In Canada, out of the total Aboriginal populations, the older Aboriginal adult populations continue to increase and suffer disproportionately poor health compared to non-Aboriginal seniors. Despite these facts, there is a dearth of research concerning Aboriginal older adults, especially regarding their engagement with physical activity. My thesis is written in the publishable paper format and is comprised of two papers. Using a scoping study methodology in paper one, I demonstrate that intersecting factors such as colonialism, social inequities, and physical activity practices that are rooted in Western ideals and do not address Indigenous older adults’ needs, all contribute to Indigenous older adults’ lack of participation in physical activity. Using a case study approach in paper two, I demonstrate how Elders In Motion, a program offered by the Northwest Territories Recreation and Parks Association (NWTRPA), shows a shift in physical activity programs becoming more respectful of older Aboriginal adults’ cultures and communities; however, while the staff at the NWTRPA has made a strong effort to adapt a southern-based program for northern communities in the NWT, there are several program features that reaffirm colonial practices and support Westernized ideas of physical activity. Taken together, the papers in this thesis make apparent that physical activity programs and research concerning physical activity for older Aboriginal adults continue to be embedded in colonial practices. As a result, there is a demonstrated need for program development and research in this area to work towards reducing health disparities and challenging colonial practices.

This purpose of this interpretive research study was to deepen our understanding of the meaning of culturally relevant physical education and health pedagogy (Ladson Billings, 1994; Halas, McRae & Carpenter, 2012) from the perspective of racialized minority women. Four female students from diverse backgrounds participated in a talking circle where they discussed their experiences in physical education and health (PEH) settings. Wilson’s (2008) idea of relational accountability, as described through Indigenous approaches to research, was used to help interrogate and interrupt systems of privilege, power and marginalization that characterize many PEH settings. The findings reveal the on-going need for culturally relevant pedagogical approaches that encourage, affirm and recognize the cultural landscapes of students. More research is needed to understand how students can be motivated to learn and grow in ways that develop their critical social consciousness regarding the social inequities that impact their PEH experiences. / October 2015

Knowledge is a critical dimension in the problem solving processes of human intelligence. Consequently, enabling intelligent systems to provide advanced services requires that their artificial intelligence routines have access to knowledge of relevant domains. Ontologies are often utilised as the formal conceptualisation of domains, in that they identify and model the concepts and relationships of the targeted domain. However complexities inherent in ontology development and maintenance have limited their availability. Separate from the conceptualisation component, domain knowledge also encompasses the concept membership of object instances within the domain. The need to capture both the domain model and the current state of instances within the domain has motivated the import of Formal Concept Analysis into intelligent systems research. Formal Concept Analysis, which provides a simplified model of a domain, has the advantage in that not only does it define concepts in terms of their attribute description but object instances are simultaneously ascribed to their appropriate concepts. Nonetheless, a significant drawback of Formal Concept Analysis is that when applied to a large dataset, the lattice with which it models a domain is often composed of a copious amount of concepts, many of which are arguably unnecessary or invalid. In this research a novel measure is introduced which assigns a relevance value to concepts in the lattice. This measure is termed the Collapse Index and is based on the minimum number of object instances that need be removed from a domain in order for a concept to be expunged from the lattice. Mathematics that underpin its origin and behaviour are detailed in the thesis showing that if the relevance of a concept is defined by the Collapse Index: a concept will eventually lose relevance if one of its immediate subconcepts increasingly acquires object instance support; and a concept has its highest relevance when its immediate subconcepts have equal or near equal object instance support. In addition, experimental evaluation is provided where the Collapse Index demonstrated comparable or better performance than the current prominent alternatives in: being consistent across samples; the ability to recall concepts in noisy lattices; and efficiency of calculation. It is also demonstrated that the Collapse Index affords concepts with low object instance support the opportunity to have a higher relevance than those of high supportThe second contribution to knowledge is that of an approach to semantic extraction from a dataset where the Collapse Index is included as a method of selecting concepts for inclusion in a final concept hierarchy. The utility of the approach is demonstrated by reviewing its inclusion in the implementation of a recommender system. This recommender system serves as the final contribution featuring a unique design where lattices represent user profiles and concepts in these profiles are pruned using the Collapse Index. Results showed that pruning of profile lattices enabled by the Collapse Index improved the success levels of movie recommendations if the appropriate thresholds are set.

xii, 55 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Selective attention refers to perceptual selection and working memory refers to the active maintenance of mental representations. Selective attention and working memory are believed to be two of the most important functions in human cognition and have been intensively investigated in cognitive psychology. However, it is quite recent that the link between attention and working memory has been systematically researched. One question that remains controversial is the effect of working memory on attentional control with inconsistent results reported in the human psychophysical literature, despite clear and strong evidence from physiological studies with nonhuman primates that working memory is the main source of top-down attentional control. The main goal of the current study is to provide a plausible solution to the puzzle of attentional control by introducing the concept of goal-specificity and competition between working memory representations. I hypothesized that the strength of the biasing effect of working memory on attention depends on the specificity of representations in working memory, and developed an experimental paradigm (the goal-specificity paradigm) to test this hypothesis using psychophysical and neuroimaging methods. One of the most important manipulations in the goal-specificity paradigm is how specifically targets in different tasks are defined. The results demonstrate that there is competition between items in working memory for attentional control that is influenced by the specificity of each representation as well as task relevancy. Also, it is shown that the effect of goal-specificity is present in both spatial and temporal domains as revealed by visual search and rapid serial visual presentation tasks. The results suggest the possibility that the negligible effect of working memory in some previous studies may be due to insufficient specificity of the objects in working memory or to the presence of other specifically-defined information in working memory. Furthermore, based on the implication from the current study that goal-specificity has a significant influence on attentional control, I expect that the experimental paradigm introduced in the current study can be utilized as an objective psychophysical measure of attentional control. / Committee in charge: Margaret Sereno, Chairperson, Psychology; Scott Frey, Member, Psychology; Michael Wehr, Member, Psychology; Richard Taylor, Outside Member, Physics

Philosophiae Doctor - PhD / The study investigates how English Home language teachers use the English Home language textbook in the grade 11 classroom. It aspires to generate an understanding of the strong relationship between the teacher, textbook and the learner thereby illustrating how a recognition of the various ways the textbook can be used. By addressing methodological issues and text relevancy as a tool for learner connection, the research appraises the use of text relevancy in the grade 11 classroom. In upholding a constructivist view of teaching the research postulates that the use of textbook material that holds a connection to learners’ lives can help increase the proficiency of the learners in the classroom and bring a deeper motivation for increase learner participation. The investigation uses a qualitative methodology to study and describe the dynamics of using the textbook. The research captured teaching methods that are already known. However, the research in this thesis also added some new dimensions that many teachers might not know of or might not be using in their classroom. These teaching methods revealed the extent to which effective teachers will go to make a difference for their learners. The teaching methods harnessed the ability from the learner to recreate, imagine and empower their understanding of the world they live in with an understanding of current topics that surfaces in a teenager’s life. The findings indicate that the textbook is still relevant and can be used in some creative ways of teaching. The study also affirms that it is possible to develop your own textbook, which can add a greater connection between the teacher and the learner.

Philosophiae Doctor - PhD / The study investigates how English Home language teachers use the English Home language textbook in the grade 11 classroom. It aspires to generate an understanding of the strong relationship between the teacher, textbook and the learner thereby illustrating how a recognition of the various ways the textbook can be used. By addressing methodological issues and text relevancy as a tool for learner connection, the research appraises the use of text relevancy in the grade 11 classroom. In upholding a constructivist view of teaching the research postulates that the use of textbook material that holds a connection to learners’ lives can help increase the proficiency of the learners in the classroom and bring a deeper motivation for increase learner participation. The investigation uses a qualitative methodology to study and describe the dynamics of using the textbook. The research captured teaching methods that are already known. However, the research in this thesis also added some new dimensions that many teachers might not know of or might not be using in their classroom. These teaching methods revealed the extent to which effective teachers will go to make a difference for their learners. The teaching methods harnessed the ability from the learner to recreate, imagine and empower their understanding of the world they live in with an understanding of current topics that surfaces in a teenager’s life.

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This dissertation is intended to provide a statistical foundation for the IV models and shed lights on a number of issues related to the IV method. The first chapter shows that the theoretical Instrumental Variable model can be derived by reparameterization of a well-specified statistical model defined on the joint distribution of the involved random variables as the actual (local) data generation process. This reveals the covariance structure of the error terms of the usual theory-driven instrumental variable model. The revealed covariance structure of the IV model have important implications, particularly, for designing simulation studies. Monte Carlo simulations are used to reexamine the Nelson and Startz (1990a) findings regarding the performance of IV estimators when the instruments are weak. The results from the simulation exercises indicate that the sampling distribution of ^Î _IV is concentrated around ^Î _OLS. The second chapter considers the underlying joint distribution function of the instrumental variable (IV) model and presents an alternative definition for the exogenous and relevant instruments. The paper extracts a system of independent and orthogonal equations that covers up a non-orthogonal structural model and argues that the estimated IV regression is well-specified if the underlying system of equations is well-specified. It proposes a new instrument relevancy measure that does not suffer from the first-stage <i>R²</i> deficiencies. Third chapter argues the application of the IV method in estimation of models with omitted variable. The paper considers the implicit parametrization of statistical models and presents five conditions for an appropriate instruments. Two of them are empirically measurable and can be tested. This improves the literature by adding one more objective criterion for the selection of instruments. This chapter applies the IV method to estimate the rate of return to education in Iran. It argues that the education of two cohorts of Iranians was delayed or cut short by the Cultural Revolution. Therefore, the Cultural Revolution, as an exogenous shock to the supply of education, establishes the year of birth as the exogenous and relevant instrument for education. Using the standard Mincerian earnings function with control for experience, ethnicity, location of residence and sector of employment, the instrumental variable estimate of the return to schooling is equal to 5.6&#37;. The estimation results indicate that the Iranian labor market values degrees more than years of schooling. / Ph. D.

Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners' acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings.

Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners¿ acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings. / Applied Science University, Jordan.