Classifying Question Papers with Bloom’s Taxonomy Using Machine Learning Techniques

Abstract

Constructing well-balanced question papers of the suitable level is a difficult and time-consuming activity. One of the remedies for this difficulty is the use of Bloom’s taxonomy. As we know that, Bloom’s taxonomy helps in classifying educational objectives into levels of specificity and complexity. Therefore, the primary goal of this research paper is to demonstrate the use of Bloom’s taxonomy in order to judge the complexity and specificity of a question paper. The proposed work employs various Machine Learning techniques to classify the question papers into different levels of Bloom’s taxonomy. To implement the same, we collected question papers data set, consisting of 1024 questions, from three universities and developed a web app to evaluate our approach. Our result shows that we achieved the best result with Logistic Regression and Linear Discriminant Analysis (LDA) Machine Learning techniques both having an accuracy of 83.3%.