Implementing the Physical Literacy Component of the 2015 Ontario Health and Physical Education Curriculum: Is the Stage Set?

Introduction: Current physical activity levels among children and youth are alarmingly low; a mere 7% of children and youth are meeting the Canadian Physical Activity Guidelines (Colley et al., 2011), which means that the vast majority of this population is at risk of developing major health problems in adulthood (Janssen & Leblanc, 2010). These high inactivity rates may be related to suboptimal experiences in sport and physical activity stemming from a lack of competence and confidence (Lubans, Morgan, Cliff, Barnett, & Okely, 2010). Developing a foundation of physical literacy can encourage and maintain lifelong physical activity, yet this does not always occur naturally as a part of human growth (Hardman, 2011). An ideal setting to foster the growth and development of physical literacy is physical education class. Physical education class can offer all children and youth an equal opportunity to learn and practice the skills needed to be active for life (Hardman, 2011). Elementary school teachers are responsible for delivering the physical education curriculum, and it is important to understand their will and capacity as the implementing agents of physical literacy development curriculum (McLaughlin, 1987). Purpose: The purpose of this study was to explore the physical literacy component of the 2015 Ontario Health and Physical Education curriculum policy through the eyes of key informants, and to explore the resources available for the implementation of this new policy. Methods: Qualitative interviews were conducted with seven key informants of the curriculum policy development, including two teachers. In tandem with the interviews, a resource inventory and curriculum review were conducted to assess the content and availability of physical literacy resources. All data were analyzed through the lens of Hogwood and Gunn’s (1984) 10 preconditions for policy implementation. Results: Participants discussed how implementation is affected by: accountability, external capacity, internal capacity, awareness and understanding of physical literacy, implementation expertise, and policy climate. Discussion: Participants voiced similar opinions on most issues, and the overall lack of attention given to physical education programs in schools will continue to be a major dilemma when trying to combat such high physical inactivity levels.

Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.