ABSTRACT
During the first French Covid19 lockdown, students had to switch to a fully online learning mode. Therefore, understanding students' digital behavior becomes crucial for analysts serving public institution policy. In particular, they want to determine and interpret the evolution of students' digital behavior. This paper aims to offer them indicators. We propose to study generic student logs corresponding to standard digital workspace services. Therefore, this paper contributes to the scientific question: Can we give an easy-to-interpret and visual indicator to model students' behavior changes from poor and generic data? We first verify that we can extract epidemic-specific temporal patterns on these logs using Contrast Mining. These patterns represent students' behaviors and pace. Then, we propose a new method called Temporal Pattern Histories (TPH), representing the evolution of the temporal patterns' over time. It is a dynamic representation of students' digital behavior. Using this method, we present graphically abrupt changes during the Cov19 lockdown, and we give some hypotheses about these results. This case study proves the relevance of TPH to detect and analyze students' behavioral changes in an interpretive way. This approach has the advantage of representing the global evolution of students' behavior without giving students specific information.