Learning to be literate : parental empowerment in early literacy interventions

Considerable research has focused on the success of early intervention programs for children. However, minimal research has focused on the effect these programs have on the parents of targeted children. Many current early intervention programs champion family-focused and inclusive programming, but few have evaluated parent participation in early interventions and fewer still have evaluated the impact of these programs on beliefs and attitudes and parenting practices. Since parents will continue to play a key role in their child's developmental course long after early intervention programs end, it is vital to examine whether these programs empower parents to take action to make changes in the lives of their children. The goal of this study was to understand parental influences on the early development of literacy, and in particular how parental attitudes, beliefs and self efficacy impact parent and child engagement in early literacy intervention activities. A mixed method procedure using quantitative and qualitative strategies was employed. A quasi-experimental research design was used. The research sample, sixty parents who were part of naturally occurring community interventions in at- risk neighbourhoods in a south-western Ontario city participated in the quantitative phase. Largely individuals whose home language was other than English, these participants were divided amongst three early literacy intervention groups, a Prescriptive Interventionist type group, a Participatory Empowering type group and a drop-in parent- child neighbourhood Control group. Measures completed pre and post a six session literacy intervention, on all three literacy and evidence of change in parental empowerment. Parents in all three groups, on average, held beliefs about early literacy that were positive and that were compatible with current approaches to language development and emergent literacy. No significant change in early literacy beliefs and attitudes for pre to post intervention was found. Similarly, there was no significant difference between groups on empowerment scores, but there was a significant change post intervention in one group's empowerment score. There was a drop in the empowerment score for the Prescriptive Interventionist type group, suggesting a drop in empowerment level. The qualitative aspect of this study involved six in-depth interviews completed with a sub-set of the sixty research participants. Four similar themes emerged across the groups: learning takes place across time and place; participation is key; success is achieved by taking small steps; and learning occurs in multiple ways. The research findings have important implications for practitioners and policy makers who target at risk populations with early intervention programming and wish to sustain parental empowerment. Study results show the value parents place on early learning and point to the importance of including parents in the development and delivery of early intervention programs. groups, were analyzed for evidence of change in parental attitudes and beliefs about early literacy and evidence of change in parental empowerment. Parents in all three groups, on average, held beliefs about early literacy that were positive and that were compatible with current approaches to language development and emergent literacy. No significant change in early literacy beliefs and attitudes for pre to post intervention was found. Similarly, there was no significant difference between groups on empowerment scores, but there was a significant change post intervention in one group's empowerment score. There was a drop in the empowerment score for the Prescriptive Interventionist type group, suggesting a drop in empowerment level. The qualitative aspect of this study involved six in-depth interviews completed with a sub-set of the sixty research participants. Four similar themes emerged across the groups: learning takes place across time and place; participation is key; success is achieved by taking small steps; and learning occurs in multiple ways. The research findings have important implications for practitioners and policy makers who target at risk populations with early intervention programming and wish to sustain parental empowerment. Study results show the value parents place on early learning and point to the importance of including parents in the development and delivery of early intervention programs.

A case study of a high functioning autistic female in a games setting

This study detennined whether or not a high functioning autistic girl can develop game structure strategies that may allow her to become an active participant in a game or sport environment. This qualitative case study involved the in-depth observation and description of one high functioning autistic student whose experience in a game setting would be studied. The type of case study carried out was a combination of descriptive and evaluative. This experience was investigated through structured, individual programming. Through on-site observation, journal entries, and hands on instruction, I was able to describe what progress the autistic student made in tenns of skill development. The results of the study demonstrated that a high-functioning autistic female has the potential to develop the necessary motor skills to participate in the chosen sport of basketball. The observation results and field notes contributed to a movement profile which described her habits of body. Teaching strategies and frameworks utilized during the study were described and listed. Insights and commentary are further provided. A thorough examination of autism and games programming is provided in the literature review.

The impact of integrated programming on student attitude and achievement in grade 9 academic mathematics and science

This research studioo the effect of integrated instruction in mathematics and~ science on student achievement in and attitude towards both mathematics and science. A group of grade 9 academic students received instruction in both science and mathematics in an integrated program specifically developed for the purposes of the research. This group was compared to a control group that had received science and mathematics instruction in a traditional, nonintegrated program. The findings showed that in all measures of attitude, there was no significant difference between the students who participated in the integrated science and mathematics program and those who participated in a traditional science and mathematics program. The findings also revealed that integration did improve achievement on some of the measures used. The performance on mathematics open-ended problem-solving tasks improved after participation in the integrated program, suggesting that the integrated students were better able to apply their understanding of mathematics in a real-life context. The performance on the final science exam was also improved for the integrated group. Improvement was not noted on the other measures, which included EQAO scores and laboratory practical tasks. These results raise the issue of the suitability of the instruments used to gauge both achievement and attitude. The accuracy and suitability of traditional measures of achievement are considered. It is argued that they should not necessarily be used as the measure of the value of integrated instruction in a science and mathematics classroom.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Perceived effectiveness of alternative programming : a case study /

The topic of this research was alternative programming in secondary public education. The purpose of this research was to explore the perceived effectiveness of two public secondary programs that are aJternative to mainstream or "regular" education. Two case study sites were used to research diverse ends of the aJtemative programming continuum. The first case study demonstrated a gifted program and the second demonstrated a behavioral program. Student needs were examined in terms of academic needs, emotional needs, career needs, and social needs. Research conducted in these sites examined how the students, teachers, onsite staff, and program administrators perceived that individual needs were met and unmet in these two programs. The study was qualitative and exploratory, using deductive and inductive research techniques. Similar themes of best practice that were identified in the case study sites aided in the development of a teaching and learning model. Four themes were identified as important within the case study sites. These themes included the commitment and motivation of teachers and the support of administration in the gifted program, and the importance of location and the flow of information and communication in the behavior program. Six themes emerged that were similar across the case study sites. These themes included the individual nature of programming, recognition of student achievement, the alternative program as a place of safety and community, importance of interpersonal capacity, priority of basic needs, and, finally, matching student capacity with program expectations. The model incorporates these themes and is designed as a resource for teachers, program administrators, parents, and policy makers of alternative educational programs.

Design, development and assessment of the Java Intelligent Tutoring System

The "Java Intelligent Tutoring System" (JITS) research project focused on designing, constructing, and determining the effectiveness of an Intelligent Tutoring System for beginner Java programming students at the postsecondary level. The participants in this research were students in the School of Applied Computing and Engineering Sciences at Sheridan College. This research involved consistently gathering input from students and instructors using JITS as it developed. The cyclic process involving designing, developing, testing, and refinement was used for the construction of JITS to ensure that it adequately meets the needs of students and instructors. The second objective in this dissertation determined the effectiveness of learning within this environment. The main findings indicate that JITS is a richly interactive ITS that engages students on Java programming problems. JITS is equipped with a sophisticated personalized feedback mechanism that models and supports each student in his/her learning style. The assessment component involved 2 main quantitative experiments to determine the effectiveness of JITS in terms of student performance. In both experiments it was determined that a statistically significant difference was achieved between the control group and the experimental group (i.e., JITS group). The main effect for Test (i.e., pre- and postiest), F( l , 35) == 119.43,p < .001, was qualified by a Test by Group interaction, F( l , 35) == 4.98,p < .05, and a Test by Time interaction, F( l , 35) == 43.82, p < .001. Similar findings were found for the second experiment; Test by Group interaction revealed F( 1 , 92) == 5.36, p < .025. In both experiments the JITS groups outperformed the corresponding control groups at posttest.

Developing and nurturing professional learning communities: principles used by elementary administrators /

This study examined the strategies used by elementary school principals to facilitate and nurture the development of professional learning communities (PLC) within their school settings. Using a reputational sample of administrators whose schools were demonstrating observable characteristics of PLCs, this study documented and described the strategies and actions taken by the principals to move their schools forward. Data collection included the use of open-ended interviews as well as observations capturing the means by which the principals addressed the areas of culture, processes, and structures within their school setting. A grounded theory approach to data analysis uncovered 4 guiding principles used by the principals to facilitate the development of the PLCs within their school: (a) protecting the purpose; (b) attending to relationships; (c) sharing the responsibility; and (d) valuing the journey. The guiding principles were used by each administrator to anchor the decisions they made and develop responsive, contextspecific strategies to support the PLC at their school. The results highlighted the complex role of the principal and the supports required to tackle the difficult work of facilitating PLCs.

Winâ Foy : functional object-oriented programming language

This thesis will introduce a new strongly typed programming language utilizing Self types, named Win--*Foy, along with a suitable user interface designed specifically to highlight language features. The need for such a programming language is based on deficiencies found in programming languages that support both Self types and subtyping. Subtyping is a concept that is taken for granted by most software engineers programming in object-oriented languages. Subtyping supports subsumption but it does not support the inheritance of binary methods. Binary methods contain an argument of type Self, the same type as the object itself, in a contravariant position, i.e. as a parameter. There are several arguments in favour of introducing Self types into a programming language (11. This rationale led to the development of a relation that has become known as matching [4, 5). The matching relation does not support subsumption, however, it does support the inheritance of binary methods. Two forms of matching have been proposed (lJ. Specifically, these relations are known as higher-order matching and I-bound matching. Previous research on these relations indicates that the higher-order matching relation is both reflexive and transitive whereas the f-bound matching is reflexive but not transitive (7]. The higher-order matching relation provides significant flexibility regarding inheritance of methods that utilize or return values of the same type. This flexibility, in certain situations, can restrict the programmer from defining specific classes and methods which are based on constant values [21J. For this reason, the type This is used as a second reference to the type of the object that cannot, contrary to Self, be specialized in subclasses. F-bound matching allows a programmer to define a function that will work for all types of A', a subtype of an upper bound function of type A, with the result type being dependent on A'. The use of parametric polymorphism in f-bound matching provides a connection to subtyping in object-oriented languages. This thesis will contain two main sections. Firstly, significant details concerning deficiencies of the subtype relation and the need to introduce higher-order and f-bound matching relations into programming languages will be explored. Secondly, a new programming language named Win--*Foy Functional Object-Oriented Programming Language has been created, along with a suitable user interface, in order to facilitate experimentation by programmers regarding the matching relation. The construction of the programming language and the user interface will be explained in detail.

Evaluation of a professional development curriculum in movement education and adapted physical activity for invisible disabilities : a critical cross-case analysis

Movement education and adapted physical activity are content areas not addressed in pre-service education or in-service training for Ontario practitioners working with individuals with disabilities in physical environments. Consequently, physical activity is often overlooked by service providers in programming and intervention for exceptional young learners. A formative evaluation, multiple-case study design was employed in this research in which a purposeful sample of expert practitioners performed a guided, descriptive evaluation of a three-day professional development workshop curriculum designed to supplement these areas lacking in professional preparation within their respective cohorts. Case-by-case and comparative analyses illustrated the inherent assumptions and societal constraints which prioritize the structure of professional development within the education system and other government organizations providing services for school-aged persons with disabilities in Ontario. Findings, discussed from a critical postmodern perspective, illustrate the paradoxical nature of Western values and prevailing mind/body dichotomy that guide professional practice in these fields.

Association between adolescent leisure, peer social capital and academic performance among Canadian youth

Previous research shows discrepant findings between youth leisure programming (before and after school programs, structured summer program, day camp, overnight camp), academic performance and other youth developmental outcomes. Studies underscores the importance of family, community and school social capital in educational success of youth, investigation of peer social capital in the leisure context and academic performance outcomes is limited. This study uses a sample of 10 and 11 year olds (N=1764) from the Canadian National Longitudinal Survey of Children and Youth (NLSCY) Cycle 6, to study the association between youth leisure programming, peer social capital and academic performance. Ordinal logistic regression models consistently showed a positive association between overnight camp and academic performance even after controlling for determinants of health, and measures of family, school and community social capital. Similarly, the measure of peer social capital was positively associated with academic performance. Most importantly, the interaction between overnight camp participation and peer social capital was significantly associated with academic performance. Study findings, highlight overnight camp opportunities and peer social

Patterns and correlates of tobacco use among young adults at college and university

This thesis describes college and university students' smoking behaviours and examines whether socioenvironmental and personal characteristics experienced during adolescence are differentially associated with their smoking participation. Results show more college students than university students currently smoke (37% and 21 % respectively) and more began smoking prior to post-secondary school (93% and 84% respectively). Early age of onset of alcohol use increased the odds of current smoking (main effect model, OR = 8.56 CI = 6.47, 11.33), especially for university students (interaction effect model, b = 2.35 CI = 7.50, 14.64). Lower levels of high school connectedness were associated with increased odds of current smoking but for university students only (interaction effect model, b = -0.15 CI = 0.84, 0.88). While limitations associated with convenience sampling and low response rate exist, this is the first Canadian study to examine college and university students separately. I t reveals that tobacco control programming needs to differ for college and university students, and early alcohol prevention and school engagement programs for adolescents may influence tobacco use. Given that both educational pathway and use of tobacco are associated with SES, future research may consider examining in more detail, SES-related socioenvironmental variables.