Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 168-173).

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Vijay K. Shanker, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (M.S.)--Rowan University, 2009. / Typescript. Includes bibliographical references.

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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This study examined the effect of active learning seminars and a mentoring program on the use of active learning teaching techniques by college faculty. A quasi-experimental study was conducted using convenience samples of faculty from two private Christian supported institutions. Data for the study were collected from surveys and faculty course evaluations. The study lasted one semester. Faculty volunteers from one institution served as the experimental group and faculty volunteers from the second institution were the comparison group. The experimental group attended approximately eight hours of active learning seminars and also participated in a one-semester mentoring program designed to assist faculty in application of active learning techniques. Several individuals conducted the active learning seminars. Dr. Charles Bonwell, a noted authority on active learning, conducted the first three-hour seminar. Seven faculty who had successfully used active learning in their classrooms were selected to conduct the remaining seminars. The faculty-mentoring program was supervised by the researcher and conducted by department chairs. Data were collected from three surveys and faculty course evaluations. The three surveys were the Faculty Active Learning Survey created by the researcher, the Teaching Goals Inventory created by Angelo and Cross, and the college edition of Learner-Centered Practices by Barbara McCombs. The use of active learning techniques by the experimental group increased significantly more than the use by those in the convenience sample. No statistical difference was found in the change of professors' teaching beliefs or the course evaluation results.

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.

Recent efforts in engineering education research have set in motion reform advocating more active learning in the classroom. Active learning centers on the student and consists of pedagogical approaches to address the broad spectrum of educational backgrounds and demographics. In order to further the research focused on active learning products, appropriate and innovative assessment methods must be developed. For this thesis, innovative active learning modules are the focus of the analysis. In total, 12 Finite Element tutorials are designed and assessed using both statistical analysis and confidence interval correlations. Fundamental and informative assessment strategies have been developed to iteratively improve active learning approaches. Results of this process show that the finite element tutorials lead to enhanced student learning that can span across student demographics. Certain cases do exist where unique learning styles or personality types respond more positively to this pedagogical technique than others. Global outcomes are presented to assess these tutorials cumulatively, as active learning products. Finally, the assessment methodology is redesigned into a useful toolkit for educators to follow in furthering efforts of integrating active learning into any engineering classroom. / text

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