Multi-Armed Bandits for Intelligent Tutoring Systems

Benjamin Clement 1 Didier Roy 1 Pierre-Yves Oudeyer 1 Manuel Lopes 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : 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.
Type de document :
Article dans une revue
Journal of Educational Data Mining (JEDM), 2015, 7 (2), pp.20--48. 〈〉
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Soumis le : mercredi 6 janvier 2016 - 14:29:33
Dernière modification le : lundi 17 décembre 2018 - 10:30:08
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  • HAL Id : hal-00913669, version 1
  • ARXIV : 1310.3174


Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, Manuel Lopes. Multi-Armed Bandits for Intelligent Tutoring Systems. Journal of Educational Data Mining (JEDM), 2015, 7 (2), pp.20--48. 〈〉. 〈hal-00913669〉



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