Adaptive teaching: learning to teach

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dc.contributor.author Lakhani, Aazim
dc.date.accessioned 2018-12-20T17:49:55Z
dc.date.available 2018-12-20T17:49:55Z
dc.date.copyright 2018 en_US
dc.date.issued 2018-12-20
dc.identifier.uri http://hdl.handle.net/1828/10440
dc.description.abstract Traditional approaches to teaching were not designed to address individual student's needs. We propose a new way of teaching, one that personalizes the learning path for each student. We frame this use case as a contextual multi-armed bandit (CMAB) problem a sequential decision-making setting in which the agent must pull an arm based on context to maximize rewards. We customize a contextual bandit algorithm for adaptive teaching to present the best way to teach a topic based on contextual information about the student and the topic the student is trying to learn. To streamline learning, we add an additional feature which allows our algorithm to skip a topic that a student is unlikely to learn. We evaluate our algorithm over a synthesized unbiased heterogeneous dataset to show that our baseline learning algorithm can maximize rewards to achieve results similar to an omniscient policy. en_US
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Adaptive Teaching en_US
dc.subject Adaptive Learning en_US
dc.subject Machine Learning en_US
dc.subject Education en_US
dc.subject Multi-armed bandits en_US
dc.subject Contextual bandits en_US
dc.subject Student-centric learning en_US
dc.title Adaptive teaching: learning to teach en_US
dc.type Project en_US
dc.contributor.supervisor Mehta, Nishant
dc.degree.department Department of Computer Science en_US
dc.degree.level Master of Science M.Sc. en_US
dc.description.scholarlevel Graduate en_US

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