South African public schools in disadvantaged areas are experiencing serious levels of under resourcing which negatively impact the educational experiences of learners. Attempts to lessen such negative impact include involving alumni who know the school‟s context, history and ethos. Unfortunately, the concept of alumni support in terms of mentoring and motivating learners is not the norm in many under resourced schools. This research focuses on fostering partnerships with alumni using participatory action research (PAR), because it is holistic, relationally driven and inclusive. Embedded in complexity theory that views the school community as a nonlinear system of different interacting parts functioning to improve the school context, the research follows actionreflection cycles of a group of ten past pupils and five educators from various backgrounds, levels of education and expertise collaborating with and mobilizing other alumni. Data were generated using drawings, photo voice and interviews. Thematic data analysis was used to build patterns and form categories. The following themes emerged namely, the importance of establishing a collective vision for sustained alumni engagement for alumni‟s personal and professional aspirations to serve the vision of the school, the importance of creating an alumni culture that reinforces the concept of „paying it forward‟. Lastly, establishing a sustainable alumni association through sustained actions and interactions and by creating an organisation of excellence The newly developed alumni structure as a „resource fountain‟ generating and cascading energy around the school emerged as an anchor for sustainability. The cascaded energy evolved into a structured „Alumni Week‟ providing ongoing motivation for current learners to sustain alumni engagement.

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Increasingly, predictive models are used to support routine business de- cisions and are integral to the strategic competitive business strategies for a wide range of industries. Most often, data-driven predictive models are in- duced from training data obtained through the businesss routine operations. However, recent research on policies for intelligent information acquisitions suggests that proactive acquisition of information can improve models at a lower cost. Most active information acquisition policies are accuracy centric; they aim to identify acquisitions of training data that are particularly benefi- cial for improving the predictive accuracy of a given model. In practice, however, inferences from a predictive model are often used along with inferences from other predictive models as well as constant factors to inform arbitrarily complex decisions. In this dissertation, I discuss how these settings motivate a new kind of collaborative information acquisition (CIA) policies that exploit knowledge of the decision to allow multiple predictive models to collaboratively prioritize the prospective information acquisitions, so as to best improve the decisions they inform jointly. I present a framework for CIA policies and two specific CIA policies: CIA for binary decisions (CIA-BD), and CIA for top-ranked opportu- nities in terms of expected revenue (CIA-TR). Extensive empirical evaluations of the policies on real-world data suggest that the notion of CIA policies is indeed a valuable one. In particular, I demonstrate that these two new poli- cies lead to superior decision-making performances as compared to those of alternative policies that are either decision-centric or do not allow multiple models to collaboratively prioritize acquisitions. The performance exhibited by the CIA policies suggest that these policies are able to effectively exploit knowledge of the decisions to avoid greedy improvements in accuracy of any individual model informing the decisions; instead, they promote improvements in any one or all of the models when such improvements are likely to benefit the decisions. / text

Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern (also known as model selection) in linear models from observations contaminated by noise. We examine a scenario where a fraction of the zero co-variates are highly correlated with non-zero co-variates making sparsity recovery difficult. We propose two methods that adaptively increment the regularization parameter to prune the Lasso solution set. We prove that the algorithms achieve consistent model selection with high probability while using fewer samples than traditional Lasso. The algorithm can be extended to a broad set of L1-regularized M-estimators for linear statistical models.

Collecting and annotating videos of realistic human actions is tedious, yet critical for training action recognition systems. We propose a method to actively request the most useful video annotations among a large set of unlabeled videos. Predicting the utility of annotating unlabeled video is not trivial, since any given clip may contain multiple actions of interest, and it need not be trimmed to temporal regions of interest. To deal with this problem, we propose a detection-based active learner to train action category models. We develop a voting-based framework to localize likely intervals of interest in an unlabeled clip, and use them to estimate the total reduction in uncertainty that annotating that clip would yield. On three datasets, we show our approach can learn accurate action detectors more efficiently than alternative active learning strategies that fail to accommodate the "untrimmed" nature of real video data. / text

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.

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

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."