Thesis (Ed. D.)--Graduate School of Education, Oral Roberts University, 2006. / Includes abstract and vita. Includes bibliographical references (leaves 157-168).

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.

Thesis (EdD)--Open University.

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.

Active learning should be the ultimate aim of education. I argue that it is a three interrelated-step model of curriculum: one which promotes critical thinking, involves dialogue and ultimately indicates growth. It is a model intertwined in an intricate web of ideas borrowed from John Dewey and Paulo Freire. In this thesis, I analyze the International Baccalaureate (IB) Theory of Knowledge (TOK) curriculum as an example of a document that seeks to foster active learning. To be able to analyze whether the IB TOK curriculum promotes active learning, I dissect the curriculum in terms of its philosophy and objectives. Curriculum theorists do not agree on a universal definition of curriculum. Therefore, I explore four distinctive theories of curriculum and theory in order to find a definition that best fits the IB TOK curriculum and philosophy: 1) curriculum as a body of knowledge to be deposited, 2) curriculum as a product theory, 3) curriculum as a process, and 4) curriculum as praxis. I argue that in order for active learning to take place, the three components of active learning need to exist together. Active learning needs to promote critical thinking as a means to understanding one’s self and others. And, active learning needs to involve dialogue to enable people to become fully aware of their own position within the community and the world, and that of others. Critical thinking and dialogue in turn ensure growth. Growth is defined in terms of conscientização and Praxis; this is premised on two conditions: 1) to become aware of the realities in one’s life; and, 2) to take informed and practical actions to change these assumptions. It is then, I argue, that learning becomes active. It is indeed, as Freire would say, breaking away from ‘silence’ imposed on us by oppressors and attaining “the freedom of the learner” in Dewey’s words. It is only through active learning that individuals can critically think, enter a meaningful dialogue with others, and ultimately have the courage to act, and as a result create a life which is meaningful—not just for themselves but for everyone. / Thesis (Master, Education) -- Queen's University, 2009-08-07 17:56:13.739

No description available.

This inquiry is about the sense-making of students in a technology education class as they build a prototype electric car in a secondary school manufacturing shop. I make sense of their sense-making by examining their talk and interaction in the interplay of the social, material, institutional, and organizational resources constituting what I call "distributed practice." This involves a move away from defining understanding and learning as self-contained structures in the minds of people, but instead sees learning as spread out in the broad social context of activity and participation. Distributed practice theorized in this way is about the interplay among "complex social relations, technologically constituted." Technologies and their use in practice provide us with a realm through which we can discuss issues related to the understanding of learners. In many respects, this dissertation is an exploration of how "the way things are done" becomes understanding and alternately, how understanding becomes "the way things are done." The analysis moves towards a social and cultural practice view of learning I call "practical negotiation."

This study investigates how an active language learner is constructed in the context of teacher education for teaching English as a foreign language (TEFL) in Vietnam, as well as the supportive factors and challenges in developing learner activeness in language learning. / Thesis (PhD)--University of South Australia, 2006.

Thesis (PhD)--University of South Australia, 2001

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 108-114).