An Intelligent Question Bank System for Automated Difficulty Classification Based on Bloom’s Taxonomy

Creating and managing assessments is a challenging task for educators, especially when attempting to categorize questions based on varying levels of difficulty. Traditional methods of question categorization are often done manually, which takes a lot of time (time-consuming), and may lead to inconsistencies. These issues become even more difficult when dealing with large question banks and inefficient administrative processes. To address this, our research introduces the design and implementation of an Intelligent Question Bank System that automates the classification of exam questions into difficulty levels : Easy, Medium, and Hard by using Bloom's Taxonomy as the guiding framework. Bloom’s Taxonomy provides a hierarchical structure to categorize cognitive skills, ranging from basic recall of facts to higher-order thinking skills like analysis and creation. The system uses a Decision Tree algorithm, a type of Classification in Machine Learning, to classify questions based on their complexity. This approach ensures accurate and consistent categorization by analyzing question patterns, context, and semantics. The system is designed to handle large datasets effectively, making it a suitable solution for educators managing extensive question banks. By combining Bloom’s Taxonomy with Machine Learning techniques, the system simplifies the assessment process and improves its quality. It saves educators time, helps them design better exams, and enhances the overall learning experience for students. This system aims to transform the way questions are developed and managed, making education more efficient and effective.

Intelligent Question Bank, Bloom Taxonomy

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