Analyzing the Cognitive Level of Classroom Questions Using Machine Learning Techniques

Abstract

Questioning is widely acknowledged as an effective instructional strategy used by teachers in their interaction with students for variety of purposes. In educational practices, the analysis of classroom questions asked by teachers is of particular benefits. This paper investigates the effectiveness of machine learning techniques on analyzing teacher's classroom questions by automatically classifying them into different cognitive levels identified in Bloom's taxonomy. More specifically, this paper reports on three of the most effective machine learning techniques for text classification: k-Nearest Neighbors, Naïve Bayes, and Support Vector Machine using term frequency as a term selection approach. In doing so, a dataset of questions has been collected and classified manually into Bloom's cognitive levels. Preprocessing steps have been applied to convert questions into a representation suitable for machine learning techniques. Using this dataset, the performance of the three machine learning techniques has been evaluated. The results show a comparable performance of k-Nearest Neighbor and Naïve Bayes and a superior performance of Support Vector Machine in term of F1 and accuracy. Moreover the results also indicate that machine learning techniques show different levels of sensitivity to the number of terms used for question representation.