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- 12Graduate and Postdoctoral Studies (GPS), Faculty of /Theses and Dissertations
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"Identity" Constructions in Online Learning Events: Gender, Subjectivities, and the Productive Effects of Power
DownloadSpring 2013
ABSTRACT Advances in computer technology have created powerful opportunities for learners to engage with others, producing very different contexts for learning, and for negotiating our very way of being. Yet, engagement in these virtual learning environments also raises many questions around how...
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Spring 2016
Monte Carlo methods are a simple, effective, and widely deployed way of approximating integrals that prove too challenging for deterministic approaches. This thesis presents a number of contributions to the field of adaptive Monte Carlo methods. That is, approaches that automatically adjust the...
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Emotional and Social Engagement in a Massive Open Online Course: An examination of Dino 101
Download2016-01-01
Lia M. Daniels, Catherine Adams, Adam McCaffrey
Broadly defined as the connection between the learner and his or her learning, student engagement is a motivational construct involving behavioral, cognitive, emotional (Fredricks, Blumenfeld, & Paris, 2004) and social (Klassen, Yerdelen, & Durksen, 2013) components. Although all four components...
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2023-01-02
SSHRC IDG awarded 2023: Many of us have been frustrated when trying to learn something that we feel is difficult. Some of us chose to quit when facing frustration and others chose to work more so that they can overcome these difficulties. While several theories detail the role of adults’ emotions...
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Integration of Health Informatics in Baccalaureate Nursing Education: Effectiveness of Face-to-Face vs. Online Teaching Methods
DownloadFall 2012
Preparedness in informatics among future nurses continues to be a major concern for employers, nurse educators and graduates of undergraduate nursing programs. The purpose of this study was to develop an educational intervention about health informatics for undergraduate baccalaureate nursing...
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Fall 2022
Modern representation learning methods perform well on offline tasks and primarily revolve around batch updates. However, batch updates preclude those methods from focusing on new experience, which is essential for fast online adaptation. In this thesis, we study an online and incremental...
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Learning What to Remember: Strategies for Selective External Memory in Online Reinforcement Learning Agents
DownloadSpring 2019
In realistic environments, intelligent agents must learn to integrate information from their past to inform present decisions. An agent's immediate observations are often limited, and some degree of memory is necessary to complete many everyday tasks. However, an agent cannot remember everything...
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Fall 2012
In this thesis, the multi-armed bandit (MAB) problem in online learning is studied, when the feedback information is not observed immediately but rather after arbitrary, unknown, random delays. In the stochastic" setting when the rewards come from a fixed distribution, an algorithm is given that...
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Spring 2013
In a discrete-time online control problem, a learner makes an effort to control the state of an initially unknown environment so as to minimize the sum of the losses he suffers, where the losses are assumed to depend on the individual state-transitions. Various models of control problems have...
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Fall 2019
We study three problems in the application, design, and analysis of online optimization algorithms for machine learning. First, we consider speeding-up the common task of k-fold cross-validation of online algorithms, and provide TreeCV, an algorithm that reduces the time penalty of k-fold...