Laurent Brisson (PhD)

Associate Professor at IMT Atlantique

Selected Publications

This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this drop-out. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider OpenClassrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. The performed experimentations cover three main axes; (1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners’ engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. The findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.
(to appear) In, 19th International Conference on Intelligent Data Engineering and Automated Learning, Madrid, 2018

The study of information dissemination in social networks is of particular importance in many areas as marketing, politics and security for example. Various strategies are being developed to disseminate information, those aimed at disseminating information widely and those aimed at disseminating information in a more confidential manner to make it scarce. In this paper, we adapt a model dedicated to spreading rumours by word of mouth in a physical space to the context of social networks. We compare two modes of dissemination based on profusion or scarcity and study the impact of the choice of the initial node. The results obtained show to what extent each mode exploits the social network topology and especially the influence of hubs.
International Conference on Complex Networks and Their Applications (COMPLEX NETWORK), Lyon, France, 2017


Breast Cancer Risk Assessment

According to the World Health Organization, starting from 2010, cancer will become the leading cause of death worldwide. Prevention of major cancer localizations through a quantified assessment of risk factors is a major concern in order to decrease their impact in our society. In this project our objective was to find and to evaluate modeling methods easily readable by a physician.


Discover some innovations in pedagogy in order to act on students’ autonomy and motivation through different activities such as role-playing or group MCQ.


I am currently a coordinator and teacher for the following courses at IMT Atlantique:

I am also a teacher for the following courses:

  • Algorithms
  • Big Data