Profiling students from their questions in a blended learning environment

Abstract

Automatic analysis of learners' questions can be used to improve their level and help teachers in addressing them. We investigated questions (N=6457) asked before the class by 1st year medicine/pharmacy students on an online platform, used by professors to prepare their on-site Q&A session. Our long-term objectives are to help professors in categorizing those questions, and to provide students with feedback on the quality of their questions. To do so, first we manually categorized students' questions, which led to a taxonomy then used for an automatic annotation of the whole corpus. We identified students' characteristics from the typology of questions they asked using K-Means algorithm over four courses. The students were clustered by the proportion of each question asked in each dimension of the taxonomy. Then, we characterized the clusters by attributes not used for clustering such as the students' grade, the attendance, the number and popularity of questions asked. Two similar clusters always appeared: a cluster (A), made of students with grades lower than average, attending less to classes, asking a low number of questions but which are popular; and a cluster (D), made of students with higher grades, high attendance, asking more questions which are less popular. This work demonstrates the validity and the usefulness of our taxonomy, and shows the relevance of this classification to identify different students' profiles.