Current statistics suggest women form the majority of online learners. Their enrollment levels may be a result of promotional materials suggesting online learning allows learners access to flexible learning opportunities that will complement their busy lives. This research questions those assertions by examining the tensions women experience while learning online. Using a poststructural feminist approach, tensions are defined as the messy spaces where complexities, contradictions and competing ideas, actions, expectations, values and emotions interact to produce opposition and opportunities. Research questions asks: How do women learning online mediate tensions in the learning environment and in their own personal context? What tensions do women face when learning online? What strategies do they use to address these tensions? Are they able to find ways to balance or overcome these tensions? A poststructural feminist theoretical framework acknowledges the diversity of womens experiences and allows space for questioning discourse around lifelong learning, online learning, womens responsibilities, and institutional authority. Data was collected using multiple methods: photo-elicitation interviews and an online focus group plus a demographic survey and autoethnography. Twelve women, who all completed at least two online courses, participated representing learners of different ages, marital and family situations, geographical locations, and level and field of study. Six women took photographs, which formed the basis of face-to-face interviews. Six other women participated in an asynchronous online focus group. Themes from the results showed the tensions they experience, namely, the blurring between the boundaries between home and school, the cost of flexibility, and three strategies they used for mediating tensions (multitasking, procrastinating and persevering). While the women acknowledged the benefits of online learning and demonstrated that they were successful students, their narratives make it clear that they faced challenges in attending to and completing their schoolwork to the standards they desired, while meeting family and work responsibilities. A theoretical analysis explores how the poststructural feminist concepts of positionality and subjectivity are useful in examining womens experiences learning online and where there are gaps in applying this theoretical framework in online learning contexts. Participants narratives and photographs and the researchers own autobiographical narrative are included. / Adult Education

In Western society, food is debated and in various ways contested. Social science research has described various cultural imperatives related to food and choices of diet, that raises questions about how people understand issues of food and eating in their everyday lives. The aim of this study is to explore everyday notions of food and eating in urban Sweden. Drawing on social representations theory, qualitative interviews were carried out with fifteen men and women about their experiences and understandings of food and eating, also using a photo-elicitation method where visual material from cookery books and dietary advice were used as a point of departure for the interview conversation. The interviewees categorize food into different sorts, such as ‘ordinary food’, ‘modern food’, ‘dangerous food’, ‘healthy food’, ‘ethic food’ and ‘festive food’, that are ascribed a meaning in relation to different arenas in time and space, for instance childhood, and related to health values as well as ethical and aesthetic values. Food is also discussed as different diets, such as mixed or vegetarian, and patterns of eating, which are in turn related to risk, health and the body. The analysis thus reveal notions about what food is and how we should eat, notions that are characterised by internal tensions and contradictions such as discipline contra pleasure, societal norms contra personal interests, everyday life contra ideals. These ‘fields of tension’ are analysed as a cultural repertoire of identity-positions. Finally, these results are discussed in terms of risk and opportunities, where the reflexive human being is depicted as able to both incorporate food imperatives and to challenge these imperatives in a process of striving for bodily and mental balance.

As computing becomes ubiquitous and intelligent, it is possible for systems to adapt their behavior based on information sensed from the situational context. However, determining the context space has been taken for granted in most ubiquitous applications, and so that context-adaptive systems often miss the situational factors that are most relevant to users. The mismatch between a system's computational model and users' mental model of the context may frustrate and disorient users. This thesis describes the CCM (cognitive context model)-based approach for eliciting individual cognitive views of a context-aware task and selecting an appropriate context space for context-aware computing. It captures the situational and cognitive context for each task, using a structural architecture in which individual participants use a context view to describe their situational perspective of the task. Clustering and optimization techniques are applied to analyze and integrate context views in CCM. Developers can use the optimization output to identify an appropriate context space, specify context-aware adaptation policies and resolve run-time policy conflicts. This approach simplifies the task of context elicitation, emphasizes individual variance in context-aware activity, and helps avoid user requirements misunderstanding.

As computing becomes ubiquitous and intelligent, it is possible for systems to adapt their behavior based on information sensed from the situational context. However, determining the context space has been taken for granted in most ubiquitous applications, and so that context-adaptive systems often miss the situational factors that are most relevant to users. The mismatch between a system's computational model and users' mental model of the context may frustrate and disorient users. This thesis describes the CCM (cognitive context model)-based approach for eliciting individual cognitive views of a context-aware task and selecting an appropriate context space for context-aware computing. It captures the situational and cognitive context for each task, using a structural architecture in which individual participants use a context view to describe their situational perspective of the task. Clustering and optimization techniques are applied to analyze and integrate context views in CCM. Developers can use the optimization output to identify an appropriate context space, specify context-aware adaptation policies and resolve run-time policy conflicts. This approach simplifies the task of context elicitation, emphasizes individual variance in context-aware activity, and helps avoid user requirements misunderstanding.

Second home use or cottaging is an increasingly desirable practice across Canada. In Manitoba, cottaging sub-divisions are generally situated along lakes and rivers in the province, many of which are situated on Crown land or in provincial parks. This study explored what is meaningful about cottaging to bring about a better understanding of the importance of cottaging as a culturally meaningful social practice. The objectives of the research were to identify what makes Flanders Lake and surrounding area of Nopiming Provincial Park a meaningful place for cottagers; determine if there are shared meanings among cottagers; and consider the implications of the cultural landscape of cottagers for natural resource management. The case study research considered the sense of place that is derived from cottagers’ experiences and what specifically contributes to the significance of cottage culture in the cottaging sub-division of Flanders Lake, Manitoba. A qualitative, interpretive research design was used for data collection. Photo-elicitation also known as resident employed photography was used, whereby cottagers were asked to photograph things, places or people that represented meaningful aspects about cottaging and the surrounding area. Semi-structured interviews were conducted with individual participants to validate and triangulate perspectives. Using NVivo 7 qualitative software to manage data and elicit themes, a cultural landscape framework of cottagers was developed. Cottagers cited recreational activities, connections with people, interactions with nature and the tonic-like effects of cottaging experiences, as the most meaningful aspects of cottaging. The study showed that it is possible to determine the most meaningful aspects of a place, and that many of those attributes are shared between community members. However, dissecting those meanings into constituent parts of a collective body of shared, interrelated and sometimes dependent meanings is complex, and not always possible. Further study on the cultural landscape of additional cottaging communities and of other area stakeholders is recommended given the increasing desirability and interest in cottaging, and potential for negotiating place meanings in multiple land use areas. / October 2008

In many areas of Artificial Intelligence (AI), we are interested in helping people make better decisions. This help can result in two advantages. First, computers can process large amounts of data and perform quick calculations, leading to better decisions. Second, if a user does not have to think about some decisions, they have more time to focus on other things they find important. Since users' preferences are private, in order to make intelligent decisions, we need to elicit an accurate model of the users' preferences for different outcomes. We are specifically interested in outcomes involving a degree of risk or uncertainty. A common goal in AI preference elicitation is minimizing regret, or loss of utility. We are often interested in minimax regret, or minimizing the worst-case regret. This thesis examines three important aspects of preference elicitation and minimax regret. First, the standard elicitation process in AI assumes users' preferences follow the axioms of Expected Utility Theory (EUT). However, there is strong evidence from psychology that people may systematically deviate from EUT. Cumulative prospect theory (CPT) is an alternative model to expected utility theory which has been shown empirically to better explain humans' decision-making in risky settings. We show that the standard elicitation process can be incompatible with CPT. We develop a new elicitation process that is compatible with both CPT and minimax regret. Second, since minimax regret focuses on the worst-case regret, minimax regret is often an overly cautious estimate of the actual regret. As a result, using minimax regret can often create an unnecessarily long elicitation process. We create a new measure of regret that can be a more accurate estimate of the actual regret. Our measurement of regret is especially well suited for eliciting preferences from multiple users. Finally, we examine issues of multiattribute preferences. Multiattribute preferences provide a natural way for people to reason about preferences. Unfortunately, in the worst-case, the complexity of a user's preferences grows exponentially with respect to the number of attributes. Several models have been proposed to help create compact representations of multiattribute preferences. We compare both the worst-case and average-case relative compactness.

<p> Probability forecasts play an important role in many decision and risk analysis applications. Research and practice over the years have shown that the shift towards distributional forecasts provides a more accurate and appropriate means of capturing risk in models for these applications. This means that mathematical tools for analyzing the quality of these forecasts, may it come from experts, models or data, become important to the decision maker. In this regard, strictly proper scoring rules have been widely studied because of their ability to encourage assessors to provide truthful reports. This dissertation contributes to the scoring rule literature in two main areas of assessment - probability forecasts and quantile assessments. </p><p>In the area of probability assessment, scoring rules typically studied in the literature, and commonly used in practice, evaluate probability assessments relative to a default uniform measure. In many applications, the uniform baseline used to represent some notion of ignorance is inappropriate. In this dissertation, we generalize the power and pseudospherical family of scoring rules, two large parametric families of commonly-used scoring rules, by incorporating the notion of a non-uniform baseline distribution for both the discrete and continuous cases. With an appropriate normalization and choice of parameters, we show that these new families of scoring rules relate to various well-known divergence measures from information theory and to well-founded decision models when framed in an expected utility maximization context. </p><p>In applications where the probability space considered has an ordinal ranking between states, an important property often considered is sensitivity to distance. Scoring rules with this property provide higher scores to assessments that allocate higher probability mass to events “closer” to that which occurs based on some notion of distance. In this setting, we provide an approach that allows us to generate new sensitive to distance strictly proper scoring rules from well-known strictly proper binary scoring rules. Through the use of the weighted scoring rules, we also show that these new scores can incorporate a specified baseline distribution, in addition to being strictly proper and sensitive to distance. </p><p>In the inverse problem of quantile assessment, scoring rules have not yet been well-studied and well-developed. We examine the differences between scoring rules for probability and quantile assessments, and demonstrate why the tools that have been developed for probability assessments no longer encourage truthful reporting when used for quantile assessments. In addition, we shed light on new properties and characterizations for some of these rules that could guide decision makers trying to choosing an appropriate scoring rule. </p> / Dissertation

<p>Urbanization causes myriad changes in watershed processes, ultimately disrupting the structure and function of stream ecosystems. Urban development introduces contaminants (human waste, pesticides, industrial chemicals). Impervious surfaces and artificial drainage systems speed the delivery of contaminants to streams, while bypassing soil filtration and local riparian processes that can mitigate the impacts of these contaminants, and disrupting the timing and volume of hydrologic patterns. Aquatic habitats where biota live are degraded by sedimentation, channel incision, floodplain disconnection, substrate alteration and elimination of reach diversity. These compounding changes ultimately lead to alteration of invertebrate community structure and function. Because the effects of urbanization on stream ecosystems are complex, multilayered, and interacting, modeling these effects presents many unique challenges, including: addressing and quantifying processes at multiple scales, representing major interrelated simultaneously acting dynamics at the system level, incorporating uncertainty resulting from imperfect knowledge, imperfect data, and environmental variability, and integrating multiple sources of available information about the system into the modeling construct. These challenges can be addressed by using a Bayesian modeling approach. Specifically, the use of multilevel hierarchical models and Bayesian network models allows the modeler to harness the hierarchical nature of the U.S. Geological Survey (USGS) Effect of Urbanization on Stream Ecosystems (EUSE) dataset to predict invertebrate response at both basin and regional levels, concisely represent and parameterize this system of complicated cause and effect relationships and uncertainties, calculate the full probabilistic function of all variables efficiently as the product of more manageable conditional probabilities, and includes both expert knowledge and data. Utilizing this Bayesian framework, this dissertation develops a series of statistically rigorous and ecologically interpretable models predicting the effect of urbanization on invertebrates, as well as a unique, systematic methodology that creates an informed expert prior and then updates this prior with available data using conjugate Dirichlet-multinomial distribution forms. The resulting models elucidate differences between regional responses to urbanization (particularly due to background agriculture and precipitation) and address the influences of multiple urban induced stressors acting simultaneously from a new system-level perspective. These Bayesian modeling approaches quantify previously unexplained regional differences in biotic response to urbanization, capture multiple interacting environmental and ecological processes affected by urbanization, and ultimately link urbanization effects on stream biota to a management context such that these models describe and quantify how changes in drivers lead to changes in regulatory endpoint (the Biological Condition Gradient; BCG).</p> / Dissertation

In general academic research and discussion on stand-alone games or the broader computer games, scholars often use questionnaires and quantitative methods to analyze consumers' perceptions and opinions of game-related issues. Due to the limitations in research methods, previous research results are somewhat difficulty to make marketing companies or decision-making departments deeply understand consumers' ultimate mental imagery and consensus mode. Therefore, nowadays stand-alone game companies often spent a lot of expenses, but can not produce the games that meet consumers' core demand. According to the report released on September 7 2011 by the U.S. market survey center, DFC Intelligence, which specializes in the video game market, it estimates that the scale of the global market of video games will grow to 81 billion U.S. dollars in 2016 from 66 billion U.S. dolloars in 2010. This figure not only symbolizes the boom and huge business opportunities of game market but also affects the changes of the next-generation video game industry, which highlights the importance of conducting related research. This study uses a mental image mode, ZMET metaphor extraction techniques, invented by Harvard University Professor Zaltman as the research method, trying to elicit contruct factors of six senior consumers for the stand-alone game, FINAL FANTASY VII, through image analysis. Research results can provide marketing implications to stand-alone game companies so that they can understand consumer needs and expectations by the imagery constructs and mental models of the senior players. The results of ZMET technical analysis got a total of 122 different constructs from six respondents, from which 49 common constructs were further extracted. This study found HVM hierarchical value consensus mental map among the six senior players and clarified the evolution of the six respondents' imagery constructs of the game and consensus model. As to the final structure constructs in HVM hierarchical value map, five ultimate values are identified, "players' favorability", "looking to the future," "witness", "dreams come true", and "classic". The ultimate values combined by the five non-verbal information can provide marketers and game designers in stand-alone game companies, a reference for future marketing decisions. Additionally, based on the research findings, marketers can design better stand-alone games that meet consumer demands, in order to effectively increase the sales and economic benefits for game companies.

We live in an image world nowadays. The government propagates policy to civilians using various kinds of visual media for communication. Dwellers make sense of temporality and space of the city through eyes and brain. Yet, any official expectation that citizens would agree with the city image will not be achieved without prior knowledge of citizens' value system. In the meantime, building city image is a strategy of enhancing competitiveness of a city. Adopting Zaltman method of elicitation technique (ZMET), this research explores the mind maps of six Kaohsiung city marketers, also city dwellers, by a series of visual elicitation. The end result is the consensus map that reflects their common thought or value system. Research finds that the prime values of their city image are aesthetics, romance, vigor, happiness, subjective well-being, and localism. Their importance is in that order. These findings not only reflects city marketers' core value but contributes to future projection and marketing of Kaohsiung's city image.