Multi-Armed Bandits for Intelligent Tutoring Systems

TL;DR

This paper introduces adaptive algorithms for intelligent tutoring systems that personalize learning activities using multi-armed bandit techniques to optimize student progress, incorporating expert knowledge and empirical learning progress estimation.

Contribution

It presents two novel algorithms, RiARiT and ZPDES, that utilize empirical learning progress and expert guidance to enhance personalized educational interventions.

Findings

Algorithms outperform baseline methods in simulated tests.

System achieves significant improvements in student skill acquisition.

User study confirms effectiveness with 400 children.

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.