Thesis (M.Sc.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 54-60. Also available online.

A dissertation submitted to the Faculty of Education in fulfilment of the requirements for the Degree of Master Of Education in the Department of Curriculum and Instructional Studies at the University of Zululand, 2018 / A secondary school has been declared dysfunctional based on the poor matriculation results they have produced over a number of years. This has resulted in teachers’ negative attitude towards the school activities, thus creating management challenges for the school management team. The aim of this study was to investigate the influence of time management behaviour among teachers and learners on learning and teaching environment. This case study intended to achieve three objectives, which are to identify time management practices among teachers and learners having impact on learning and teaching; to establish the effect of time management on learning and teaching as well as to suggest strategies that may have positive impact on time management behaviour among teachers and learners. To achieve the objectives of the study, I used qualitative research design collecting data through interviews and observation. I used purposive sampling to select participants for this study. Data analysis was done through ATLAS.ti, which coded data into themes and subthemes. Result of this study showed that learners and teachers were found to have poor time management behaviour which had a negative influence on the quality of teaching and learning. / National Research Foundation Of South Africa (Grant Number: CPT160513164973 and 105246)

The influences of school reform on the instructional practices of a seventh-grade language arts teacher was the focus of this study. This study was conducted in three phases: an ethnographic study, a pilot study, and a case study. The ethnography focused on the restructuring committee's proceedings, yielding information relating to the origin of reform efforts. The pilot study, stemming from the ethnography study, explored the instructional practices within a block scheduling format. Finally, the case study, investigated the viewpoints of the restructuring efforts as perceived by the participants. Data sources consisted of observations, formal interviews, previous and current classroom assignments, school reports, student surveys, and field notes. The data were analyzed, noting changes in curriculum intent, instructional approaches, assessment, and classroom management. The results of this study suggest that this teachers' success occurred as a results of the school division's posture toward restructuring and the teacher's level of readiness and intense personal desire for change. The implications for this study suggest that success in reform depends greatly on the approaches used in decision making, the level of teacher preparation and ownership, and the perspectives of all individuals involved. / Ph. D.

Learning in America has been restrained by time. Educators have developed a time-bound mentality and deceived themselves into believing that schools can educate all students at the same pace. Across the nation there is a growing trend toward restructuring as educators seek smaller class enrollment with more flexible use of time. Block scheduling utilizes classes organized into longer blocks of time and may be an element that meets these demands for restructuring. In Virginia, 4/4 block scheduling is the most popular (31.6%) arrangement of the school day. Advocates of 4/4 block scheduling are convinced this schedule meets students' needs. With the adoption of the new Standards of Learning Tests for Virginia Public Schools it is important for educators to determine which schedule will help students improve their test scores. There are no empirical studies on the effect of 4/4 block scheduling on these Standards of Learning Tests. This study will attempt to determine if there is a meaningful relationship between two types of schedules, the 4/4 block and 7-period traditional schedules, and student achievement on the Standards of Learning Tests for Virginia Public Schools. / Ed. D.

Central place foragers, such as territorial hummingbirds, feed from resources that tend to be constant in space and to replenish with time (e.g. nectar in flowers). The ability to remember both where and when resources are available would allow these animals to forage efficiently. Animals that feed at multiple locations would also benefit from forming routes between these multiple locations. Hummingbirds are thought to forage by repeating the order in which they visit several locations following a route called a “trapline”, although there are no quantitative data describing this behaviour. As a first step to determining how and if wild free living hummingbirds forage by traplining, I decomposed this behaviour into some of its key components. Through five field experiments, where I trained free-living hummingbirds to feed from artificial flowers, I confirmed that territorial hummingbirds will, in fact, trapline. Birds will use the shortest routes to visit several locations and will prioritize those locations that are closest to a usual feeding site. Additionally, even though hummingbirds can learn to use temporal information when visiting several patches of flowers, the spatial location of those patches has a larger influence in how these birds forage in the wild. Since male and female hummingbirds were thought to forage differently I also tested whether there were sex differences in the types of cues they use when foraging. Contrary to expectation, female hummingbirds will also use spatial cues to relocate a rewarded site. Using the foraging ecology of rufous hummingbirds to formulate predictions as to what information these birds should use has lead me to discover that these birds forage in a completely different way than previously thought.

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Reading has long been acknowledged to be a critical skill that is best acquired early in life. According to the most recent National Assessment of Educational Progress (NAEP) reports, American public school children continue to struggle to master the basics of reading. Although federal funding in real dollars has increased consistently over time, reading scores have not followed suit, suggesting that fiscal resources have not been applied successfully to the variables that are directly related to reading acquisition and achievement. The current state of affairs therefore suggests the need for identifying a fiscally-targetable, instructionally-relevant variable with a direct, causal relationship to early-reading achievement. One way to determine whether such a relationship exists between two variables is by means of dose-response methodology. Although this methodology has not been broadly implemented in educational research, it is attractive because it allows for the formal characterization and comparison of cause-effect relationships, and may also inform practice in readily implementable ways. Researchers have noted that time spent learning (TSL), and in particular academic learning time (ALT), is a promising candidate for a dose-response relationship with student achievement in early reading. Although ALT holds promise, there have traditionally been significant difficulties in operationalizing and quantifying it. The growing prevalence of academic software in the American public classroom holds promise for overcoming these challenges and provides an opportunity to test the hypothesis that there is a dose-response relationship between ALT and student achievement in early reading.

[pt] Com a crescente quantidade de dados sendo gerados e coletados, torna-se mais comum cenários em que se dispõe de dados rotulados em larga escala, mas com recursos computacionais limitados, de modo que não seja possível treinar modelos preditivos utilizando todas as amostras disponíveis. Diante dessa realidade, adotamos o paradigma de Machine Teaching como uma alternativa para obter modelos eficazes utilizando um subconjunto representativo dos dados disponíveis. Inicialmente, consideramos um problema central da área de Machine Teaching que consiste em encontrar o menor conjunto de amostras necessário para obter uma dada hipótese alvo h(asterisco). Adotamos o modelo de ensino black-box learner introduzido em (DASGUPTA et al., 2019), em que o ensino é feito interativamente sem qualquer conhecimento sobre o algoritmo do learner e sua classe de hipóteses, exceto que ela contém a hipótese alvo h(asterisco). Refinamos alguns resultados existentes para esse modelo e estudamos variantes dele. Em particular, estendemos um resultado de (DASGUPTA et al., 2019) para o cenário mais realista em que h(asterisco) pode não estar contido na classe de hipóteses do learner e, portanto, o objetivo do teacher é fazer o learner convergir para a melhor aproximação disponível de h(asterisco). Também consideramos o cenário com black-box learners não adversários e mostramos que podemos obter melhores resultados para o tipo de learner que se move para a próxima hipótese de maneira suave, preferindo hipóteses que são mais próximas da hipótese atual. Em seguida, definimos e abordamos o problema de Aprendizado com Restrição de Tempo considerando um cenário em que temos um enorme conjunto de dados e um limite de tempo para treinar um dado learner usando esse conjunto. Propomos o método TCT, um algoritmo para essa tarefa, desenvolvido com base nos princípios de Machine Teaching. Apresentamos um estudo experimental envolvendo 5 diferentes learners e 20 datasets no qual mostramos que TCT supera métodos alternativos considerados. Finalmente, provamos garantias de aproximação para uma versão simplificada do TCT. / [en] With the growing amount of data being generated and collected, it becomes increasingly common to have scenarios where there are large-scale labeled data but limited computational resources, making it impossible to train predictive models using all available samples. Faced with this reality, we adopt the Machine Teaching paradigm as an alternative to obtain effective models using a representative subset of available data. Initially, we consider a central problem of the Machine Teaching area which consists of finding the smallest set of samples necessary to obtain a given target hypothesis h(asterisk). We adopt the black-box learner teaching model introduced in (DASGUPTA et al., 2019), where teaching is done interactively without any knowledge about the learner s algorithm and its hypothesis class, except that it contains the target hypothesis h(asterisk). We refine some existing results for this model and study its variants. In particular, we extend a result from (DASGUPTA et al., 2019) to the more realistic scenario where h(asterisk) may not be contained in the learner s hypothesis class, and therefore, the teacher s objective is to make the learner converge to the best available approximation of h(asterisk). We also consider the scenario with non-adversarial black-box learners and show that we can obtain better results for the type of learner that moves to the next hypothesis smoothly, preferring hypotheses that are closer to the current hypothesis. Next, we address the Time-Constrained Learning problem, considering a scenario where we have a huge dataset and a time limit to train a given learner using this dataset. We propose the TCT method, an algorithm for this task, developed based on Machine Teaching principles. We present an experimental study involving 5 different learners and 20 datasets in which we show that TCT outperforms alternative methods considered. Finally, we prove approximation guarantees for a simplified version of TCT.

Nowadays the Learning Management System (LMS) platforms provide limited bidirectional, interactive mechanisms that they are competent to handle personal or small-scale distance learning systems. These mechanisms are designed for one to many online tutorials, and the technology utilizes single-input by single-output video stream technology, the video and audio data need to be coupled with one or many Multipoint Control Units (MCU) to mix or convert them into a single output media stream. In this platform MCU is critical to LMS, however, such system is expensive, lack of capacity and difficult to be massively deployed. Access-Grid (AG), an Open Source program, offers users capability to watch online multimedia audio-video contents from all the interconnected nodes of LMS through Multicast protocol, and supports groups-to-group high quality interactive distance learning. It requires all the networks to support the Multicast protocol. The MBONE (Multicast Backbone) can be used to connect different Multicast groups via Unicast communication. However, if the number of groups involving in the distance learning are large, the host computers or routers of the network will be heavily loaded because they need to handle the delivering of the media packets. To use a QuickBridge for aggregating and delivering packages is an alternative of LMS and requires (N-1) *N *BW bandwidth . For example, if there is a 15 nodes online conference and each node uses 800kbps data rate to transmit audio-video contents, then the demanded bandwidth of the aggregation is 168 Mbps. The way of dispersing and controlling the data flow becomes important factors and will greatly affect the quality of the AG online conference. This thesis modifies the procedure of AG and QuickBridge, and allows all the AG Clients to be able to transmit Unicast and Multicast packets in the online conference. It offers a Meeting Management Server to dynamically adjust topology and hub points, and achieves better elasticity to the system. By modifying VIC and RAT procedure, the system controls the outbound audio-video data flow from each nodes of online conference, and reduces the demand of bandwidth. The system can directly provide end-to-end conferencing, using Unicast communication to connect the nodes in different Multicast groups, or using the Multicast on the backbone and then using unicast communication to the local nodes. The functionality of the LMS can be improved and capable of supporting multi-windows to multi-user interactive online conference for the users. The results of this thesis can be applied upon real-time interactive distance learning, online video conferencing and interactive online TV. It also helps to lower the cost of the system and reduce the requirement of network bandwidth.

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator.