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Data Mining and Recommender System for Education : Towards a support system for academic guidance in higher education

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    Université de Lorraine
    Computer scienceDigital systems
    First Stage Researcher (R1)
    12/06/2020 23:00 - Europe/Athens

Keywords : Data Mining, Recommender System, Artificial Intelligence

1. Background

The work carried out during this PhD thesis will contribute to the AILES project (Accompagnement à l'Intégration des Lycéens dans l'Enseignement Supérieur) of the National French Research, Action “Innovation and territories”.

AILES aims to ensure better orientation and integration of high school students in higher education, whatever their origins, backgrounds, skills and projects, and thus promote their development in their studies and their professional integration.


The aim of the thesis project is to design a decision-support system, concretely an orientation aid for high school students, to facilitate their integration into higher education. This decision support system will be based on a set of data, representing traces of the past, and coming from multiple sources and of a heterogeneous nature. They are, for example, knowledge data in the field of guidance from experts, but also guidance trace data representing previous years, profile data of high school students, etc. The data will be used to make decisions on the basis of a set of data, representing traces of the past, and coming from multiple and heterogeneous sources. These data have the advantage of being, for the most part, structured. Results from questionnaires can also be made available.

The designed decision support system will therefore be based on data mining algorithms to enable the identification of influencing factors in the students' decision making during their orientation process.

2. Scientific issues

Several locks will be studied during the thesis, either locks related to algorithms, or locks related to the application context.

The first scientific challenge will be related to the identification of influencing factors in decision making, whether they are factors currently used by high school students, or those that can be automatically identified in the data that will be searched. The first type of factors will be done in collaboration with specialists in the field, and through strong interactions with high school students.

The second challenge, which will be central to the thesis project, consists in designing a decision support system on data coming from multiple, heterogeneous sources whose reliability can be questioned.

The problem of lack of data will be present throughout the thesis. Indeed, not only will it be impossible to have enough data for each "situation", but also all possible situations will not appear in the data. Mechanisms of inference on missing data will have to be designed.

The decision-support system will have to be designed for a dynamic environment: new student profiles, new training (or disappearance of training), constraints for some students, etc. The system will have to be designed for a dynamic environment.

Finally, the question of the nature of the single or multiple recommendation, sequential or not, the need for explainable algorithms, etc. will have to be studied.

In concrete terms, is a training course recommended for the following year? or a set of training courses with elements enabling the pupil to make a choice? or a whole course that may lead to professional integration?


It should be noted that this thesis work is part of the more global context of the AILES project, in which teachers, students, guidance and rectorship staff are involved. The doctoral student will have to interact with the entire team in order to identify the data to be collected.

In addition, the doctoral student will have to interact with researchers and stakeholders from other disciplines, particularly in psychology, to enable the design of a decision-support system that meets the needs and reality of the field.

3. Skills and motivation

The doctoral student must hold (or be in the process of obtaining) a Master's degree or engineering degree in computer science.

The candidate's design and analysis skills and autonomy are important for this thesis, as well as development skills and a good capacity for interaction and listening (given the multidisciplinary context of the thesis).

4. Contact

Pr. Davy Monticolo, davy.monticolo@univ-lorraine.fr, laboratoire ERPI, Université de Lorraine

Work location(s)
1 position(s) available at
Université de Lorraine
8 rue Bastien Lepage

EURAXESS offer ID: 526281


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