Abstract: Bloom's Taxonomy is a framework used by educators to categorize the learning objectives that they assign to their students. This taxonomy's cognitive domain is intended to confirm a student's cognitive proficiency in a written test. Teachers may occasionally find it difficult to determine whether the exam questions they create adhere to Bloom's taxonomy requirements at various cognitive levels. Based on this taxonomy, this research suggests an automated examination question analysis to identify the relevant category. This rule-based method uses Natural Language Processing (NLP) approaches to find significant verbs and keywords that could help determine a question's category. The topic area of computer programming is the main emphasis of this work. Currently, the research uses a set of 100 questions, comprising 30 test questions and 70 training questions. According to preliminary findings, the guidelines could be able to help candidates accurately identify the Bloom's taxonomy category in test questions. By utilizing the hierarchical structure of Bloom's cognitive domain, automatic question paper production using Bloom's taxonomy can generate questions with different levels of complexity and cognitive ability. After analyzing the learning objectives or content using algorithms and natural language processing, the system creates pertinent questions that correspond with the levels of Bloom's taxonomy, which include knowledge, comprehension, application, analysis, synthesis, and assessment. This method aids teachers in developing thorough, well- balanced exams for pupils that encourage critical thinking and deeper comprehension.
Keywords: Bloom’s Taxonomy, Natural Language Processing(NLP)
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DOI:
10.17148/IJARCCE.2024.13451
