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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Professional development and beyond : a participative study of a self-facilitated learning group

Goodall, Helen January 2015 (has links)
This is a participative case study of a self-facilitating, collaborative, women’s learning group. The group’s longevity afforded a unique opportunity to investigate, in depth, both what encouraged its members to join at its outset, and what has sustained the participation of its current members for thirteen years. Its longevity also provided an opportunity to explore the impact of sustained membership on the women in the group. These two components of the study are its most significant original contributions to the existing literature which does not appear to cover anything similar. The initial raison d’étre of the group was its members’ professional development and this forms a central strand of the investigation, along with identity and self-facilitation. A pragmatic research paradigm, the collaborative nature of the group and the writer’s dual role as both participant and researcher were all influential in the decision to use a participative approach. A range of methods, chosen by the participants, was utilised during the investigation which, whilst participatory, is not emancipatory research. This experimental divergence from how a participative approach is traditionally employed is offered for consideration by researchers who wish to work in a new way that minimises power in other, non-emancipatory situations. The findings support, contradict and add to the literature. The mutuality of longevity and the depth of discourse and learning experienced by group members is a particularly striking aspect of this study. As members of the group have aged, its focus has segued from professional development to encompass a much broader agenda: it has shifted from contributing to members’ professional identity to sustaining their perceptions of self as women who remain capable of complex, critical thinking as they move out of full-time work. The longevity of the group has also fostered deep attachments between group members, despite the differences between them: sustained membership of the group, in turn, provides sustenance for its members. The significance of grounding, ground rules and group composition are highlighted, as is the need to contemplate how members will leave a group during its formation. Alignment between participants in a group is identified as important for its continuation but not always possible. This research makes no claim to offer a definitive model for collaborative learning groups but, instead poses a series of questions for consideration by others who are interested in collaborative learning.
32

An Examination of High School Student Success in Online Learning

Eaton, Gina N. 03 July 2020 (has links)
No description available.
33

The Effects Of Cooperative Learning On Learning Outcomes And Reactions To Training In An In-service Training Course

Gokmen, Suheyla 01 March 2009 (has links) (PDF)
The purpose of this study is to compare the effects of cooperative learning method and individualistic learning method on learning outcomes and training reactions of adults participating an in-service training course. The study was conducted with 42 adults in pilot study and 92 in main study conducted in a government bank. Subjects were randomly assigned to two pilot study groups and four main study groups. Two different training programs were developed, one for individualistic learning, and the other for cooperative learning in order to test the effect of each method on learning outcomes and training reactions. The content and length of the training programs taught were held constant, and duration of training was totally 15 hours (3 hours in each of the five days). Participants, in all groups, learned the same topic of &ldquo / Structured On-the-Job Training&rdquo / and were taught by the same trainer. Cooperative learning groups worked on the exercises structured with the five basic elements of cooperative learning, and the individualistic learning groups worked as individually with the instructor calling on participants at random. Learning Outcomes Tests were administered at the end of each day to measure cognitive learning outcomes, which learners attained during the Training. Training Reactions Questionnaire was administered at the end of the Training. A significant difference between the cooperative learning group and the individualistic learning group was examined concerning learning outcomes as a result of ANCOVA by using the age as covariate. Subjects in the cooperative learning group had a significantly higher level of Learning Outcomes Test score than did those in the individualistic learning group. However, there was no significant difference between the cooperative learning groups and individualistic learning groups based on their training reactions. This study indicated that cooperative learning appears to be a method of instruction that is well suited to the needs of adult learners. Subjects of the study learned more through the cooperative learning method than individualistic learning method that was used. They responded to training as much positive as their counterparts learning in individualistic learning group. Results of the study suggest that structuring positive social interdependence in the classroom through cooperative learning procedures can be used effectively within adult education and specifically training settings.
34

Mobile Learning Effectiveness in Higher Education

Yaqub, Naveed, Iqbal, Atif January 2010 (has links)
<p>This research investigates mobile learning effectiveness in higher education. Mobile learning is composition of two words Mobile and Learning. In simple words mobile learning is mobility of learners by using mobile technologies in learning environment. Many researches addressed mobile learning but few of them covered mobile learning effectiveness. This study explores mobile learning effectiveness with the help of learning theories and models. Behaviorist, cognitive, humanistic, situational, and mobile learning theories are discussed that elaborate social, psychological, and philosophical aspects of learning.  Detailed evolution of learning is also part of this report that covers the literature of distance learning, electronic learning as well as formal and informal learning. Three effective learning models are taken in consideration: the Garrison’s Community of Inquiry, the Swan’s Interactivity and Online Learning, and the Danaher and his colleagues’ model of mobile learning and teaching evaluation model. Danaher’s model is selected as a conceptual framework of the study that is composed of three elements that are engagement, presence and flexibility. Engagement is the active participation of the learner in learning activities. Presence means being there, physically or mentally, in learning activity or place. Flexibility is how easy and facilitative the system is for teachers and students. These three elements are used to determine mobile learning effectiveness.  Survey method was used as our research approach. Empirical data was collected from Linnaeus University (prev. Växjö University) Sweden, by using two separate questionnaires for students and teachers. Collected data was analyzed with respect to learning theories and the theoretical model. The result reveals the potential of mobile learning as an effective mode of learning in terms of engagement and presence but flexibilty approved to be weaker in mobile learning.</p>
35

Opportunities for all learners to achieve their potential : an investigation into the effects of learning talk in the secondary school classroom

Williams, Sharon January 2014 (has links)
A major challenge to contemporary education is to meet the Government’s directive, depicted in OFSTED guidelines and the Department for Education’s Teacher Standards that all our learners make progress, are autonomous and are able to engage in independent learning. However they offer no guidance as to how this can be achieved. The research has built on earlier theories to close the gap between Government measurements of the quality of teaching and twenty-first century educational theories, with particular focus on learning talk. The primary intention of this research was to determine the impact that dynamically dialogic learning conversations, that is learning talk, have on deepening learning, and how they may be used to enable teachers to meet OFSTED’s requirement for all students to make progress. The data for this case study was collected through a process of lesson observations, interviews and focus-group discussions over a period of one year. Sixteen lessons were video-recorded for a variety of topics and the recordings were analysed in depth against established theories of learning and the complex patterns and relationships between the different types of student and teacher learning talk observed in the classroom. The outcome of the analysis is a set of observable characteristics of learning talk which form an Observation Database. The findings support the premise that learning talk in the classroom leads to deeper learning. The Observation Database contains of a set of tools for observing, evaluating and enabling learning talk in the classroom and therefore offers teachers the opportunity to demonstrate OFSTED criteria. The process of developing the Observation Database and the tools developed have been shared both locally and nationally to heighten awareness of learning talk in the classroom and its link to deeper learning.
36

Learning with unlabeled data. / 在未標記的數據中的機器學習 / CUHK electronic theses & dissertations collection / Zai wei biao ji de shu ju zhong de ji qi xue xi

January 2009 (has links)
In the first part, we deal with the unlabeled data that are in good quality and follow the conditions of semi-supervised learning. Firstly, we present a novel method for Transductive Support Vector Machine (TSVM) by relaxing the unknown labels to the continuous variables and reducing the non-convex optimization problem to a convex semi-definite programming problem. In contrast to the previous relaxation method which involves O (n2) free parameters in the semi-definite matrix, our method takes advantage of reducing the number of free parameters to O (n), so that we can solve the optimization problem more efficiently. In addition, the proposed approach provides a tighter convex relaxation for the optimization problem in TSVM. Empirical studies on benchmark data sets demonstrate that the proposed method is more efficient than the previous semi-definite relaxation method and achieves promising classification results comparing with the state-of-the-art methods. Our second contribution is an extended level method proposed to efficiently solve the multiple kernel learning (MKL) problems. In particular, the level method overcomes the drawbacks of both the Semi-Infinite Linear Programming (SILP) method and the Subgradient Descent (SD) method for multiple kernel learning. Our experimental results show that the level method is able to greatly reduce the computational time of MKL over both the SD method and the SILP method. Thirdly, we discuss the connection between two fundamental assumptions in semi-supervised learning. More specifically, we show that the loss on the unlabeled data used by TSVM can be essentially viewed as an additional regularizer for the decision boundary. We further show that this additional regularizer induced by the TSVM is closely related to the regularizer introduced by the manifold regularization. Both of them can be viewed as a unified regularization framework for semi-supervised learning. / In the second part, we discuss how to employ the unlabeled data for building reliable classification systems in three scenarios: (1) only poorly-related unlabeled data are available, (2) good quality unlabeled data are mixed with irrelevant data and there are no prior knowledge on their composition, and (3) no unlabeled data are available but can be achieved from the Internet for text categorization. We build several frameworks to deal with the above cases. Firstly, we present a study on how to deal with the weakly-related unlabeled data, called the Supervised Self-taught Learning framework, which can transfer knowledge from the unlabeled data actively. The proposed model is able to select those discriminative features or representations, which are more appropriate for classification. Secondly, we also propose a novel framework that can learn from a mixture of unlabeled data, where good quality unlabeled data are mixed with unlabeled irrelevant samples. Moreover, we do not need the prior knowledge on which data samples are relevant or irrelevant. Consequently it is significantly different from the recent framework of semi-supervised learning with universum and the framework of Universum Support Vector Machine. As an important contribution, we have successfully formulated this new learning approach as a Semi-definite Programming problem, which can be solved in polynomial time. A series of experiments demonstrate that this novel framework has advantages over the semi-supervised learning on both synthetic and real data in many facets. Finally, for third scenario, we present a general framework for semi-supervised text categorization that collects the unlabeled documents via Web search engines and utilizes them to improve the accuracy of supervised text categorization. Extensive experiments have demonstrated that the proposed semi-supervised text categorization framework can significantly improve the classification accuracy. Specifically, the classification error is reduced by 30% averaged on the nine data sets when using Google as the search engine. / We consider the problem of learning from both labeled and unlabeled data through the analysis on the quality of the unlabeled data. Usually, learning from both labeled and unlabeled data is regarded as semi-supervised learning, where the unlabeled data and the labeled data are assumed to be generated from the same distribution. When this assumption is not satisfied, new learning paradigms are needed in order to effectively explore the information underneath the unlabeled data. This thesis consists of two parts: the first part analyzes the fundamental assumptions of semi-supervised learning and proposes a few efficient semi-supervised learning models; the second part discusses three learning frameworks in order to deal with the case that unlabeled data do not satisfy the conditions of semi-supervised learning. / Xu, Zenglin. / Advisers: Irwin King; Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 158-179). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
37

Možnosti e-learningu v podmínkách distančního studia na FIS VŠE Praha / Possibilities of use of e-learning applications in distance learning at FIS VŠE Prague

Turčín, Pavel January 2010 (has links)
Main subjects of this thesis are distant learning and e-learning. It provides an overview of current state of distance learning in tertiary education in Czech republic, focusing mainly on informatics programs. It identifies limitations of standard e-learning systems regarding the requirements of courses on the Faculty of Informatics and Statistics at University of Economics, Prague. Practical part of this thesis is describing implementation and use of special application that provides functionality that is not provided by standard e-learning systems.
38

Role e-learningu v prostředí současné vysoké školy / The Role of e-Learning Environment in the Current High School

Koubková, Jana January 2011 (has links)
This thesis deals with distance education in the setting of high schools. The first part describes the history, then the possibility of e learning and its basic components. Another part is focused on the description of the current offer of educational systems. The practical part focuses on the characterization and comparison of LMS systems. The last chapter deals with ways of graduation at the Faculty of Management and describes the courses with which students have the most problems.
39

Application of prior information to discriminative feature learning

Liu, Yang January 2018 (has links)
Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image classification as most prevalent feature learning methods currently do, we also explore some other general-purpose priors and verify their effectiveness in the discriminant feature learning. As a more powerful representation can be learned by implementing such general priors, our approaches achieve state-of-the-art results on challenging benchmarks. We elaborate on these general-purpose priors and highlight where we have made novel contributions. We apply sparsity and hierarchical priors to the explanatory factors that describe the data, in order to better discover the data structure. More specifically, in the first approach we propose that we only incorporate sparse priors into the feature learning. To this end, we present a support discrimination dictionary learning method, which finds a dictionary under which the feature representation of images from the same class have a common sparse structure while the size of the overlapped signal support of different classes is minimised. Then we incorporate sparse priors and hierarchical priors into a unified framework, that is capable of controlling the sparsity of the neuron activation in deep neural networks. Our proposed approach automatically selects the most useful low-level features and effectively combines them into more powerful and discriminative features for our specific image classification problem. We also explore priors on the relationships between multiple factors. When multiple independent factors exist in the image generation process and only some of them are of interest to us, we propose a novel multi-task adversarial network to learn a disentangled feature which is optimized with respect to the factor of interest to us, while being distraction factors agnostic. When common factors exist in multiple tasks, leveraging common factors cannot only make the learned feature representation more robust, but also enable the model to generalise from very few labelled samples. More specifically, we address the domain adaptation problem and propose the re-weighted adversarial adaptation network to reduce the feature distribution divergence and adapt the classifier from source to target domains.
40

An architecture for situated learning agents

Mitchell, Matthew Winston, 1968- January 2003 (has links)
Abstract not available

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