Project-Team Flowers
Personnel
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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Section: New Results

Applications in Educational Technologies

Machine Learning for Adaptive Personalization in Intelligent Tutoring Systems

Participants : Manuel Lopes [correspondant] , Pierre-Yves Oudeyer, Didier Roy, Alexandra Delmas, Benjamin Clement.
The Kidlearn project

Kidlearn is a research project studying how machine learning can be applied to intelligent tutoring systems. It aims at developing methodologies and software which adaptively personalize sequences of learning activities to the particularities of each individual student. Our systems aim at proposing to the student the right activity at the right time, maximizing concurrently his learning progress and its motivation. In addition to contributing to the efficiency of learning and motivation, the approach is also made to reduce the time needed to design ITS systems.

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem.

The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children. [14]

Linear UCB for intelligent tutoring system

What we wanted to do was to use the feature space considering features of students, features of explanations and maybe feature about the exercises. We wanted use this feature space to guide the bandit algorithm to recommend explanations. The algorithms that we already developed cannot be used in this kind of problem, because the order and the value of the bandit values depends on the time and on the learning progress the actions give in time. And in this new experiment, we wanted to recommend feedback, base on the population results depending on student's and explanation's features. In this problem, there is no consideration about temporality and the long-term progression of the student is too hard to correlate with a particular explanation. So different algorithms have been studied in the literature to use contextual bandit to make recommendation over a population. The algorithm that we wanted to use and adapt to our purpose is the LinUCB algorithm from [136].

Different kind of simulation have been made to test this framework. These experiments were made by generating various population of student defined by binary features with a number of dimension between 5 and 20. Also a set of activity have been defined and depending of the features of the students, their result for each activity was right or wrong. The algorithm was trained by making the student work on random activities, this way, the algorithm would learn the correlation between the student feature space and the success/failure to activities. After that the tests was made by letting the algorithms to choose the activity to propose to the student depending of their features. A lot of different configurations have been tested. The algorithm showed good results for low dimension feature space (3 to 9 features) with 90% accuracy, but for high dimension feature space, the results dropped to 20-30% accuracy.

Experiment in class in 2018

An experiment will be held in mars 2018 about testing the kidlearn framework in classroom in Bordeaux Metropole. The goal is to test a new feature by giving the student the opportunity to have different kinds of choice. This choice would be managed by a multi-armed bandit algorithm. We want to make an experiment with 600 student from bordeaux Metropole and we are currently discussing with the Local Education Authority to also test the application in other school in other departments.
The KidBreath project

To create learning contents linked to asthma to personalize it like mathematics activities in Kidlearn project [14] we used recommandation criterias in Therapeutic Education Program for asthma kids made by Health High Autority. Following an approach of participatory design [102], contents were validated by medical experts like health educators, pulmonoligists and pediatrics. Then, we conducted a workshop with forty kids aged 8 in order to iterate over the application interfaces and evaluate enjoy about it with observations. Finally, we realized a focus group with 5 asthma kids to validate the global comprehension of a part of the content. It revealed that children wanted more contents about the crisis treatment and how the asthma works in the human system (verbatims).

In a preliminary study, we experimented the participatory design process (PD) in the context of asthma e-learning using KidBreath tool. We evaluated in two Year 4 classes its efficacy in a motivation way [175], usability [142], [157], disease knowledge [92] and interests of children by their system [119]. After two weeks a use, results showed, in acceptance with behaviors, high level of intrinsic motivation when using KidBreath, usability and enjoyment of edutainment activities. This pilot study tends to confirm to continue with this approach with asthma kids at home (study in progress). These results was presented in the 29st Conference in Computer-Human Interaction in Poitiers, France.

We presented Thesis project in some events this year, with one publication surbmitted and validated:

Poppy Education: Designing and Evaluating Educational Robotics Kits

Participants : Pierre-Yves Oudeyer [correspondant] , Didier Roy, Théo Segonds, Stéphanie Noirpoudre, Thibault Desprez, Damien Caselli, Aurélie Lopes, Kelian Schindowsky.

Poppy Education project aims to create, evaluate and disseminate all-inclusive pedagogical kits, open-source and low cost, for teaching computer science and robotics.

It is designed to help young people to take ownership with concepts and technologies of the digital world, and provide the tools they need to allow them to become actors of this world, with a considerable socio-economic potential. It is carried out in collaboration with teachers and several official french structures (French National Education, High schools, engineer schools, ... ). For secondary education and higher education, scientific literacy centers, Fablabs.

Poppy Education is based on the robotic platform poppy (open-source platform for the creation, use and sharing of interactive 3D printed robots), including:
Pedagogical experimentations : Design and experiment robots and the pedagogical activities in classroom.

This project is user centered design. The pedagogical tools of the project (robots and ressources) are being created directly with the users and evaluated in real life by experiments. So teachers and researchers co-creatie activities, test with students in class-room, exchange their uses and develop the platform as needed [81].

The activities were designed mainly with Snap! and Python. Most activities use Poppy Ergo Jr, but some use Poppy Torso (mostly in higher school because of its cost).

The pedagogical experiments in classroom carried out during the first year of the project notably allowed to create and experiment many robotic activities and to create pedagogical resources to taking in hand the robot. The main objective of the second year was to make all the activities and resources reusable (with description, documentation and illustration) easily and accessible while continuing the experiments and the diffusion of the robotic kits.

Figure 20. Experiment robots and pedagogical activities in classroom
IMG/robotics_in_classroom.jpg
Pedagogical documents and resources
Diffusion
Symposium robotics

We organized a symposium robotics for the third year (http://dm1r.fr/colloque-robotique-education/) that present research results and feedback on the use of Poppy and Thymio robots in education (other robots have been discussed as well). Poppy Education team and the working group teachers helped with the organisation of the event and during the event (talk and workshops).

Evaluate the pedagogical kits

After experimenting and create tools with educational activities in class and for the class, it is now to evaluate qualitatively and quantitatively the impact of these tools. We must therefore assess, at first, if these tools offer good usability (i.e. effectiveness, efficiency, satisfaction). Then, in a second step, select items that can be influenced by the use of these tools. For example, students' representations of robotics, their motivation to perform this type of activity, or the evolution of their skills in these areas. In 2017 we conducted experiments to evaluate the usability of kits. We also collected data on students' perceptions of robotics.
Partnership on education projects

IniRobot: Educational Robotics in Primary Schools

Participants : Didier Roy [correspondant] , Pierre-Yves Oudeyer.

IniRobot (a project done in collaboration with EPFL/Mobsya) aims to create, evaluate and disseminate a pedagogical kit which uses Thymio robot, open-source and low cost, for teaching computer science and robotics.

IniRobot Project consists to produce and diffuse a pedagogical kit for teachers and animators, to help to train them directly or by the way of external structures. The aim of the kit is to initiate children to computer science and robotics. The kit provides a micro-world for learning, and takes an enquiry-based educational approach, where kids are led to construct their understanding through practicing an active investigation methodology within teams. It is based on the use of the Thymio II robotic platform. More details about this projects were published in RIE 2015 [50] , which presents the detailed pedagogical objectives and a first measure of results showing that children acquired several robotics-related concepts. See also http://www.inirobot.fr.

Deployment: After 30 months of activity, IniRobot is used by about 1800 adults and 20 000 children in 72 cities of France. Example of action in university: MEEF teacher training for the hope of Aquitaine. Example of action in school: training of all Gironde Pedagogical ICT Advisors, covering nearly 1000 schools. Example of action in the extracurricular time: training 82 facilitators TAP cities of Talence, Pessac, Lille, ..., CDC Gates of inter-seas. Example of national action: Training of the digital mediators of the 8 Inria centers.

Partnership

The project is carried out in main collaboration with the LSRO Laboratory from EPFL (Lausanne) and others collaborations with French National Education/Rectorat d'Aquitaine, with Canopé Educational Network, with ESPE (teacher's school) Aquitaine, ESPE Martinique, ESPE Poitiers, National Directorate of Digital Education
Created pedagogical documents and resources
Scientific mediation

Inirobot is very popular and often presented in events (conferences, workshops, ...) by us and by others.
Symposium robotics

With Poppy Education, Inirobot is a main line in our colloquium "Robotics and Education" (http://dm1r.fr/)

Spread of Inirobot activities

Inirobot activities are inside several projects : Dossier 123 codez from Main à la Pâte Fundation, Classcode project, ...
Future MOOC Thymio

A new project is coming, MOOC Thymio, in collaboration with Inria Learning Lab and EPFL (Lausanne, Switzerland), on FUN platform and edX EPFL Platform.) To teach how to use Thymio robot in education.


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