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- All Subjects: Mental Health
- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- 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
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
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
Protest has been both a practice of citizenship rights as well as a means of social pressure for change in the context of Mexico City's water system. This paper explores the role that citizen protest plays in the city's response to its water challenges. We use media reports of water protests to examine where protests happen and the causes associated with them. We analyze this information to illuminate socio-political issues associated with the city's water problems, such as political corruption, gentrification, as well as general power dynamics and lack of transparency between citizens, governments, and the private businesses which interact with them. We use text analysis of newspaper reports to analyze protest events in terms of the primary stimuli of water conflict, the areas within the city more prone to conflict, and the ways in which conflict and protest are used to initiate improved water management and to influence decision making to address water inequities. We found that water scarcity is the primary source of conflict, and that water scarcity is tied to new housing and commercial construction. These new constructions often disrupt water supplies and displace of minority or marginalized groups, which we denote as gentrification. The project demonstrates the intimate ties between inequities in housing and water in urban development. Key words: Conflict, protest, Mexico City, scarcity, new construction
ContributorsFlores, Shalae Alena (Author) / Eakin, Hallie C. (Thesis director) / Baeza-Castro, Andres (Committee member) / Lara-Valencia, Francisco (Committee member) / School of Geographical Sciences and Urban Planning (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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
Description
This study investigates how the patient-provider relationship between lesbian, gay, and bisexual women and their healthcare providers influences their access to, utilization of, and experiences within healthcare environments. Nineteen participants, ages 18 to 34, were recruited using convenience and snowball sampling. Interviews were conducted inquiring about their health history and their experiences within the healthcare system in the context of their sexual orientation. The data collected from these interviews was used to create an analysis of the healthcare experiences of those who identify as queer. Although the original intention of the project was to chronicle the experiences of LGB women specifically, there were four non-binary gender respondents who contributed interviews. In an effort to not privilege any orientation over another, the respondents were collectively referred to as queer, given the inclusive and an encompassing nature of the term. The general conclusion of this study is that respondents most often experienced heterosexism rather than outright homophobia when accessing healthcare. If heterosexism was present within the healthcare setting, it made respondents feel uncomfortable with their providers and less likely to inform them of their sexuality even if it was medically relevant to their health outcomes. Gender, race, and,socioeconomic differences also had an effect on the patient-provider relationship. Non-binary respondents acknowledged the need for inclusion of more gender options outside of male or female on the reporting forms often seen in medical offices. By doing so, medical professionals are acknowledging their awareness and knowledge of people outside of the binary gender system, thus improving the experience of these patients. While race and socioeconomic status were less relevant to the context of this study, it was found that these factors have an affect on the patient-provider relationship. There are many suggestions for providers to improve the experiences of queer patients within the healthcare setting. This includes nonverbal indications of acknowledgement and acceptance, such as signs in the office that indicate it to be a queer friendly space. This will help in eliminating the fear and miscommunication that can often happen when a queer patient sees a practitioner for the first time. In addition, better education on medically relevant topics to queer patients, is necessary in order to eliminate disparities in health outcomes. This is particularly evident in trans health, where specialized education is necessary in order to decrease poor health outcomes in trans patients. Future directions of this study necessitate a closer look on how race and socioeconomic status have an effect on a queer patient's relationship with their provider.
ContributorsRajan, Sindhu (Co-author) / Bradley, Arianna (Co-author) / Larson, Michelle (Co-author) / Alvarado, Aimee (Co-author) / Ingram-Waters, Mary (Thesis director) / Arnold, Michelle (Committee member) / School of Molecular Sciences (Contributor) / School of Social Transformation (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Previous research has found improvements in motor and cognitive measures following Assisted Cycle Therapy (AC) in adolescence with Down syndrome (DS). Our study investigated whether we would find improvements in mental health in older adults with DS as measured from the Adapted Behavior Dementia Questionnaire (ABDQ), Physical Activity Self Efficacy Scales (PACES), Children's Depressive inventory, which are early indicators of Alzheimer's disease (AD) in persons with Down syndrome. This study consisted of seven participants with Down syndrome between the ages of 31 and 54, inclusive, that cycled for 30 minutes 3 x/week for eight weeks either at their voluntary cycling rate (VC) or approximately 35% faster with the help of a mechanical motor (ACT). Our results were consistent with our prediction that self efficacy improved following ACT, but not VC. However, our results were not consistent with our prediction that dementia and depression were improved following ACT more than VC. These results were interpreted with respect to the effects of exercise in older adults with DS. Future research should focus on recruiting more participants, especially those with deficits in mental health.
ContributorsPandya, Sachin (Author) / Ringenbach, Shannon (Thesis director) / Coon, David (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05