Generating Questions Bank for Adaptive Assessment Using Machine Learning Techniques

This research investigates the core components and machine learning techniques essential for adaptive assessment processes. It aims to propose a comprehensive system design, evaluate the effectiveness of various machine learning approaches, and develop methodologies to utilize traditional assessment data for predicting question difficulty and learner proficiency levels. By leveraging response data of learners, the study seeks to create a dynamic question bank enriched with metadata, facilitating personalized adaptive assessments. The findings aim to provide a robust framework for implementing adaptive assessments, thereby enhancing their adaptability and accuracy in evaluating learner performance.