The ANR MOBILES project aims to document, understand, and support the spatial practices and language learning experiences of international students welcomed in higher education institutions in France. The uniqueness of the project lies in analyzing the potential learning opportunities offered by immersive stays through spatial practices enhanced by digital tools.

The project involves (1) analyzing the spatial practices of students, uncovering learning opportunities within their contexts; (2) designing a city mapping interface that combines heterogeneous data sources and allows for both quantitative and qualitative exploration; (3) studying how user-participation-based recommendation systems can be implemented to support learning objectives.

A significant part of the MOBILES project entails (1) developing innovative methods for collecting explicit and declared activity traces from diverse and geolocated sources, such as photographs, social networks, route searches, and more; (2) devising modeling, analysis, and visualization methods that blend qualitative approaches with formalized techniques in computer science and geomatics.

My role

A prototype smartphone application has been developed to enable the project team to (1) collect digital data related to individual activities, including geographic traces and produced annotations; and (2) provide a cartographic visualization of these traces along with individual, shared, and collective discussions.

The second part of the project focuses on how the smartphone mapping application, when combined with a recommendation system and enriched by an annotation system, can support language learning and the social integration of international students.

My tasks within this project are as follows:

  • Provide students with the ability to annotate traces to enrich them. These annotations aim to allow students to share the locations that facilitated their integration and explain why these places contributed to their city appropriation.
  • Utilize the enriched geolocation traces to offer recommendations for diversifying students’ practices and learning experiences. Annotations should be interpretable by a computer system, the recommendation engine, to use them as a knowledge source for offering recommendations to all participants based on their specific trajectories. Enriched traces will be processed to suggest relevant locations for new students based on activities to be conducted, internal factors (e.g., interests, goals, prior studies, nationality), their current study program, and their familiarity with the city.

The ultimate goal is to understand the annotations provided by students and explore how they can be correlated with the trajectories of new students, identifying similarities in their traces to recommend relevant locations to their peers and explain why these places were useful. The aim is not to define good or bad practices in behavior but to design a socializing tool that aids in the recognition (awareness) of learning and its connection to spatial practices for potential enrichment.