A rule-based approach in Bloom's Taxonomy question classification through natural language processing

This paper describes a rule-based approach to analyze and classify written examination questions through natural language processing for computer programming subjects. In general, Bloom's Taxonomy or the Taxonomy of Educational Objectives (TEO) acts as a main guideline in assessing a student's cognitive level. However, academicians need to design the appropriate questions and categorize it to the cognitive level of TEO manually. Our aim is to provide lecturers with a tool that can ease their task to assess the student's cognitive levels from the written examination questions. This paper describes a natural language processing technique to analyze the cognitive levels of Bloom's taxonomy for each question through the development of rules. Preliminary results from the experiments show that it is a viable approach to help categorize the questions automatically according to Bloom's Taxonomy.