Exam Question Classification Based on Bloom Taxonomy

Abstract

Examination has an important role in the process of learning. The assessment of examination is important in educational institutions because examine is one of the most common ways to assess student achievement in a particular field. Therefore, there is a crucial need to create a balanced and high-quality test, which satisfies different levels of comprehension. Therefore, many educators rely on the understanding field of Bloom's taxonomy, which is a popular framework designed to examine students'skills and abilities. There are several proposed activitiesto automatically manage the classification of queries according to Bloom's taxonomy. Most of these activities classify questions according to a specific domain. As a result, there is a shortage of questionnaires for multi-domain sites. The purpose of this paper is to introduce a classification model to separate exam questions based on Bloom's tax administration in multiple areas. Here, the difficulty level of each question deposited in the question paper is determined from the criteria of keyword/s found in the question. A knowledge-based approach and text mining technique will be used to identify and extract information and keywords from textual content in the exam paper. using the prototype system developed, an illustration of the overall analysis for level of difficulty of examination question paper will be obtained. The outcome of our idea should be significant in classifying questions from multiple domains based on Bloom’s taxonomy.