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Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning To Induce Pedagogical Tutorial Tactics

Chi, Min (2010) Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning To Induce Pedagogical Tutorial Tactics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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In this dissertation, I investigated applying a form of machine learning, reinforcement learning, to induce tutorial tactics from pre-existing data collected from real subjects. Tutorial tactics are policies as to how the tutor should select the next action when there are multiple ones available at each step. In order to investigate whether micro-level tutorial decisions would impact students' learning, we induced two sets of tutorial tactics: the ``Normalized Gain' tutorial tactics were derived with the goal of enhancing the tutorial decisions that contribute to the students' learning while the "Inverse Normalized Gain" ones were derived with the goal of enhancing those decisions that contribute less or even nothing to the students' learning. The two sets of tutorial tactics were compared on real human participants. Results showed that when the contents were controlled so as to be the same, different tutorial tactics would indeed make a difference in students' learning gains. The "Normalized Gain" students out-performed their ``Inverse Normalized Gain' peers. This dissertation sheds some light on how to apply reinforcement learning to induce tutorial tactics in natural language tutoring systems.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairLitman, Diane Jlitman@cs.pitt.eduDLITMAN
Committee MemberMostow,
Committee MemberVanLehn,
Committee MemberDruzdzel, Marek Jmarek@sis.pitt.eduDRUZDZEL
Committee MemberBrusilovsky, Peterpeterb@mail.sis.pitt.eduPETERB
Date: 28 January 2010
Date Type: Completion
Defense Date: 20 November 2009
Approval Date: 28 January 2010
Submission Date: 9 December 2009
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Intelligent Tutoring System; Pedagogical Strategies; Reinforcement Learning
Other ID:, etd-12092009-144719
Date Deposited: 10 Nov 2011 20:09
Last Modified: 15 Nov 2016 13:54


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