in both directions through secure, authenticated endpoints, so predictions and recommendations stay synchronized across every connected platform.
Supports timely interventions and improved student outcomes
Security & Compliance
Built on Trust, Privacy, and Local Compliance
Student records are sensitive by nature, so every layer of agile-student is built around strict access control and encryption to protect personal and academic data.
The platform is designed to align with local data protection regulations for educational institutions in India, giving educators, administrators, and students confidence that their information stays private.
Supports timely interventions and improved student outcomes
Built for Indian educational institutions
Ready to transform academic outcomes with agile-student?
Join educators, administrators, and institutions across India already using agile-student's machine learning predictions to identify at-risk students early, personalize interventions, and improve academic outcomes at scale.
Trusted by schools, colleges, and universities across India•Actionable insights, not just numbers
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