This work focuses on the analysis of social networks or interaction networks in order to detect communities or behaviours. Temporal evolution of interactions and communities is a major concern.
The approaches used are:
- detection of temporal communities in graphs that may have attributes on nodes and/or edges
- graphlet identification (small induced subgraphs)
- machine learning tools for classification and clustering
- 2D visualization tools for community evolution
The XAI-EDU project aims to use Artificial Intelligence (AI) techniques to produce Learning Analytics (LA) that will support learners and teachers in changing their practices based on
in particular on a dimension that is currently little exploited: the influence of social interactions.
To address this issue, we propose a mixed numerical and symbolic approach (based on business knowledge) to make decision-making tools explainable and actionable.
This work aims to use techniques from the analysis of social networks and machine learning to produce Learning Analytics (LA) that will support learners and teachers in changing their practices.
We are particularly interested in how the analysis of social interactions allows us to obtain insights into the dynamics of learning strategies.
The goal of this project is to analyze and evaluate the socio-economic effects of digital transformations on information quality and pluralism (QPI) in the media universe.