From Student Questions to Student Profiles in a Blended Learning Environment

The analysis of students’ questions can be used to improve the learning experience for both students and teacher. 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 developed a taxonomy of questions 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 based on the question dimensions only. Then, we characterised 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 (lower than average students with popular questions, and higher than average students with unpopular questions). We replicated these analyses on the same courses across different years to show the possibility to predict students’ profiles online. This work shows the usefulness and the validity of our taxonomy and the relevance of this approach to identify different students’ profiles.