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- All Subjects: Sustainability
- All Subjects: Mental Health
- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Resource Type: Text
Description
Social-emotional learning (SEL) methods are beginning to receive global attention in primary school education, yet the dominant emphasis on implementing these curricula is in high-income, urbanized areas. Consequently, the unique features of developing and integrating such methods in middle- or low-income rural areas are unclear. Past studies suggest that students exposed to SEL programs show an increase in academic performance, improved ability to cope with stress, and better attitudes about themselves, others, and school, but these curricula are designed with an urban focus. The purpose of this study was to conduct a needs-based analysis to investigate components specific to a SEL curriculum contextualized to rural primary schools. A promising organization committed to rural educational development is Barefoot College, located in Tilonia, Rajasthan, India. In partnership with Barefoot, we designed an ethnographic study to identify and describe what teachers and school leaders consider the highest needs related to their students' social and emotional education. To do so, we interviewed 14 teachers and school leaders individually or in a focus group to explore their present understanding of “social-emotional learning” and the perception of their students’ social and emotional intelligence. Analysis of this data uncovered common themes among classroom behaviors and prevalent opportunities to address social and emotional well-being among students. These themes translated into the three overarching topics and eight sub-topics explored throughout the curriculum, and these opportunities guided the creation of the 21 modules within it. Through a design-based research methodology, we developed a 40-hour curriculum by implementing its various modules within seven Barefoot classrooms alongside continuous reiteration based on teacher feedback and participant observation. Through this process, we found that student engagement increased during contextualized SEL lessons as opposed to traditional methods. In addition, we found that teachers and students preferred and performed better with an activities-based approach. These findings suggest that rural educators must employ particular teaching strategies when addressing SEL, including localized content and an experiential-learning approach. Teachers reported that as their approach to SEL shifted, they began to unlock the potential to build self-aware, globally-minded students. This study concludes that social and emotional education cannot be treated in a generalized manner, as curriculum development is central to the teaching-learning process.
ContributorsBucker, Delaney Sue (Author) / Carrese, Susan (Thesis director) / Barab, Sasha (Committee member) / School of Life Sciences (Contributor, Contributor) / School of Civic & Economic Thought and Leadership (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis project was conducted to create a practical tool to help micro and small local food enterprises identify potential strategies and sources of finance. Currently, many of these enterprises are unable to obtain the financial capital needed to start-up or maintain operations.
Sources and strategies of finance studied and ultimately included in the tool were Loans, Equity, Membership, Crowdfunding, and Grants. The tool designed was a matrix that takes into account various criteria of the business (e.g. business lifecycle, organizational structure, business performance) and generates a financial plan based on these criteria and how they align with the selected business strategies. After strategies are found, stakeholders can search through an institutional database created in conjunction with the matrix tool to find possible institutional providers of financing that relate to the strategy or strategies found.
The tool has shown promise in identifying sources of finance for micro and small local food enterprises in practical use with hypothetical business cases, however further practical use is necessary to provide further input and revise the tool as needed. Ultimately, the tool will likely become fully user-friendly and stakeholders will not need the assistance of another expert helping them to use it. Finally, despite the promise of the tool itself, the fundamental and underlying problem that many of these businesses face (lack of infrastructure and knowledge) still exists, and while this tool can also help capacity-building efforts towards both those seeking and those providing finance, an institutional attitude adjustment towards social and alternative enterprises is necessary in order to further simplify the process of obtaining finance.
Sources and strategies of finance studied and ultimately included in the tool were Loans, Equity, Membership, Crowdfunding, and Grants. The tool designed was a matrix that takes into account various criteria of the business (e.g. business lifecycle, organizational structure, business performance) and generates a financial plan based on these criteria and how they align with the selected business strategies. After strategies are found, stakeholders can search through an institutional database created in conjunction with the matrix tool to find possible institutional providers of financing that relate to the strategy or strategies found.
The tool has shown promise in identifying sources of finance for micro and small local food enterprises in practical use with hypothetical business cases, however further practical use is necessary to provide further input and revise the tool as needed. Ultimately, the tool will likely become fully user-friendly and stakeholders will not need the assistance of another expert helping them to use it. Finally, despite the promise of the tool itself, the fundamental and underlying problem that many of these businesses face (lack of infrastructure and knowledge) still exists, and while this tool can also help capacity-building efforts towards both those seeking and those providing finance, an institutional attitude adjustment towards social and alternative enterprises is necessary in order to further simplify the process of obtaining finance.
ContributorsDwyer, Robert Francis (Author) / Wiek, Arnim (Thesis director) / Forrest, Nigel (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
RecyclePlus is an iOS mobile application that allows users to be knowledgeable in the realms of sustainability. It gives encourages users to be environmental responsible by providing them access to recycling information. In particular, it allows users to search up certain materials and learn about its recyclability and how to properly dispose of the material. Some searches will show locations of facilities near users that collect certain materials and dispose of the materials properly. This is a full stack software project that explores open source software and APIs, UI/UX design, and iOS development.
ContributorsTran, Nikki (Author) / Ganesh, Tirupalavanam (Thesis director) / Meuth, Ryan (Committee member) / Watts College of Public Service & Community Solut (Contributor) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
As climate change and air pollution continue to plague the world today, committed citizens are doing their part to minimize their environmental impact. However, financial limitations have hindered a majority of individuals from adopting clean, renewable energy such as rooftop photovoltaic solar systems. England Sustainability Consulting plans to reverse this limitation and increase affordability for residents across Northern California to install solar panel systems for their energy needs. The purpose of this proposal is to showcase a new approach to procuring solar panel system components while offering the same products needed by each customer. We will examine market data to further prove the feasibility of this business approach while remaining profitable and spread our company's vision across all of Northern California.
ContributorsEngland, Kaysey (Author) / Dooley, Kevin (Thesis director) / Keahey, Jennifer (Committee member) / Department of Supply Chain Management (Contributor) / School of Social and Behavioral Sciences (Contributor) / W.P. Carey School of Business (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Building construction, design and maintenance is a sector of engineering where improved efficiency will have immense impacts on resource consumption and environmental health. This research closely examines the Leadership in Environment and Energy Design (LEED) rating system and the International Green Construction Code (IgCC). The IgCC is a model code, written with the same structure as many building codes. It is a standard that can be enforced if a city's government decides to adopt it. When IgCC is enforced, the buildings either meet all of the requirements set forth in the document or it fails to meet the code standards. The LEED Rating System, on the other hand, is not a building code. LEED certified buildings are built according to the standards of their local jurisdiction and in addition to that, building owners can chose to pursue a LEED certification. This is a rating system that awards points based on the sustainable measures achieved by a building. A comparison of these green building systems highlights their accomplishments in terms of reduced electricity usage, usage of low-impact materials, indoor environmental quality and other innovative features. It was determined that in general IgCC is more holistic, stringent approach to green building. At the same time the LEED rating system a wider variety of green building options. In addition, building data from LEED certified buildings was complied and analyzed to understand important trends. Both of these methods are progressing towards low-impact, efficient infrastructure and a side-by-side comparison, as done in this research, shed light on the strengths and weaknesses of each method, allowing for future improvements.
ContributorsCampbell, Kaleigh Ruth (Author) / Chong, Oswald (Thesis director) / Parrish, Kristen (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The goal of our study is to identify socio-economic risk factors for depressive disorder and poor mental health by statistically analyzing survey data from the CDC. The identification of risk groups in a particular demographic could aid in the development of targeted interventions to improve overall quality of mental health in the United States. In our analysis, we studied the influences and correlations of socioeconomic factors that regulate the risk of developing Depressive Disorders and overall poor mental health. Using the statistical software STATA, we ran a regression model of selected independent socio-economic variables with the dependent mental health variables. The independent variables of the statistical model include Income, Race, State, Age, Marital Status, Sex, Education, BMI, Smoker Status, and Alcohol Consumption. Once the regression coefficients were found, we illustrated the data in graphs and heat maps to qualitatively provide visuals of the prevalence of depression in the U.S. demography. Our study indicates that the low-income and under-educated populations who are everyday smokers, obese, and/or are in divorced or separated relationships should be of main concern. A suggestion for mental health organizations would be to support counseling and therapeutic efforts as secondary care for those in smoking cessation programs, weight management programs, marriage counseling, or divorce assistance group. General improvement in alleviating poverty and increasing education could additionally show progress in counter-acting the prevalence of depressive disorder and also improve overall mental health. The identification of these target groups and socio-economic risk factors are critical in developing future preventative measures.
ContributorsGrassel, Samuel (Co-author) / Choueiri, Alexi (Co-author) / Choueiri, Robert (Co-author) / Goegan, Brian (Thesis director) / Holter, Michael (Committee member) / Sandra Day O'Connor College of Law (Contributor) / School of Molecular Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05