424,889 research outputs found

    Efficient Lifelong Learning Algorithms: Regret Bounds and Statistical Guarantees

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    We study the Meta-Learning paradigm where the goal is to select an algorithm in a prescribed family \u2013 usually denoted as inner or within-task algorithm \u2013 that is appropriate to address a class of learning problems (tasks), sharing specific similarities. More precisely, we aim at designing a procedure, called meta-algorithm, that is able to infer this tasks\u2019 relatedness from a sequence of observed tasks and to exploit such a knowledge in order to return a within-task algorithm in the class that is best suited to solve a new similar task. We are interested in the online Meta-Learning setting, also known as Lifelong Learning. In this scenario the meta-algorithm receives the tasks sequentially and it incrementally adapts the inner algorithm on the fly as the tasks arrive. In particular, we refer to the framework in which also the within-task data are processed sequentially by the inner algorithm as Online-Within-Online (OWO) Meta-Learning, while, we use the term Online-Within-Batch (OWB) Meta-Learning to denote the setting in which the within-task data are processed in a single batch. In this work we propose an OWO Meta-Learning method based on primal-dual Online Learning. Our method is theoretically grounded and it is able to cover various types of tasks\u2019 relatedness and learning algorithms. More precisely, we focus on the family of inner algorithms given by a parametrized variant of Follow The Regularized Leader (FTRL) aiming at minimizing the withintask regularized empirical risk. The inner algorithm in this class is incrementally adapted by a FTRL meta-algorithm using the within-task minimum regularized empirical risk as the meta-loss. In order to keep the process fully online, we use the online inner algorithm to approximate the subgradients used by the meta-algorithm and we show how to exploit an upper bound on this approximation error in order to derive a cumulative error bound for the proposed method. Our analysis can be adapted to the statistical setting by two nested online-to-batch conversion steps. We also show how the proposed OWO method can provide statistical guarantees comparable to its natural more expensive OWB variant, where the inner online algorithm is substituted by the batch minimizer of the regularized empirical risk. Finally, we apply our method to two important families of learning algorithms parametrized by a bias vector or a linear feature map

    A meta-analysis of research of problem solving activities in online discussion

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    Today, technology has changed the teaching and learning method. Most of instructor and students are using computer and online medium to implement teaching and learning method even though they are not in the classroom. By using online medium, teaching and learning also can be occurredat anywhere andat anytime. Within the learning process, psychological processescan be occurred and same goes to online learning.Psychological processes could perform any types of activities that use a variety of processes such as thinking, remembering, problem solving, interpretation and others. One of the psychological processes in online learning is problem solving.Problem solving refers to the mental process that involves discovering, analyzing and solving problems (Cherry, 2003).Nowadays, by using variety of online medium,problem solving can be applied within teaching and learning through online discussion.This paper discusses about a meta-analysis of research of problem solving activities in online discussion

    Investigating knowledge building dialogues in networked communities of practice. A collaborative learning endeavor across cultures

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    We have analyzed data from two online courses, designed to promote collaborative online learning, and in the contexts of two different cultures. Ongoing assessment (self, group, instructor) that actively engages students within the course (as opposed to instructor assessment at the end) is a central feature for achieving collaborative knowledge building in online dialog. Careful articulation and deployment of assessment criteria is a design feature that promotes meta-awareness, which, in turn, together with student-centeredness and operationalization of student experiences in the design of the curriculum, enhances student participation, motivation and ownership in the dialog. From eight years of experience with online dialog and two quite different implementations we offer a set of design principles, having a sound theoretical basis, that enhance the quality and quantity of online knowledge building. Our analysis suggests that the characteristics of the discussion threads emerging under these design criteria give evidence of true collaborative learning.We have analyzed data from two online courses, designed to promote collaborative online learning, and in the contexts of two different cultures. Ongoing assessment (self, group, instructor) that actively engages students within the course (as opposed to instructor assessment at the end) is a central feature for achieving collaborative knowledge building in online dialog. Careful articulation and deployment of assessment criteria is a design feature that promotes meta-awareness, which, in turn, together with student-centeredness and operationalization of student experiences in the design of the curriculum, enhances student participation, motivation and ownership in the dialog. From eight years of experience with online dialog and two quite different implementations we offer a set of design principles, having a sound theoretical basis, that enhance the quality and quantity of online knowledge building. Our analysis suggests that the characteristics of the discussion threads emerging under these design criteria give evidence of true collaborative learning

    The Evaluation and Assessment of Online Skills Through Online Group Discussion

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    Increasingly much of a psychology student’s learning occurs within electronic environments, however rarely are the new skills they develop in these contexts identified, improved or assessed. Over the past 5 years, I have addressed this important need (Taylor, 2012) and examples from assessed online student discussions will be presented in this poster to demonstrate an innovative way to develop and assess online skills. These skills were formally assessed using two methods and examples will be provided on the poster. The first method involved tutor marking of online transcripts for evidence of critical thinking, online leadership and effective academic communication. The second method involved students reflecting on their experiences and perceived understanding of the psychology topic as a result of participating in the online discussions. Also, I will present findings from a separate empirical study to evaluate the impacts of shared online reflection on meta-cognitive awareness which employs both quantitative and qualitative methods. The final section of the poster will highlight examples where these new skills can be used to enhance psychology graduate employability in new technological environments. I will be interested to interact with attendees viewing my poster to discuss my techniques and findings and to explore future collaboration

    Investigating knowledge building dialogues in networked communities of practice. A collaborative learning endeavor across cultures

    Get PDF
    We have analyzed data from two online courses, designed to promote collaborative online learning, and in the contexts of two different cultures. Ongoing assessment (self, group, instructor) that actively engages students within the course (as opposed to instructor assessment at the end) is a central feature for achieving collaborative knowledge building in online dialog. Careful articulation and deployment of assessment criteria is a design feature that promotes meta-awareness, which, in turn, together with student-centeredness and operationalization of student experiences in the design of the curriculum, enhances student participation, motivation and ownership in the dialog. From eight years of experience with online dialog and two quite different implementations we offer a set of design principles, having a sound theoretical basis, that enhance the quality and quantity of online knowledge building. Our analysis suggests that the characteristics of the discussion threads emerging under these design criteria give evidence of true collaborative learning.We have analyzed data from two online courses, designed to promote collaborative online learning, and in the contexts of two different cultures. Ongoing assessment (self, group, instructor) that actively engages students within the course (as opposed to instructor assessment at the end) is a central feature for achieving collaborative knowledge building in online dialog. Careful articulation and deployment of assessment criteria is a design feature that promotes meta-awareness, which, in turn, together with student-centeredness and operationalization of student experiences in the design of the curriculum, enhances student participation, motivation and ownership in the dialog. From eight years of experience with online dialog and two quite different implementations we offer a set of design principles, having a sound theoretical basis, that enhance the quality and quantity of online knowledge building. Our analysis suggests that the characteristics of the discussion threads emerging under these design criteria give evidence of true collaborative learning

    Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control

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    Projection operations are a typical computation bottleneck in online learning. In this paper, we enable projection-free online learning within the framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures how the history of decisions affects the current outcome by allowing the online learning loss functions to depend on both current and past decisions. Particularly, we introduce the first projection-free meta-base learning algorithm with memory that minimizes dynamic regret, i.e., that minimizes the suboptimality against any sequence of time-varying decisions. We are motivated by artificial intelligence applications where autonomous agents need to adapt to time-varying environments in real-time, accounting for how past decisions affect the present. Examples of such applications are: online control of dynamical systems; statistical arbitrage; and time series prediction. The algorithm builds on the Online Frank-Wolfe (OFW) and Hedge algorithms. We demonstrate how our algorithm can be applied to the online control of linear time-varying systems in the presence of unpredictable process noise. To this end, we develop a controller with memory and bounded dynamic regret against any optimal time-varying linear feedback control policy. We validate our algorithm in simulated scenarios of online control of linear time-invariant systems.Comment: The version corrects proofs and updates presentatio

    The Relationship Between Motivation and Online Self-Regulated Learning

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    manage their own learning. The self-regulated learning practices of goal setting, environment structuring, task strategies, self-evaluation, time management, and help seeking are developed through experience and motivation. This study sought to determine the levels of self-regulated learning and identify the motivation constructs that correlated to the levels of self-regulated learning of students in an online agriculture dual enrollment course. Students had the highest self-regulation in the areas of goal setting and environment structuring. The lowest online learning self-regulation was in help seeking. Task value was the motivation construct receiving the highest mean score, while test anxiety received the lowest score. Relationships between online self-regulated learning and the motivation constructs of task value, self-efficacy, intrinsic motivation, extrinsic motivation, control beliefs, and test anxiety were statistically significant. Faculty in online courses are encouraged to aid in the development of help seeking, time management, and meta-analysis strategies. Faculty are also encouraged to incorporate valuable tasks within the online curriculum to increase students’ motivation to learn. Course developers are encouraged to incorporate problem-based learning, authentic assessments, and team-based learning approaches to better engage students. Research should continue to investigate these practices as they relate to increasing student motivation
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