70 research outputs found
Multi-Armed Bandits for Intelligent Tutoring 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
Personalized Stopping Rules in Bayesian Adaptive Mastery Assessment
We propose a new model to assess the mastery level of a given skill
efficiently. The model, called Bayesian Adaptive Mastery Assessment (BAMA),
uses information on the accuracy and the response time of the answers given and
infers the mastery at every step of the assessment. BAMA balances the length of
the assessment and the certainty of the mastery inference by employing a
Bayesian decision-theoretic framework adapted to each student. All these
properties contribute to a novel approach in assessment models for intelligent
learning systems. The purpose of this research is to explore the properties of
BAMA and evaluate its performance concerning the number of questions
administered and the accuracy of the final mastery estimates across different
students. We simulate student performances and establish that the model
converges with low variance and high efficiency leading to shorter assessment
duration for all students. Considering the experimental results, we expect our
approach to avoid the issue of over-practicing and under-practicing and
facilitate the development of Learning Analytics tools to support the tutors in
the evaluation of learning effects and instructional decision making.Comment: 12 page
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