321 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
A Methodology for Discovering how to Adaptively Personalize to Users using Experimental Comparisons
We explain and provide examples of a formalism that supports the methodology
of discovering how to adapt and personalize technology by combining randomized
experiments with variables associated with user models. We characterize a
formal relationship between the use of technology to conduct A/B experiments
and use of technology for adaptive personalization. The MOOClet Formalism [11]
captures the equivalence between experimentation and personalization in its
conceptualization of modular components of a technology. This motivates a
unified software design pattern that enables technology components that can be
compared in an experiment to also be adapted based on contextual data, or
personalized based on user characteristics. With the aid of a concrete use
case, we illustrate the potential of the MOOClet formalism for a methodology
that uses randomized experiments of alternative micro-designs to discover how
to adapt technology based on user characteristics, and then dynamically
implements these personalized improvements in real time
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments
Randomized experiments ensure robust causal inference that are critical to
effective learning analytics research and practice. However, traditional
randomized experiments, like A/B tests, are limiting in large scale digital
learning environments. While traditional experiments can accurately compare two
treatment options, they are less able to inform how to adapt interventions to
continually meet learners' diverse needs. In this work, we introduce a trial
design for developing adaptive interventions in scaled digital learning
environments -- the sequential randomized trial (SRT). With the goal of
improving learner experience and developing interventions that benefit all
learners at all times, SRTs inform how to sequence, time, and personalize
interventions. In this paper, we provide an overview of SRTs, and we illustrate
the advantages they hold compared to traditional experiments. We describe a
novel SRT run in a large scale data science MOOC. The trial results
contextualize how learner engagement can be addressed through inclusive
culturally targeted reminder emails. We also provide practical advice for
researchers who aim to run their own SRTs to develop adaptive interventions in
scaled digital learning environments
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
publishedVersio
Getting too personal(ized): The importance of feature choice in online adaptive algorithms
Digital educational technologies offer the potential to customize students'
experiences and learn what works for which students, enhancing the technology
as more students interact with it. We consider whether and when attempting to
discover how to personalize has a cost, such as if the adaptation to personal
information can delay the adoption of policies that benefit all students. We
explore these issues in the context of using multi-armed bandit (MAB)
algorithms to learn a policy for what version of an educational technology to
present to each student, varying the relation between student characteristics
and outcomes and also whether the algorithm is aware of these characteristics.
Through simulations, we demonstrate that the inclusion of student
characteristics for personalization can be beneficial when those
characteristics are needed to learn the optimal action. In other scenarios,
this inclusion decreases performance of the bandit algorithm. Moreover,
including unneeded student characteristics can systematically disadvantage
students with less common values for these characteristics. Our simulations do
however suggest that real-time personalization will be helpful in particular
real-world scenarios, and we illustrate this through case studies using
existing experimental results in ASSISTments. Overall, our simulations show
that adaptive personalization in educational technologies can be a double-edged
sword: real-time adaptation improves student experiences in some contexts, but
the slower adaptation and potentially discriminatory results mean that a more
personalized model is not always beneficial.Comment: 11 pages, 6 figures. Correction to the original article published at
https://files.eric.ed.gov/fulltext/ED607907.pdf : The Thompson sampling
algorithm in the original article overweights older data resulting in an
overexploitative multi-armed bandit. This arxiv version uses a normal
Thompson sampling algorith
Online Optimization and Personalization of Teaching Sequences
International audienceIn this work we are focused on the tutoring model, that is, how to choose the activities that provide a better learning experience based on the estimation of the student competence levels and progression, and some knowledge about the cognitive and student model. We can imagine a student wanting to acquire many different skills, e.g. adding, subtracting and multiplying numbers. A teacher can help by proposing activities such as: multiple choice questions, abstract operations to compute with a pencil, games where items need to be counted through manipulation, videos, or others. The challenge is to decide what is the optimal sequence of activities that maximizes the average competence level over all skills
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