From Student Questions to Student Profiles in a Blended Learning Environment

International audience ; The analysis of student questions can be used to improve the learning experience for both students and teachers. We investigated questions (N = 6457) asked before the class by first-year medicine/pharmacy students on an online platform, used by professors to prepare for Q&A sessions. 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, we developed a coding scheme and then used it for automatic annotation of the whole corpus. We identified student characteristics from the typology of questions they asked using the k-means algorithm over four courses. Students were clustered based on question dimensions only. Then, we characterized the clusters by attributes not used for clustering, such as student grade, attendance, and 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 of predicting student profiles online. This work shows the usefulness and validity of our coding scheme and the relevance of this approach to identify different student profiles. Notes for Practice • Questions provide important insights into students' level of knowledge, but coding schemes are lacking to study this phenomenon. • After providing a bottom-up coding scheme of student questions in a blended environment, we analyzed the relationship between the questions asked and the student profiles. • Profiling students based on their questions over a year allows us to predict the profiles of future students to help the teacher understand who asks what. • These results provide both a coding scheme that can be reused in various contexts involving questions, and a methodology that can be replicated in any context where students ask many questions, in particular to help the teacher ...