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).

Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset. We refer to the scheme as Bounded Expectation of Label Assignment (BELA). BELA greedily queries an oracle (or human labeler) and partitions a dataset to find data subsets that have mostly the same label. BELA can then infer labels by majority vote of the known labels in each subset. BELA makes the decision to split or label from a subset by maximizing a lower bound on the expected number of correctly labeled examples. BELA improves upon existing hierarchical labeling schemes by using supervised models to partition the data, therefore avoiding reliance on unsupervised clustering methods that may not accurately group data by label. We design BELA with strategies to avoid bias that could be introduced through this adaptive partitioning. We evaluate BELA on labeling of four datasets and find that it outperforms existing strategies for adaptive labeling. / Master of Science / Most machine learning classifiers require data with both features and labels. The features of the data may be the pixel values for an image, the words in a text sample, the audio of a voice clip, and more. The labels of a dataset define the data. They place the data into one of several categories, such as determining whether a image is of a cat or dog, or adding subtitles to Youtube videos. The labeling of a dataset can be expensive, and usually requires a human to annotate. Human labeled data can be moreso expensive if the data requires an expert labeler, as in the labeling of medical images, or when labeling data is particularly time consuming. We introduce a scheme for labeling data that aims to lessen the cost of human labeled data by labeling a subset of an entire dataset and making an educated guess on the labels of the remaining unlabeled data. The labeled data generated from our approach may be then used towards the training of a classifier, or an algorithm that maps the features of data to a guessed label. This is based off of the intuition that data with similar features will also have similar labels. Our approach uses a game-like process of, at any point, choosing between one of two possible actions: we may either label a new data point, thus learning more about the dataset, or we may split apart the dataset into multiple subsets of data. We will eventually guess the labels of the unlabeled data by assigning each unlabeled data point the majority label of the data subset that it belongs to. The novelty in our approach is that we use supervised classifiers, or splitting techniques that use both the features and the labels of data, to split a dataset into new subsets. We use bias reduction techniques that enable us to use supervised splitting.

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.