Blooms Taxonomy Based Question Paper Generation

Blooms Taxonomy Based Question Paper Generation

Prof. Girish. R. Shinde, Mr. Atharva Suresh Chaudhari, Mr. Jay Nitin Shinde, Mr. Divyanshu Sandeep Ghodekar, Mr. Amey mandar Vaidya

Department of Computer Technology

K. K. Wagh Polytechnic, Nashik, India

Abstract :- Blooms Taxonomy Based Question Paper Generation system is an innovative approach to revolutionize the conventional process of question paper generation. It addresses the critical need to reduce the time and effort invested in manual paper creation while offering a more efficient and secure data management system. The key feature of this system is its ability to empower users by allowing them to input questions and define their complexity levels. Leveraging advanced machine learning techniques, including SVM and Naive Bayes, the system assigns marks to each question by aligning them with Bloom's Taxonomy. One of the remarkable features is the simplicity and flexibility it provides to administrators. When it comes time to generate a question paper, administrators can effortlessly select the desired percentage of difficulty. The system then takes over, selecting questions randomly, resulting in a customized question paper. This paper can be conveniently converted into a PDF format and emailed directly to examiners. The broad applications of this system encompass educational institutions, including colleges and universities, as well as government exams. By enhancing efficiency and ensuring reliability in the assessment processes, it contributes significantly to the overall educational ecosystem. This project exemplifies a valuable contribution to the domain of educational technology, promising to redefine how question papers are created and managed.

Key Words: Question paper generation, educational technology, machine learning, assessment processes, PDF format, educational institutions, government exams.