Adaptive teaching: learning to teach

dc.contributor.authorLakhani, Aazim
dc.contributor.supervisorMehta, Nishant
dc.date.accessioned2018-12-20T17:49:55Z
dc.date.available2018-12-20T17:49:55Z
dc.date.copyright2018en_US
dc.date.issued2018-12-20
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractTraditional 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.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/10440
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectAdaptive Teachingen_US
dc.subjectAdaptive Learningen_US
dc.subjectMachine Learningen_US
dc.subjectEducationen_US
dc.subjectMulti-armed banditsen_US
dc.subjectContextual banditsen_US
dc.subjectStudent-centric learningen_US
dc.titleAdaptive teaching: learning to teachen_US
dc.typeProjecten_US

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