The following is a list of topics which are being be proposed as internship topics (sujets de stage) to students from Nantes and Brest, but also outside Nantes/Brest. They will also constitute themes studied in our research reading groups and seminars. They will be funded by AILE or by our other projects.
We are looking for candidates who have
- The intent of pursuing an Engineering degree or a Masters degree in Artificial Intelligence, Data Science, Natural Language Processing, Machine Learning or a related field
- Capacity to work independently, as well as collaborate within a team
- Solid programming skills
If you are interested or have any questions, please do not hesitate to contact Olivier Aubert (email@example.com), Nicolas Hernandez (firstname.lastname@example.org) and the referrer of the internship topic.
To apply, send a curriculum vitae together with your academic results and a motivation letter and indicate the topics of interest.
Measuring the hardness of an educational resource
- Many educational resources are available on the web but it is important to evaluate automatically the age group the resource is intended for and the level required to understand it. This task requires the use of machine learning. This project is principally based on X5-GON data.
- Referrers: Colin de la Higuera (email@example.com), LS2N TALN team
Detecting the theme shifts in a lecture
- In a lecture presented by a one hour video or a 50 pages pdf, the themes vary over time. We aim to use techniques from data science to study the so-called concept drift in this context. This project is based on X5-GON/PASTEL data.
- Referrers: Nicolas Hernandez (firstname.lastname@example.org) and Colin de la Higuera (email@example.com), LS2N TALN team
Predicting MOOC attrition
- Using learning traces left by MOOC users, and generalizing from previous activities, can we compute attrition indicators? This project is based on HUBBLE data.
- Referrers: Antoine Pigeau (firstname.lastname@example.org), LS2N DUKe team
Mixing AI techniques to give relevant insights on Mooc attrition
- Using learning traces left by MOOC users, how can we mix AI techniques to give relevant insights and explainable results as well. This project is based on HUBBLE data.
- Referrers: Serge Garlatti (Serge.Garlatti@imt-atlantique.fr), IHSEV Lab-STICC
- How can we design feedback, for example dashboards that makes those insights explicit and actionable for the users? This project is based on HUBBLE data.
- Referrers: Jean-Marie Gilliot (email@example.com), IHSEV Lab-STICC
- Analyzing user activity would allow to identify activities generating unexpected behaviour, and therefore help resource/MOOCs authors to refactor their content using this information. This project is principally based on FUN MOOCs data.
- Referrers: Yannick Prié (firstname.lastname@example.org), LS2N DUKe team
- Identification and suggestion of personalized pedagogical paths (made of texts, videos, and other media) according to specific objectives of knowledge and competencies to be acquired by the learner. This is mainly related to X5-GON data.
- Referrers: Hoël le Capitaine (email@example.com), LS2N DUKe team
From micro-competence to professional project.
- Can we support learner to define their personal learning paths, in terms of objectives and aimed competencies? A meta review on AI, competencies and self development will be part of the work;
- Referrers: Jean-Marie Gilliot (firstname.lastname@example.org) & Issam Rebaï (email@example.com), IHSEV Lab-STICC
- Re-centralizing data is a powerful paradigm to enable semantic indexing, incremental data integration, and query discovery. Many resources exist in education but they are spread around the web. We propose to build a datahub for educational resources. This portal accepts resources as RDF data and allows query processing across data.
- Referrers: Hala Skaf (firstname.lastname@example.org), LS2N GDD team