International audienceThis study aims at investigating which cues teachers detect and process from their students during instruction. This information capturing process depends on teachers' sensitivity, or awareness, to students' needs, which has been recognized as crucial for classroom management. We recorded the gaze behaviors of two pre-service teachers and two experienced teachers during a whole math lesson in primary classrooms. Thanks to a simple Learning Analyt-ics interface, the data analysis reports, firstly, which were the most often tracked students, in relation with their classroom behavior and performance; secondly, which relationships exist between teachers' attentional frequency distribution and lability, and the overall classroom climate they promote, measured by the Classroom Assessment Scoring System. Results show that participants' gaze patterns are mainly related to their experience. Learning Analytics use cases are eventually presented, enabling researchers or teacher trainers to further explore the eye-tracking data

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.

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. CCS CONCEPTS • Computing methodologies~Unsupervised learning • Computing methodologies~Cluster analysis • Applied computing~E-learning