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Description
When earning a teaching certification, there is no curriculum when it comes to the treatment of students with a diagnosis as well as how to educate their fellow classmates. Diagnoses affect the process of child development of the diagnosed as well as the friends and family. Children of all different ages have different responses and reactions to the world of health. Looking at a developmental perspective, teachers can properly educate themselves and their students about these diagnoses. To be able to successfully inform students of diagnoses, there must be an overall understanding of how well they are able to acquire the knowledge. According to Jean Piaget, a key researcher in cognitive development, the age of the child correlates with their overall understanding and comprehension. In his theory, he explained how he believed that the environment of an organism affects how it will respond and adapt to the situations at hand. There are four stages that are connected to age, from infancy to adolescence and adulthood. Therefore, this project will focus on school-age children who are in the concrete operational stage. The concrete operational stage is made up of elementary and early adolescents and focuses on intelligence that is demonstrated through logical and precise thinking of concrete ideas (Huitt, W., & Hummel, J, 2003). This type of thinking applies to all parts of the child’s life and informs their behaviors on how to “adapt” to new information. Knowing this information, we will be able to create a curriculum of lectures, informational videos, worksheets and quizzes that can properly assess the student’s and their knowledge of the diagnoses.
ContributorsGreer, Rebecca E. (Author) / Visconti, Kari (Thesis director) / Collins, Jena (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.
ContributorsRajan, Deepta (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
There are many factors that influence the college decision process, but rural students face a unique set of challenges because of the environment in which they make the decision. This is a qualitative study that combines a review of previous literature on the subject with a survey of twelve students from the graduating class of 2011 in a rural area of Arizona. Results from the interviews found that the rural students consider the perception of importance of a college degree, parental influence, and self-discovery as important factors in the decision making process. In addition, not all non-college-going students felt that college was necessary for a better quality of living, but did express desire for more development opportunities while in high school. The findings resulted in the following recommendations for local educators to help students better navigate the college decision process: teach parents how to have more meaningful conversations, provide step-by-step assistance to students about the college application process, and provide more opportunities for self/educational/career development to students.
ContributorsCrow, Ellyse Diann (Author) / Wang, Lili (Thesis director) / Hollin, Michelle (Committee member) / Barrett, The Honors College (Contributor) / Division of Educational Leadership and Innovation (Contributor) / School of Community Resources and Development (Contributor) / W. P. Carey School of Business (Contributor) / Department of Management (Contributor)
Created2015-05
Description
This project was undertaken for the purposes of exploring the feasibility of website development for arts education information. In partnership with the Arizona Commission on the Arts, ideas for website design were collected. The original plan was to build a website that would be a "one-stop-shop" for educators to find arts education resources. Some resources deemed important to include on the website were: a search engine, calendar of events, curriculum ideas, discussion forum, feedback, ticketing, and financial support available. This website would make accessing arts education information easier, thus more appealing. It is understood that art is a fundamental part of education and it needs to be integrated into the public schools system, however, due to a lack of educational funding in Arizona it is important to bring outside organizations and resources into the education system. The following paper will examine how arts education is beneficial for children in grades K-12, what resources people want available on the website, what education administrators have to say about the website, and what aspects of the website would need to be included and addressed.
ContributorsJenner, Lindsay Ann (Author) / Phillips, Rhonda (Thesis director) / Ramella, Kelly (Committee member) / Nelson, Alex (Committee member) / Barrett, The Honors College (Contributor) / School of Dance (Contributor) / School of Community Resources and Development (Contributor) / Department of Psychology (Contributor)
Created2013-05
Description
The public education system in the United States is one of the nation's most powerful and influential institutions. Although this system was and continues to be viewed as a societal equalizer, the institution of public education was never constructed to support equity. This paper examines educational inequity by analyzing American history state standards in Arizona, California, New Mexico, Montana, and Oklahoma. American history state standards are carefully curated to construct a dominant "American story." For this project three frameworks were utilized to analyze the five state standards: Timeframe of Inclusion, Life Domains, and Population Characterization. These three frameworks helped unpack the state standards, which overall do not holistically include Latino or Native American historical elements. This paper supports the need to reconstruct the American history state standards in Arizona, California, New Mexico, Montana, and Oklahoma to more accurately represent Native American and Latino contributions and historical elements.
ContributorsBartlett, Maria Juanita (Author) / Lomawaima, K. Tsianina (Thesis director) / Nethero, Brian (Committee member) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Sense of Community is related to numerous positive outcomes for university students. The purpose of this study was to explore sense of community amongst low income students who received a last dollar scholarship. This study also sought to understand how students define community and how they interact with communities from their past (before university), present (since they started college), and how they envision their future community involvement after graduation. Through purposive sampling, six low income Arizona State University students were selected based on similar characteristics. The scholarship that they belong to selects them based on financial need, integrity, and prolonged commitment to community service. Using a qualitative narrative inquiry, I interviewed participants about their understanding and experiences with communities. Interviews were audio recorded and transcribed verbatim for analysis. Based on the analysis, I identified three major themes: community as construction, community as nonlinear, and community as intersectional. Drawing from participants' definitions and experiences of community, I argue that community is a construction. In other words, individuals create their own constructions of community, and their actions vary based on that construction. Participants also experience their communities intersectionally, that is individual's experience their communities as coexisting and through multiple community perspectives, rather than as a single stand-alone entity. Finally, community does not exist as part of a linear time paradigm. Instead community is experienced in terms of relevance to the individual in creating meaning from that community. In addition to the above themes, I also examined participant perspectives of ASU as a community. Based on this research, I recommend that a platform be provided for students to engage in a dialogue about their understanding of community and interactions with communities. Moreover, I suggest researchers utilize intersectionality, constructionism, and non-linear time to frame future research on sense of community. This research is significant because it helps us understand student engagement, and offers a framework through which universities can provide students an opportunity to better understand their own sense of community.
ContributorsWhite, Misha Alexsandra (Author) / Foroughi-Mobarakeh, Behrang (Thesis director) / Legg, Walter Eric (Committee member) / School of Community Resources and Development (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The nonprofit sector has experienced exponential growth in recent decades, thus creating a separate industry for nonprofits—an industry that requires education and training to run efficiently and successfully. As a result, Nonprofit Management Education (NME) at both graduate and undergraduate levels has steadily increased in number and demand. Recent changes in the political climate and changes in the government funding present new challenges to nonprofit professionals, thus enhancing the value of specific NME to prepare professionals for these challenges. To leverage NME and ensure that students are adequately prepared for these challenges, it is important to design curriculum that addresses the needs of the growing nonprofit industry. The Nonprofit Academic Center of Councils is the creator of the NACC Curricular Guidelines, which are currently used as a model all NME curricula should emulate. This study utilizes Arizona State University (ASU) to compare its current curriculum model to the NACC Curricular Guidelines, as well as the current challenges facing the nonprofit sector. In so doing, this study will provide an in-depth overview of NME at ASU through 1) focus groups of nonprofit leaders; 2) survey data from former students; and 3) curriculum mapping.
The comprehensive results indicated areas of opportunity for both ASU and the NACC Curricular Guidelines. According to the feedback of students, nonprofit professionals, and the current state of the ASU curriculum, ASU may wish to increase emphasis on Financial Management, Managing Staff and Volunteers, Assessment, Evaluation, and Decision Making, and Leading and Managing Nonprofit Organizations. After considering feedback from nonprofit professionals, NACC may consider amending some new competencies that reflect an emphasis on collective impact, cross sector leadership, or relationship building and the use of technology for nonprofit impact. The research team recommends accomplishing these changes through enhancing pedagogy by including case studies and an integrated curriculum into the ASU NME program. by applying the suggested changes to both the ASU curriculum and the NACC guidelines, this research prepares both ASU and NACC towards the process of accreditation and formalizing the NLM degree on a national level.
The comprehensive results indicated areas of opportunity for both ASU and the NACC Curricular Guidelines. According to the feedback of students, nonprofit professionals, and the current state of the ASU curriculum, ASU may wish to increase emphasis on Financial Management, Managing Staff and Volunteers, Assessment, Evaluation, and Decision Making, and Leading and Managing Nonprofit Organizations. After considering feedback from nonprofit professionals, NACC may consider amending some new competencies that reflect an emphasis on collective impact, cross sector leadership, or relationship building and the use of technology for nonprofit impact. The research team recommends accomplishing these changes through enhancing pedagogy by including case studies and an integrated curriculum into the ASU NME program. by applying the suggested changes to both the ASU curriculum and the NACC guidelines, this research prepares both ASU and NACC towards the process of accreditation and formalizing the NLM degree on a national level.
ContributorsFindlay, Molly Rebecca (Author) / Legg, Eric (Thesis director) / Ashcraft, Robert (Committee member) / Department of Information Systems (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.
ContributorsSantucci, Robert (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioðlu, Cihan (Committee member) / Bakkaloglu, Bertan (Committee member) / Tsakalis, Kostas (Committee member) / Arizona State University (Publisher)
Created2013
Description
Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
ContributorsLee, Jongmin (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2017
Description
A distributed wireless sensor network (WSN) is a network of a large number of lowcost,multi-functional sensors with power, bandwidth, and memory constraints, operating
in remote environments with sensing and communication capabilities. WSNs
are a source for a large amount of data and due to the inherent communication and
resource constraints, developing a distributed algorithms to perform statistical parameter
estimation and data analysis is necessary. In this work, consensus based
distributed algorithms are developed for distributed estimation and processing over
WSNs. Firstly, a distributed spectral clustering algorithm to group the sensors based
on the location attributes is developed. Next, a distributed max consensus algorithm
robust to additive noise in the network is designed. Furthermore, distributed spectral
radius estimation algorithms for analog, as well as, digital communication models
are developed. The proposed algorithms work for any connected graph topologies.
Theoretical bounds are derived and simulation results supporting the theory are also
presented.
ContributorsMuniraju, Gowtham (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Berisha, Visar (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2021