Automatic Question Paper Generation With Marks Allocation Using Bloom’s Taxonomy

In the field of education, creating evaluations that are in line with the goals of the courses is a significant difficulty for instructors. The task becomes harder due to the requirement for diversity in question papers and the need to follow university assessment norms. In order to tackle this problem, a system that can quickly create question papers according to teacher guidelines is desperately needed. To successfully explain and define questions, researchers recommend using a variety of tags, such as cognitive level, difficulty, question type, and content/topic. Our suggested remedy for this is the use of an independent mechanism for creating question papers. Users can input a series of questions and add varying degrees of complexity to each question here. Using Bloom’s taxonomy, the system uses machine learning techniques to evaluate and mark questions in order to expedite this process. Subsequently, the indicated questions are effectively recorded in a database together with their corresponding levels of complexity. In addition to saving teachers time, this creative method aims to guarantee that the question papers are of a high calibre and reflect the course’s overall learning objectives. accurately give the question’s marks and also be able to use machine learning and Bloom’s Taxonomy approach to create various question papers.