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  • To address the challenges faced in ensuring authenticity and fairness for Test-Takers to who attempt Online Exams.
  • The challenge is to ensure each question set for test-taker contains questions of similar difficulty levels.
  • Come-up with a web-application which will ensure that the questions are tagged with correct difficulty level for next iteration of Exam, using machine learning analysis on the performance of test-takers for previous exam.

Ojectives achieved

  • Identification of the features in the Exam Question Data
  • Development of Machine Learning Algorithm for predicting the difficulty levels or tags of the Question Data
  • Provide User Interface for Question Creators with following features:
    • Provision for performing Machine Learning analysis on Selected Exam Data
    • Creation of Analysis report on difficulty tagging for each year‘s Exam data
    • Statistics for change in the tags, that is, deficits and excess, overall
    • Adding new Questions
    • Editing Previous Questions
    • Provided Question Revision history and provision to mark question active or inactive
    • Provided Audit trail on Questions
    • Question Sets creation with the option of shuffling and pagination
    • User Management for Admin, Question Editor and Moderator





Report and documentation can be found on this Documentation

Folder Tree

  • docs contains documentation and paper
  • src contains codes
    • 611-Proj-Ques-Eval-Frontend: Contains the Website
    • Python-Web-Server: Provide REST APIs solution for running Machine Learning Algorithm



Prof. Alan Hunt, CSE Dept. University at Buffalo - SUNY


  • Machine Learning
    • Python 3.6
    • Tornado for Rest APIs
    • SQLAlchemy
    • Matplotlib for plotting the clusters
  • Web Development
    • Laravel 5.4 with Symphony and Eloquent ORM
    • PHP 7
    • MySQL
    • Apache Server for deploying the Website
    • Bootstrap 3.6
    • Google Charts


  • e-Yantra, IIT-Bombay


This project is open-sourced under MIT License

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