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Call for PhD application in the Flowers Lab at Inria/Ensta ParisTech, France:
Machine Learning for Personalization of Online Tutoring Systems
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The Flowers Lab (https://flowers.inria.fr) is searching for highly talented
candidates for application to a PhD within the KidLearn Project
(https://flowers.inria.fr/research/kidlearn/).
This project aims at elaborating novel machine learning approaches for the
personalization of online tutoring systems, e.g. in MOOCs, and based on
recently developped models of active learning, curiosity, and algorithmic
teaching (see here).
Work will involve elaboration of algorithmic approaches based on these models,
as well as real world experimentations in collaborations with pedagogy experts
and industry leading companies in the domain of online educational software.
Thus, we are searching candidates with very strong skills in statistical
inference and machine learning, with interest in practical application and
transfer to industry.
This PhD will potentially take place through a direct collaboration and funding
with/by one of the leading companies in educational technologies (through a
CIFRE scheme).
Please apply here, after contacting Manuel Lopes ([email protected]) and
Pierre-Yves Oudeyer ([email protected]).
More details:
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KidLearn: Machine Learning for Personalization of Online Tutoring Systems
Position type: PhD Student
Functional area: Bordeaux (Talence)
Research theme: Perception, cognition, interaction
Project: FLOWERS
Scientific advisors: [email protected] and [email protected]
HR Contact: [email protected]
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About Inria and the job: http://www.inria.fr/en
Established in 1967, Inria is the only French public research body fully
dedicated to computational sciences.
Combining computer sciences with mathematics, Inria’s 3,500 researchers strive
to invent the digital technologies of the future. Educated at leading
international universities, they creatively integrate basic research with
applied research and dedicate themselves to solving real problems,
collaborating with the main players in public and private research in France
and abroad and transferring the fruits of their work to innovative companies.
The researchers at Inria published over 4,450 articles in 2012. They are behind
over 250 active patents and 112 start-ups. The 180 project teams are
distributed in eight research centers located throughout France.
Job offer description
Algorithmic teaching (AT) formally studies the optimal teaching problem, that
is, finding the smallest sequence of examples that uniquely identifies a target
concept to a learner. AT can be seen as a complementary problem from the active
learning but here it is the teacher that is choosing its examples in an
intelligent way. Algorithmic teaching gives insights into what constitutes
informative examples for a learning agent [3,5].
The main approach used until the moment is to ask a pedagogical experts to
provide a set of Knowledge Units (KU) and the respective teaching approaches
(e.g. lectures, exercises or videos). The goal of the tutoring system is to
select the KU that will improve the knowledge of the student. One limitation of
the Knowledge Tracing model is that the system is agnostic to the specific
problem being addressed. KU are considered as discrete entities and at most a
pre-requisite structure is defined. In this work we want to explicitly model
the structural properties of the problem (e.g. mathematical, geometrical or
chemistry) and infer the knowledge from the observed actions. These approaches
take advantage of the knowledge on how to correctly solve the problem and by
measuring how the student solved, we can estimate what wrong assumptions were
made. Such knowledge would allow creating dedicated demonstrations or questions
that either repeat the instruction on the topics, or provide new exercises that
clarify the differences of the different concepts.
Skills and profile
The first phase of this work will be to do studies on the different approaches
for teaching and on pedagogical approaches to teaching mathematics.
A second phase, in collaboration with teachers, will be to identify a set of
problems of higher impact and define a set of suitable knowledge units. New
machine learning algorithms need to be developed, beyond previous approaches
such as [3,4,5], that are able to estimate the knowledge level of the students
and that optimize the pedagogical value of each exercise. Special interest will
be given to algorithms that take in to account explicitly the structural
knowledge about the problem at hand. In this way the system will not only be
able to select exercises from a pre-defined database but will also be able to
synthetize new exercises and problems.
The final phase will be to deploy a large-scale study in collaboration with
pedagogical experts and an industry leading company in the domain, to validate
and study the impact of the optimization algorithms in identifying the
knowledge level of students, in the teaching objectives and in general
improvement in the interest and motivation to engage in the teaching process.
Excellent knowledge on machine learning.
Good programming capabilities, especially on the design of interfaces
Interest for multidisciplinary studies and experience in performing user
studies.
Benefits
Participation for transportation and restauration
Duration: 3 years
Additional information
References :
• 1. J.E. Beck. Difficulties in inferring student knowledge
from observations (and why you should care). In Educational Data Mining:
Supplementary Proceedings of the 13th International Conference of Artificial
Intelligence in Education, 2007.
• 2. J.I. Lee and E. Brunskill. The impact on individualizing
student models on necessary practice opportunities. In International Conference
on Educational Data Mining (EDM), 2012.
• 3. A. Rafferty, E. Brunskill, T. Griffiths, and P. Shafto.
Faster teaching by pomdp planning. In Artificial Intelligence in Education,
2011.
• 4. Manuel Lopes, Benjamin Clement, Didier Roy, Pierre-Yves
Oudeyer. Multi-Armed Bandits for Intelligent Tutoring Systems, arXiv:1310.3174
[cs.AI], 2013.
• 5. Maya Cakmak and Manuel Lopes. Algorithmic and Human
Teaching of Sequential Decision Tasks. AAAI Conference on Artificial
Intelligence (AAAI), Toronto, Canada, 2012._______________________________________________
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