Theses, Dissertations, and other Required Graduate Degree Essays

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This collection contains digitized SFU theses except for those theses submitted within the last 12 months. If you cannot find the thesis you are looking for please search Recently Submitted Theses as it may be a recently submitted thesis and thus not yet available in Summit.

Relationship between mindful teaching methods and student perception of their retention of mathematical knowledge

Author: 
Date created: 
2018-12-19
Abstract: 

Although little research has been done on what students perceive increases their retention of mathematical content, studies show that how a memory is acquired has a direct impact on how strong that memory is. By increasing student engagement through the use of teaching tools found in Liljedahl’s thinking classroom, along with digital technology, an increase in student retention in a mathematics classroom could occur. This research study focuses on if there is a relationship between students’ perception of their retention of mathematical knowledge and the use of engaging teaching methods such as vertical, non-permanent surfaces, visibly random groupings, mindful notes, and digital technology. Results were gathered through student surveys and interviews, although staggered assessments were also analyzed to see if variance in results occurred with the implementation of new teaching methods. Results showed that the implementation of engaging teaching methods have a positive impact on students’ perception of their retention of mathematical content.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Peter Liljedahl
Sean Chorney
Department: 
Education: Faculty of Education
Thesis type: 
(Thesis (Education)) M.Sc.

Resources to support Indigenous reproductive health and justice in Toronto: A respondent-driven sampling study

Date created: 
2018-11-09
Abstract: 

In Canada, the reproductive health and rights of Indigenous women, two-spirit, trans, and gender diverse people are threatened by the complex nature of historic and ongoing colonialism. In the face of widespread oppression, however, Indigenous women, two-spirit, trans, and gender diverse people find ways to achieve wellness. To provide novel statistical information about Indigenous reproductive health, this Master’s thesis takes a strengths-based approach to understanding causes of wellness in a cohort of urban Indigenous women, two-spirit, trans, and gender diverse people of reproductive age (n=323). Through a community-based research partnership with the Seventh Generation Midwives of Toronto and the Well Living House, this study uses secondary data collected with respondent-driven sampling (RDS) methods for the community-driven health survey Our Health Counts Toronto. By drawing on community perspectives and Indigenous reproductive justice theories, we hypothesized that four different resources enhance wellness: (1) relationship to land; (2) traditional foods; (3) cultural connectedness; and, (4) Indigenous programs and services. Logistic regression modelling revealed that relationships to land, traditional foods, and Indigenous programs and services were statistically significant to wellness. This study may aid policy makers and service providers in promoting equitable reproductive health care for Indigenous peoples in Toronto and other Canadian cities. Furthermore, this study demonstrates the applicability of critical Indigenous theories and activism to the fields of population health and epidemiology.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Nicole Berry
Scott Venners
Department: 
Health Sciences: Faculty of Health Sciences
Thesis type: 
(Thesis) M.Sc.

Densification of Vancouver's neighbourhoods: Energy use, emissions, and affordability

Author: 
Date created: 
2018-12-03
Abstract: 

The City of Vancouver in British Columbia has committed to use 100% renewable energy and reduce emissions by 80% by 2050. Like many cities in North America, much of the Vancouver's land area currently consists of single-family detached home neighbourhoods—a type of land use that has been associated with higher than average per capita energy use and emissions. In this study, I used an energy-economy-emissions model, CIMS, to evaluate how densifying these low-density neighbourhoods with medium-density housing forms would influence energy use, emissions, and home energy and personal transportation affordability. While densification was found to have a modest influence on reducing building emissions, zero-emission building regulations were found to be much more effective, highlighting the importance of energy-switching policy for residential building decarbonization. However, an affordability co-benefit of densification was found: smaller, more energy efficient dwellings in dense building forms reduce annual energy costs relative to detached homes, especially when coordinated with policies and actions to limit vehicle ownership.

Document type: 
Graduating extended essay / Research project
File(s): 
Senior supervisor: 
Mark Jaccard
Department: 
Environment: School of Resource and Environmental Management
Thesis type: 
(Project) M.R.M. (Planning)

A model of health: Using business analytics to identify older Canadian adults with heart disease

Author: 
Date created: 
2018-12-14
Abstract: 

Nearly 90% of older Canadians have at least one chronic disease; 65% have two or more. The aims of my thesis were to apply business analytics techniques to predict the presence of an exemplar chronic disease, heart disease, among older Canadians, and to calculate the corresponding expected healthcare costs. I used neural networks to develop logistic regression models of heart disease using demographic, lifestyle, and health information for 15,599 older adults from the Canadian Longitudinal Study on Aging. The Economic Burden of Illness in Canada provided healthcare cost data. The best model identified 65.8% of heart disease cases from 40% of participants with the highest predicted probabilities of heart disease, accounting for $2.7 million more expected annual healthcare costs than a randomly sampled 40%. Among all older Canadians, this difference would be $1.1 billion. These methods could assist healthcare decision makers to optimize the delivery of chronic disease prevention interventions.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Dawn Mackey
Department: 
Science: Department of Biomedical Physiology and Kinesiology
Thesis type: 
(Thesis) M.Sc.

VRCast: Mobile streaming of live 360-degree videos

Author: 
Date created: 
2018-12-10
Abstract: 

Live streaming of immersive multimedia content, e.g., 360-degree videos, is getting popular due to the recent availability of commercial devices that support interacting with such content such as smart phones, tablets, and head-mounted displays. Unicast streaming of immersive content on cellular networks consumes substantial network resources and does not scale to large number of users. Multicast, on the other hand, offers a scalable solution but it introduces multiple challenges, which include handling user interactivity, ensuring smooth quality, supporting user mobility, conserving the energy of mobile receivers, and ensuring fairness among users. We propose a comprehensive solution for the problem of live streaming of 360-degree videos to mobile users, which we refer to as VRCast. VRCast is designed for cellular networks that support multicast, such as LTE. It divides the 360-degree video into tiles and then solves the complex live streaming problem in two steps to maximize the viewport quality of users and ensure a smooth quality within the same viewport while saving the energy of mobile devices and achieving fairness across users. Extensive trace driven simulation and real LTE testbed results show that VRCast outperforms the closest algorithms in the literature by wide margins across several performance metrics. For example, compared to the state-of-the-art, VRCast enhances the median frame quality by up to 22% and reduces the variation in the spatial quality by up to 53% and improves the energy saving for mobile devices by up to 250%.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Mohamed Hefeeda
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.

Deep video visual relation detection

Author: 
Date created: 
2018-12-19
Abstract: 

We propose a deep learning approach to the video visual relation detection problem which aims to spatiotemporally localize objects in videos and then predicts the interaction relationship between objects. A video visual relation instance is represented by a relation triplet with the trajectories of the object1 and object2. Our framework is composed of three stages. In stage one, an object tubelet detection model is employed on video RGB frames, which takes as input a sequence of frames and output object tubelets. In stage two, pairs of object tubelets are passed to a temporal relation detection model, which outputs a relation predicate between objects as relation tubelet. In stage three, detected short-term relation tubelets which have same relation triplet and efficient high volume overlap are associated into relation tube. We validate our method on VidVRD dataset and demonstrate that the performance of our method outperforms the state-of-the-art baselines.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Greg Mori
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.

Reading to learn mathematics: Textbooks, student notes and classroom communication

Author: 
Date created: 
2018-12-17
Abstract: 

‘Reading to learn mathematics’ has diverse interpretations: from reading to decoding text to reading mathematical literature. This blind study examined the impact of enhanced reading of the mathematics textbook in a Pre-Calculus 11 classroom. Students read and made personal notes on new content before there was any discussion or direct instruction. Their work was collected and examined for aspects and features of the mathematical text noted and whether work was directly copied or uniquely created. Prompts such as, ‘Create notes for a friend who missed class’ were used. The voice of their written work was compared to the voice of the textbook. Results indicated it was not the correctness of explanations or interpretations that mattered, rather the personal involvement with text that allowed for understanding. Further, students demonstrated increased ‘why’ questions, a broader use of mathematical register during class discussion, and changes to their personal connection to their learning.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Peter Liljedahl
David John Pimm
Department: 
Education: Faculty of Education
Thesis type: 
(Thesis (Education)) M.Sc.

Quantitative analysis of the coding capacity of C. elegans using RNA-Seq data

Author: 
Date created: 
2018-11-30
Abstract: 

Annotating the genome of the nematode Caenorhabditis elegans has been an ongoing challenge for the last twenty years. Studies have leveraged high-throughput RNA-sequencing (RNA-Seq) to uncover evidence for thousands of novel splicing events, indicating that the current annotations are far from complete. Yet, there is some uncertainty whether the many rare events represent functional transcripts, or simply biological noise. We developed a method that leverages the wealth of publicly available RNA-Seq data to perform a quantitative evaluation of the completeness of the current C. elegans genome annotation. We identified 134,949 and 204,812 novel high-quality introns and exons, respectively. We find that many introns and exons are rarely expressed overall, but strongly expressed at specific developmental stages suggesting a functional role. We assembled a high-quality set of 72,274 protein-coding transcripts to show that only a fraction of the coding transcriptome of C. elegans is represented in the current genome annotation.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Jack Chen
Department: 
Science: Department of Molecular Biology and Biochemistry
Thesis type: 
(Thesis) M.Sc.

An investigation into the time dependent deformation behaviour of open pit slopes at Gibraltar Mine, BC, Canada

Author: 
Date created: 
2018-12-18
Abstract: 

Open pit slope instabilities experience a sequence of decelerating deformation events following changes in stress state due to blasts or mining. These deformation events are poorly understood. This thesis uses large databases of specific energy and slope radar monitoring data to characterise five slope instabilities. Eight different rheological and empirical curve-fitting models are applied to 24 deformation events to identify which model best approximates observed deformation. The best-performing model, the Fractional Maxwell model, is then applied to nearly 200 deformation events identified from the five slope instabilities. The resulting model parameters α, fractional viscosity, and A, magnitude of the response, are tracked and compared with deformation history, instability size and geometry, and blast size and location. Slope instabilities exhibit increasingly viscous behaviour with deformation as damage accumulates within the rock mass. The magnitude and likelihood of deformation events correlate with the proximity of the stress change to critical geological structures.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Doug Stead
Department: 
Science: Department of Earth Sciences
Thesis type: 
(Thesis) M.Sc.

Optimization for mobile deep learning applications with edge computing

Author: 
Date created: 
2018-12-14
Abstract: 

The emergence of deep learning has attracted the attention from a wide range of fields and brought a large number of related applications. With the rapid growth of mobile computing techniques, numerous deep learning applications are designed for the mobile end. However, since deep learning tasks are computational-intensive, the limited computation resource on the mobile device cannot execute the application effectively. Traditional approach is to push the data and the workload to the remote cloud. Meanwhile, it introduces a high data transmission delay and possibly bottlenecks the overall performance. In this thesis, we apply a new rising concept, edge computing, for mobile deep learning applications. Comparing with cloud learning, the communication delay can be significantly reduced by pushing the workload to the near-end edge. Unlike the existing edge learning frameworks only concerning inference or training, this thesis will focus on both and put forward different optimization approaches towards them. Specifically, the thesis proposes a layer-level partitioning strategy for inference tasks and an edge compression approach with the autoencoder preprocessing for training tasks, to exploit all the available resources from the devices, the edge servers, and the cloud to collaboratively improve the performance for mobile deep learning applications. To further verify the optimization performance in practice, we formulate a scheduling problem for the multi-task execution and propose an efficient heuristic scheduling algorithm. Real-world experiments and extensive simulation tests show that our edge learning framework can achieve up to 70% delay reduction.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Jiangchuan Liu
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.