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).
KidLearn: Machine Learning for Personalization of Online Tutoring Systems
Position type: PhD Student
Functional area: Bordeaux (Talence)
Research theme: Perception, cognition, interaction
Scientific advisors: firstname.lastname@example.org and email@example.com
HR Contact: firstname.lastname@example.org
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.
Participation for transportation and restauration
Duration: 3 years
• 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.