2019-02123 - Research Engineer F/M Analysis of traces of mathematical learning and prediction

Contract type : Fixed-term contract

Level of qualifications required : PhD or equivalent

Other valued qualifications : PhD

Fonction : Temporary scientific engineer

Level of experience : From 3 to 5 years

About the research centre or Inria department

The Inria Sophia Antipolis - Méditerranée center counts 34 research teams as well as 8 support departments. The center's staff (about 500 people including 320 Inria employees) is made up of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrative staff. 1/3 of the staff are civil servants, the others are contractual agents. The majority of the center’s research teams are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Four teams are based in Montpellier and two teams are hosted in Bologna in Italy and Athens. The Center is a founding member of Université Côte d'Azur and partner of the I-site MUSE supported by the University of Montpellier.

Context

We are looking for a research engineer with a background in Semantic Web, Graph-based Knowledge Representation and Reasoning, Automatic classification, Recommender Systems to join the Inria Wimmics team to work within the collaborative project funded by the Ministry of National Education (MEN) as part of the PI IA market (https://eduscol.education.fr/cid118880/partenariat-d-innovation-et-ia.html).

This project is a follow-up to the joint EduMICS laboratory between Inria's Wimmics team and Educlever, where we developed a knowledge graph based on data from the Educlever learning platform, which integrates the pedagogical resources of the Educlever platform, including training exercises, their indexing to a thesaurus of knowledge and skills, and the learning traces of the platform's users, including their success or failure in exercises. We have shown how to implement the existing platform's functionalities with semantic queries on this knowledge graph and how to develop new functionnalities that exploit these new possibilities of representation and reasoning.

As part of the present project funded by the PI IA of the MEN, we wish to develop bricks for adaptive learning on the Educlever platform. To do this, we want to explore the possibilities of using the existing Educlever knowledge graph to adapt the learning to the user's profile and context. A first step will be to predict the learner's success in an exercise based on his or her profile and learning context. Ultimately, we aim to produce personalized recommendations of exercises, taking into account the learner's profile and context.

Assignment

The recruited person will be in charge of developing innovative prediction methods and algorithms based on the knowledge graph extracted from the Educlever platform. He or she will participate in the project monitoring and meetings with partners.

Main activities

The recruited person will focus on the following research questions:

  • Which features should be selected from the Educlever knowldge graph to best predict the learner success to an exercice?
  • Which machine learning algorithm should be selected for the prediction?
  • How should the system recommand adapted exercises based on the prediction of learner's success in the exercises?
  • How does the exploitation of Semantic Web models enable to improve the resource recommendation process?

Experiments will be designed in collaboration with the project partners in order to evaluate the proposed methods and algorithms.

Skills

Important skills are:

  • Knowledge of Semantic Web models and techniques
  • Experience on Graph-based Knowledge Representation and Reasoning
  • Experience in using Machine Learning libraries for classification tasks
  • Experience in using or designing Recommender systems
  • Interest for the domain of eEducation
  • Fluent English and/or French

 

 

Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage

Remuneration

From 2632 euros gross monthly (according to degree and experience)