Thesis (Ph.D.) - University of St Andrews, April 2008.

Thesis (Ph.D.) - University of Queensland, 2003. / Includes bibliography.

Thesis (M.S.)--Brigham Young University. Dept of Computer Science, 2006. / Includes bibliographical references (p. 69-70).

Thesis (Ph. D. in Teaching and Learning)--Vanderbilt University, Aug. 2006. / Title from title screen. Includes bibliographical references.

Thesis (Ph.D) - University of Queensland, 2003. / Includes bibliography.

Thesis (Ed. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 28, 2007) Vita. Includes bibliographical references.

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. / Science, Faculty of / Computer Science, Department of / Graduate

This phenomenological study explores how certain “innovative” pedagogies were experienced by a group of Chinese students studying Business Management at a mid-ranking UK university. Analysis of the transcripts of interviews (some in Chinese) with 24 students using NVivo shows that whilst most students felt that Active Learning pedagogies effectively supported their learning, for some students the “zone of indeterminacy” in which group projects and simulations were carried out was an uncomfortable space. Salient aspects of these students’ experiences were language, relationships and metacognitive skills, and the discussion explores the way in which these three experiential themes can be conceptualised as interrelated elements of the action (Biesta, 2006) which takes place in Active Learning classrooms. The following recommendations are made: HEIs should attempt to provide students with the advanced skills of negotiation which they will need to use in the flexible, ill-structured environments associated with Active Learning pedagogies; tutors should develop consistent approaches to collaborative assignments focussing on group work processes as well as task completion; the development of metacognitive skills through Active Learning pedagogies should be promoted through the use of explicit reflective elements embedded within the teaching, learning and assessment activities. The concluding discussion proposes that the successful use of Active Learning pedagogies requires a reconceptualisation of the purpose of education and that these pedagogies provide a potential readjustment of the balance between the functions of qualification, socialisation and subjectification (Biesta, 2010).

Community college leaders face challenges due to a lack of persistence data concerning 2-year colleges, especially in rural settings, prompting these leaders to turn to national data sets to drive local institutional changes. The purpose of this study was to identify variables associated with student place-frame and academic integration which are predictive of student persistence from the first to the second year in a small, residential community college in a rural frontier setting. Guided by Tinto's institutional departure theory, the theory of social representation, and Bassett's work in ruralism, a nonexperimental, correlational, quantitative research design was used to examine predictive relationships between student place-frame variables (age, sex, and intent to transfer), academic integration variables (student effort, collaborative learning, active learning, and academic challenge), and student persistence. Archival Community College Survey of Student Engagement data collected in 2013–2016 from 332 student participants were used for the study. Regression analysis showed a significant predictive relationship between student age and student intent to transfer with active learning. Additional binary logistical regression showed a significant positive relationship between active learning scores and student persistence. These findings informed development of evidence-based recommendations for programmatic changes to increase active learning practices, which could increase students' academic integration and persistence over time. By improving students' academic integration and persistence, positive social change may result through more students completing their degrees and their 2-year colleges gaining access to more substantial resources that are tied to student performance.

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