Matching Items (588)
Filtering by
- Creators: School of Mathematical and Statistical Sciences
- Member of: Theses and Dissertations
Description
This paper recommends amendments to the Montessori teaching system, which can in turn be adapted by individual educators or administrative school boards. The proposed tools mentioned in this paper follow the tenets of Constructivist teaching, which Montessori uses as some of its core teaching values (“Who and What is Montessori?”). Constructivist teaching argues that students learn best when they are able to apply their knowledge base to new learning experiences. The word comes from the idea that students are “constructing” their knowledge base one piece at a time, a process that starts from the ground, or base layer, and builds up from that. This construction involves physical representations of concepts, or guided experiences. Contrary to traditional, “top down” teaching, students learning through constructivist teaching get to experiment with learning concepts before a teacher explains the proper theory. These teachings try to generate excitement for the subject matter as extensions of students’ prior learning. Simulation and data visualization are powerful tools that allow students to discover the patterns present in natural processes by giving them the power to affect the environment and see the results. Implementation of the learning strategies of data visualizations and simulations should improve student performance and excitement in Earth and Space Science (ESS), while also being compliant with the Montessori teaching method.
ContributorsGreig, Connor (Author) / Tran, Samantha (Thesis director) / Schneider, Laurence (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
DescriptionThis thesis explores the progress of autonomous vehicle technology, regulation, and deployment. It also studies how autonomous vehicles will affect auto insurance, in particular how liability coverage will change and how liability premiums for autonomous vehicles will be different from premiums for traditional vehicles.
ContributorsLaw, Madelyn (Author) / Zhou, Hongjuan (Thesis director) / Milovanovic, Jelena (Committee member) / Zicarelli, John (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
Description
An examination of various reserving methods and their application in commercial auto insurance. Seeks to answer two questions: Which is the best model, out of the Chain Ladder, Mack Chain Ladder, Munich Chain Ladder, Clark's LDF and Clark's Cape Cod methods? Which loss basis, paid or incurred, yields better reserves?
ContributorsLindgren, Connor (Author) / Zicarelli, John (Thesis director) / Milovanovic, Jelena (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
DescriptionAn examination of various reserving methods and their application in commercial auto insurance. Seeks to answer two questions:
Which is the best model, out of the Chain Ladder, Mack Chain Ladder, Munich Chain Ladder, Clark's LDF and Clark's Cape Cod methods?
Which loss basis, paid or incurred, yields better reserves?
ContributorsLindgren, Connor (Author) / Zicarelli, John (Thesis director) / Milovanovic, Jelena (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
DescriptionAn examination of various reserving methods and their application in commercial auto insurance. Seeks to answer two questions:
Which is the best model, out of the Chain Ladder, Mack Chain Ladder, Munich Chain Ladder, Clark's LDF and Clark's Cape Cod methods?
Which loss basis, paid or incurred, yields better reserves?
ContributorsLindgren, Connor (Author) / Zicarelli, John (Thesis director) / Milovanovic, Jelena (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
Description
Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on the maps. For the sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.
ContributorsKumar, Vishesh (Author) / Presse, Steve (Thesis director) / Bryan IV, J. Shep (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2024-05
Description
This thesis aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every 33 seconds in the United States alone. As the leading cause of death, early identification becomes critical for early intervention and prevention. The study addresses key research questions, including the role of machine learning in enhancing heart disease detection, comparative analysis of the six machine learning models, and the importance of predictive indicators. By leveraging machine learning algorithms for medical data interpretation, the thesis contributes insights into early disease detection.
ContributorsLa, Nikki (Author) / Sheehan, Connor (Thesis director) / Connor, Dylan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Quantum entanglement, a phenomenon first introduced in the realm of quantum mechanics by the famous Einstein-Podolsky-Rosen (EPR) paradox, has intrigued physicists and philosophers alike for nearly a century. Its implications for the nature of reality, particularly its apparent violation of local realism, have sparked intense debate and spurred numerous experimental investigations. This thesis presents a comprehensive examination of quantum entanglement with a focus on probing its non-local aspects.
Central to this thesis is the development of a detailed project document outlining a proposed experimental approach to investigate the non-local nature of quantum entanglement. Drawing upon recent advancements in quantum technology, including the manipulation and control of entangled particles, the proposed experiment aims to rigorously test the predictions of quantum mechanics against the framework of local realism.
The experimental setup involves the generation of entangled particle pairs, such as photons or ions, followed by the precise manipulation of their quantum states. By implementing a series of carefully designed measurements on spatially separated entangled particles, the experiment seeks to discern correlations that defy explanation within a local realistic framework.
ContributorsWasserbeck, Noah (Author) / Lukens, Joseph (Thesis director) / Arenz, Christian (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
The purpose of this research is to create predictive models for a leading sustainability certification - the B Corporation certification issued by the non-profit company B Lab based on the B Impact Assessment. This certification is one of many that are currently being used to assess sustainability in the corporate world, and this research seeks to understand the relationships between a corporation's characteristics (e.g. market, size, country) and the B Certification. The data used for the analysis comes from a B Lab upload to data.world, providing descriptive information on each company, current certification status, and B Impact Assessment scores. Further data engineering was used to include attributes on publicly traded status and years certified. Comparing Logistic Regression and Random Forest Classification machine learning methods, a predictive model was produced with 87.58% accuracy discerning between certified and de-certified B Corporations.
ContributorsBrandwick, Katelynn (Author) / Samara, Marko (Thesis director) / Tran, Samantha (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
The cerebellum is recognized for its role in motor movement, balance, and more recently, social behavior. Cerebellar injury at birth and during critical periods reduces social preference in animal models and increases the risk of autism in humans. Social behavior is commonly assessed with the three-chamber test, where a mouse travels between chambers that contain a conspecific and an object confined under a wire cup. However, this test is unable to quantify interactive behaviors between pairs of mice, which could not be tracked until the recent development of machine learning programs that track animal behavior. In this study, both the three-chamber test and a novel freely-moving social interaction test assessed social behavior in untreated male and female mice, as well as in male mice injected with hM3Dq (excitatory) DREADDs. In the three-chamber test, significant differences were found in the time spent (female: p < 0.05, male: p < 0.001) and distance traveled (female: p < 0.05, male: p < 0.001) in the chamber with the familiar conspecific, compared to the chamber with the object, for untreated male, untreated female, and mice with activated hM3Dq DREADDs. A social memory test was added, where the object was replaced with a novel mouse. Untreated male mice spent significantly more time (p < 0.05) and traveled a greater distance (p < 0.05) in the chamber with the novel mouse, while male mice with activated hM3Dq DREADDs spent more time (p<0.05) in the chamber with the familiar conspecific. Data from the freely-moving social interaction test was used to calculate freely-moving interactive behaviors between pairs of mice and interactions with an object. No sex differences were found, but mice with excited hM3Dq DREADDs engaged in significantly more anogenital sniffing (p < 0.05) and side-side contact (p < 0.05) behaviors. All these results indicate how machine learning allows for nuanced insights into how both sex and chemogenetic excitation impact social behavior in freely-moving mice.
ContributorsNelson, Megan (Author) / Verpeut, Jessica (Thesis director) / Bimonte-Nelson, Heather (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05