Lundi 12 Octobre 2020 à 11h en salle 25-26/105, Jussieu
In this talk, I will review some of our recent work in understanding collaborative learning and solving using network approaches on large empirical datasets. First, using fine-grained quantitative data from digital lab notebooks of more than 2,000 teams who participated to the science and engineering iGEM competition in the past 10 years, I will exhibit shared aspects of team work, team structure and team dynamics, as well as features underlying team performance and team improvement throughout participations. I will then introduce our ongoing ‘iGEM TIES’ project aimed at mapping high-resolution team interactions in the lab using a bluetooth-enabled smartphone app. I will contrast these results with behavior observed in large, distributed open-source communities from GitHub. Finally, I will introduce our recent work on collaborative learning using fine-grain social data from online forums and phone call records, and show how interaction data can help predict learning outcomes and identify peer influence in performance and engagement.