Master of Science (MS), Wright State University, 2019, Computer Science

Asthma, chronic pulmonary disease, is one of the major health issues in the United States. Given its chronic nature, the demand for continuous monitoring of patient's adherence to the medication care plan, assessment of their environment triggers, and management of asthma control level can be challenging in traditional clinical settings and taxing on clinical professionals. A shift from a reactive to a proactive asthma care can improve health outcomes and reduce expenses. On the technology spectrum, smart conversational systems and Internet-of-Things (IoTs) are rapidly gaining popularity in the healthcare industry. By leveraging such technological prevalence, it is feasible to design a system that is capable of monitoring asthmatic patients for a prolonged period and empowering them to manage their health better. In this thesis, we describe kBot, a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBOT takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient's history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Valerie Shalin Ph.D. (Committee Member); Maninder Kalra M.D., Ph.D. (Committee Member) Subjects: Computer Science; Health Care Management; Information Technology

Doctor of Education (Ed.D.), University of Findlay, 2024, Education

This dissertation investigates the impact of the Ohio Blended Collaborative (OBC) on fostering 21st century skills among students. Utilizing a mixed-methods research design, this study aims to understand how participation in the OBC influences perceptions and self-assessments of critical thinking, collaboration, communication, and creativity. The quantitative analysis is derived from a validated instrument consisting of 30 Likert-style questions, complemented by qualitative insights from open-ended survey questions. The demographic data of 227 students and 29 teachers provided a foundation for understanding the context of the research participants. Quantitatively, both students and teachers reported high levels of confidence in collaboration and communication skills, with notable strengths in problem-solving and teamwork. Qualitatively, themes emerged around increased engagement, confidence, and student-centered learning, underscoring the value of personalized learning environments fostered by the OBC. Notably, the study identified a discrepancy between students' self-perceptions and teachers' assessments, particularly regarding the application of knowledge to a new contextual situation. Additionally, the interrelatedness of the four skill categories was highlighted, indicating a holistic approach to 21st century skill development. This dissertation contributes to the field of education by offering insights into the benefits and challenges of implementing personalized learning with ongoing collaboration and support through a structured professional learning community. The findings suggest that while the OBC positively impacts the development of 21st century skills, ongoing support and adjustments are essential for maximizing student outcomes. Recommendations for practice, limitations of the study, and future research directions are discussed, providing a comprehensive overview of the OBC's implications for stakeholders in education.

Committee: Nicole Schilling (Committee Chair); Brian Bowser (Committee Member); Amanda Ochsner (Committee Member) Subjects: Curriculum Development; Education; Teaching

Doctor of Education (Ed.D.), University of Findlay, 2024, Education

This mixed-methods study explored the relationship between teacher academic optimism and personalized professional learning in a rural Appalachian elementary school in southeastern Ohio. Twenty K-5 educators participated, with pre/post-surveys utilizing the Teacher Academic Optimism Scale-Elementary (TAOS-E) yielding quantitative data. Six teachers engaged in personalized professional learning (treatment group), while fourteen formed the control group. One-on-one interviews with the treatment group added a qualitative dimension, enhancing overall validity and reliability through data triangulation. Results indicated positive changes in self-efficacy, trust, academic emphasis, and overall academic optimism for both groups. Unexpectedly, the control group experienced statistically significant gains in self-efficacy, trust, and overall academic optimism, prompting further investigation into external variables. As a practitioner in the elementary school, the researcher explores these influences in the discussion section. Qualitative analysis highlighted themes of personalized learning's value, appreciation for meaningful experiences, and varied learning format preferences. The study underscores the positive impact of a four-week personalized professional learning experience. Emphasis on job- embedded learning and collaboration enabled teachers to apply new skills in real-world situations. Interviews with the treatment group revealed positive changes in mindset and practices, emphasizing themes of positivity, reflection, engagement, relationship building, trust, effective communication, and a language shift. Overall, the teachers in the treatment group perceived the personalized professional learning approach as meaningful and positive even though the quantitative results were not significant and did not indicate a relationship between their overall levels of academic optimism.

Committee: Mary Heather Munger Dr. (Committee Chair) Subjects: Education; Education Policy; Educational Leadership; Elementary Education

Doctor of Education (Ed.D.), University of Findlay, 2024, Education

This study investigated how a self-regulated learning intervention, implemented within a personalized learning environment, affected students' self-regulation, self-efficacy, and academic achievement at a middle school. The personalized learning environment in this study was two self-paced units within middle school seventh grade math classrooms. The study included 12 difference classrooms across four different teachers. A quasi-experimental design was used in order to provide a self-regulated learning intervention to the participants by class. Dosage of the intervention varied as some classes received the intervention for eight weeks and some classes received the dosage for only the second four weeks. A matching design was used in order to ensure that approximately the same number of students were in each dosage group. Outcome measures for this study were effort regulation (ER), metacognitive self-regulation (MSR), self-efficacy, and IXL Real Time Diagnostic score. The ER, MSR, and self-efficacy scores were from the MSLQ subscales for the respective constructs. Outcome measures were taken at the beginning, in the middle, and at the end of the study. As this study utilized repeated measures, the nonindependence of individual responses within participants was accounted for by using mixed effects linear regression. The mixed effects models determined that the self-regulated intervention did not have an effect on self-regulation or academic outcomes, but did have a significant effect on student self-efficacy. Along with the self-regulated intervention having a significant effect on self-efficacy, there were also significant effects for time, and the interaction terms time x treatment and time x treatment x co-taught.

Committee: Nicole Schilling Ph.D. (Committee Chair); Kyle Wagner Ph.D. (Committee Member); Patrick Ward Ph.D. (Committee Member) Subjects: Education; Educational Psychology; Mathematics Education; Middle School Education

Doctor of Philosophy, The Ohio State University, 2023, Biomedical Engineering

Micro- and nanoscale technologies can be engineered to modulate the biochemical cues expressed by adipose tissue, thereby influencing their fate and reprogramming cascade. By leveraging these revolutionary platforms, regenerative therapies can be tailored to the unmet specific needs of patients, amplifying controlled tissue repair, wound healing, and immunomodulation. Adipose tissue-related abnormalities are characteristic of a disease state but engineering of in vitro or in vivo models can be an optimistic avenue to study, explore, and mitigate such irregularity. This could potentially be achieved by mimicking the surface energy and topography of the extracellular matrix (ECM). The presented research highlights promising micro/nanotechnology-based contrivances for augmenting adipose tissue-related cellular and reconstructive therapies. Micro/nanotechnology-based platforms hold immense, yet still untapped potential of adipose tissue-derived stem cells to advance personalized treatments. Thus, the technologies described here in the research and their future derivatives could usher in a new era of regenerative medicine by lending intelligent solutions for more effective patient outcomes. The first chapter therefore provides an overview of adipose tissues intricacies and how micro- and nanotechnology can empower our understanding of the same to perform in-situ cell transformation or develop biomaterials which could bolster cellular microenvironment. The second chapter introduces promising direct reprogramming of one cell to another cell type using implantable micro- and nano-channel based gene reprogramming therapy (TNT) device. Such a device offers cellular reprogramming with precision and can transform a fibroblast into brown adipose tissue. The third chapter focusses on a different route using injectable electrospun biopolymer for tissue reconstructive purposes. The last chapter brings a holistic viewpoint highlighting the potential of micro/nanotechnologies fo (open full item for complete abstract)

Committee: Daniel Gallego Perez (Advisor); Derek Hansford (Committee Member); Natalia Higuita Castro (Committee Member); Kristin Stanford (Committee Member) Subjects: Biomedical Engineering; Genetics; Nanoscience; Nanotechnology; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 0, Biomedical Engineering

Glioblastoma multiforme (GBM) is an aggressive, grade IV brain cancer. The current standard-of-care treatment for GBM patients is multimodal that includes surgical resection followed by radiotherapy and concomitant chemotherapy with temozolomide i.e. chemoradiation-therapy (CRT). However, in spite of such an aggressive treatment, GBM patients have a dismal median survival of 12-15 months and only <10% patients survive for over 5 years. Unfortunately, over 40% of GBM patients undergo disease progression within few weeks of CRT treatment. This poor prognosis can be attributed to genetic instability and intra- and inter-tumor heterogeneity of GBM that leads to treatment resistance, progression, and tumor recurrence. Although, isocitrate dehydrogenase-1 (IDH1) mutations, O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation, and extent of resection have shown promise as prognostic biomarkers, to our knowledge, currently there are no validated image-based biomarkers that could apriori determine the risk of poor survival in a GBM patient. Each patient is unique with distinct morphological as well as phenotypical profiles. There is thus an unmet need to identify non-responder patients prior to CRT treatment and to predict progression-free survival, for personalizing treatment decisions in GBM patients. Radiographic imaging such as magnetic resonance imaging (MRI) is routinely used for clinical diagnosis and response assessment of GBM by manual visual inspection. Similarly, surgically resected tissue slides contain rich phenotypic information that could reveal the inherent intra-tumoral heterogeneity and thus has prognostic implications. Recent advances in computational techniques such as radiomics and pathomics have shown improved efficacy (over manual inspection) for prognosis and response assessment of GBM tumors from MRI scans and histopathology slides, respectively. However, there still remain a few open questions that need to be addressed in order to b (open full item for complete abstract)

Committee: Pallavi Tiwari (Advisor); Anant Madabhushi (Committee Chair); Manmeet Ahluwalia (Committee Member); Efstathios Karathanasis (Committee Member); Jennifer Yu (Committee Member) Subjects: Artificial Intelligence; Biomedical Engineering; Computer Engineering; Computer Science; Health Care; Medical Imaging

Doctor of Education, Miami University, 2021, Educational Leadership

This dissertation is published in the format a white paper series which tells the story about Butler Tech's Fifth Day Experience across six issues. FDE is a student-centered initiative that was launched in the spring of 2020, and aims to operationalize deeper learning by exploring student voice, teacher agency, equity, personalized learning and innovation. FDE also aims to serve as a catalyst to redesign schools. A mixed methods approach was used to capture the action research process. Each white paper issue ranges between 12-15 pages and focuses on different themes within the research. Each issue include narratives, trends, and patterns from the data and highlights the evolution of FDE as it moves in real-time from a 1.0 model, to the 2.0, and eventual 3.0 versions. Issue one focuses on the origin of FDE. Issue two unpacks student voice in the design process. Issue three centers on the student experiences during the implementation process. Issue four focuses on an unexpected Covid-19 influenced model. Issue five captures the structural changes and highlights the rich programming. Issue six offers an executive summary, compares the three design models and provides a vision for the future of FDE. My hope is that school leaders, policymakers, and change agents use this white paper series as a vehicle to influence their own efforts to transform the educational world. Each publication is practical, applicable and offer critical insights into the FDE journey.

Committee: Thomas Poetter PhD (Advisor); Lucian Szlizewski (Committee Member); Lucian Szlizewski (Committee Member); Joel Malin (Committee Member); Joel Malin (Committee Member); Stephanie Danker (Committee Member); Stephanie Danker (Committee Member) Subjects: Curriculum Development; Education; Educational Leadership; Educational Theory; Entrepreneurship; Higher Education; Multicultural Education; Pedagogy; Personal Relationships; Teacher Education; Teaching

Master of Science in Pharmaceutical Science (MSP), University of Toledo, 2021, Pharmaceutical Sciences (Industrial Pharmacy)

Patient-specific medicine is a growing area of treatment in the healthcare sector and additive manufacturing, or 3D printing technology is a recent pharmaceutical approach to confront the challenge of this individualized drug delivery system. The focus of this study was to investigate the feasibility of formulating a 3D printed personalized dosage form using fused deposition modelling (FDM) in combination with hot-melt extrusion (HME) process. Acetaminophen was selected as a model drug and a commercial polyvinyl alcohol (PVA) filament was used to fabricate 3D printed tablets with two different drug loading percentages. After screening several polyvinyl alcohols (PVA), the commercial PVA filament was selected to enhance the extrusion process. 5% and 15% acetaminophen loaded filaments were successfully extruded through a filament extruder and tablets were printed using an FDM 3D printer. Thermal analysis using DSC and TGA confirmed the thermal stability of 3D printed tablets. No endothermic events corresponding to acetaminophen were observed in the DSC thermograms of drug-loaded filaments and tablets indicating that the drug was amorphously dispersed in PVA. With TGA, the drug-loaded filaments and tablets did not show any appreciable weight loss at the printing temperature of 240 ˚C suggesting that the polymer was stabilizing the drug. Molecular interactions of acetaminophen and PVA on drug-loaded tablets were verified through FTIR analysis. SEM micrographs of cross-sectioned drug-loaded filaments appeared to have a rough surface in compare to the commercial PVA filament due to the inclusion of acetaminophen, which was consistent with the drug-loaded tablets as well. Physical and mechanical characterization was performed according to mandated standards. The 3D printed tablets passed the weight variation, friability, thickness, dimensions, and breaking force tests with minimal outliers. Drug content loss was analyzed using a validated HPLC method. HPLC data demonstrat (open full item for complete abstract)

Committee: Jerry Nesamony (Committee Chair); Joseph Lawrence (Committee Member); Gabriella Baki (Committee Member) Subjects: Pharmaceuticals; Pharmacy Sciences

Doctor of Philosophy, The Ohio State University, 2020, Biomedical Engineering

Matrix metalloproteinases (MMPs) are the primary regulators of matrix degradation and reorganization through which they control key physiological processes such as wound healing and embryogenesis. Aberrant MMP activity contributes to the progression of several disease states, including cancer, in which it has been extensively characterized. To directly measure the activity of MMPs in cancer, researchers have developed MMP-degradable, fluorescent peptide biosensors. However, the peptide sequences used in these biosensors are often characterized in-solution using recombinant or purified enzymes, which are not representative of cell-mediated processes. To overcome this limitation, we have adapted MMP-degradable biosensors to the development of fluorescent peptide zymography. By covalently conjugating the peptide to the polyacrylamide backbone, this technique was able to measure a wider range of MMPs and displayed improved sensitivity compared to traditional zymography. Fluorescent peptide zymography was then used in combination with other MMP-sensing technologies to design a MMP-sensitive hydrogel drug delivery platform targeting liposarcoma, in vitro. Liposarcoma cell lines exhibiting elevated MMP activity stimulated drug release by selectively degrading a stably incorporated peptide-drug conjugate. The drug delivery platform can complement traditional surgical methods for the treatment of locally recurrent liposarcoma. Finally, we adapted a peptide-conjugated poly (ethylene glycol) (PEG) hydrogel to study the effects of dimensionality on drug treatment-induced MMP activity in breast cancer. Culture conditions regulated cellular MMP activity in response to drug treatment, where cells developed a chemoresistant phenotype in three-dimensional culture. This work motivated us to evaluate the feasibility of directly encapsulating tissue samples in the PEG hydrogels to predict patient-specific drug response. Ex vivo breast tissue dissections were >85% viable in PEG hydrogel (open full item for complete abstract)

Committee: Jennifer Leight Ph.D. (Advisor); Keith Gooch Ph.D. (Committee Member); Daniel Stover M.D. (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, University of Akron, 2019, Biomedical Engineering

Cancer treatments have shifted toward using therapies that target specific oncogenic molecules in cells. However, cancer cells develop adaptive responses to such targeted therapies and render them ineffective. Understanding mechanisms of resistance to targeted therapies is critical to improve the treatments outcome. We addressed this unresolved problem using three-dimensional (3D) tumor spheroid cultures under long-term cyclic treatment and recovery cycles. This regimen mimicked how cancer patients receive chemotherapy and allowed cancer cells to develop adaptive resistance to inhibitors of oncogenic protein kinases. Our phenotypic compound testing paralleled with comprehensive molecular analysis of tumor spheroids identified activation of specific signaling pathways that promoted adaptive drug resistance of colorectal cancer cells. Using a design-driven approach, we developed drug combinations that effectively suppressed both initially active and treatment-induced oncogenic signaling in cancer cells. To address potential toxicity of drug combinations to normal tissues, we identified low-dose synergistic drug pairs and demonstrated their use in a sequential combination treatment regimen to effectively inhibit growth of tumor spheroids and minimize toxicity to normal cells. Incorporating patient-derived cells in our 3D tumor model will help identify drivers of drug resistance toward realizing personalized cancer medicine with effective and safe combinations of targeted drugs.

Committee: Hossein Tavana PhD (Advisor); Marnie Saunders PhD (Committee Member); Ge Zhang PhD (Committee Member); Sailaja Paruchuri PhD (Committee Member); Nic Leipzig PhD (Committee Member) Subjects: Biology; Biomedical Engineering; Engineering

Doctor of Education, Miami University, 2019, Educational Leadership

How does an initiative on culturally responsive practices stimulate best practices in the classroom for all students? Research states that culturally responsive practices help include and engage students. In addition, students who have strong relationships with teachers may feel included in the classroom and, thus, engaged in their learning. This study supports the research in that when a teacher creates a relationship and becomes aware of culturally responsive practices through a focused professional development experience, students and teachers benefit. The benefits range from creating an inclusive environment to personalized learning for the student, which address their needs and takes into account their interests and backgrounds.

Committee: Lisa Weems (Committee Chair); Lucian Szlizewski (Committee Co-Chair); Amity Noltemeyer (Committee Member) Subjects: Educational Leadership

Doctor of Philosophy, Case Western Reserve University, 2018, EECS - Computer and Information Sciences

Precision medicine is a promising new approach to medicine that takes into account the individual differences in people's genetic makeup and lifestyle to identify specific treatment and prevention strategies for diseases. However, many human diseases are complex, and are driven by multiple layers of dysregulation at the cellular level, in addition to environmental factors. In recent years, the advances in high throughput technologies enable interrogation of biological systems at multiple levels, offering valuable types of data representing various aspects of cellular systems. These data types include sequences and structures of genes, RNAs, proteins, quantitative measurements on the abundance of these molecules under different conditions, and the interactions among these molecules. However, these data are noisy, incomplete, high-dimensional, highly heterogeneous, and often provide static representations of a complex and dynamic system. In this thesis, we develop computational methods to make use of these useful, yet limited sources of biological data, with a view to gaining insights on the molecular mechanisms of complex diseases. In particular, we develop novel algorithms to integrate genomic (genome-wide association studies), transcriptomic (expression-quantitative trait locus interactions), proteomic (protein expression screened via mass spectrometry), phospho-proteomic (large scale data on the phosphorylation of signaling proteins screened via mass spectrometry), and interactomic (protein interaction networks, pathway databases) datasets. Using these integrative algorithms, we develop computational tools for the identification of disease-associated protein subnetworks, risk assessment for complex diseases, and prediction of kinase-substrate associations in specific biological contexts.

Committee: Mehmet Koyuturk (Advisor); Mark Chance (Committee Member); Soumya Ray (Committee Member); Liberatore Vincenzo (Committee Member) Subjects: Bioinformatics; Computer Science

Doctor of Psychology (Psy.D.), Xavier University, 2017, Psychology

Alcohol misuse by United States college students is widespread and associated with numerous negative outcomes. Personalized feedback interventions (PFIs) are a commonly used intervention for college alcohol misuse and involve providing detailed feedback about an individual's alcohol use and the associated risks and consequences. Components included in PFIs continue to evolve but little is known about the relative perceived value of the various elements. The present study used program evaluation data from 96 students (62.9% male; average age = 19.34) mandated to complete the BASICS (Dimeff, Baer, Kivlahan, & Marlatt, 1999) program as a university sanction. At completion of the standard BASICS program, students were asked to indicate which PFI component they found most useful, least useful and why. Results indicated that the components identified as most useful were typical/peak BAC (31.3%) and peer percentile ranking (28.1 %). When asked about which component they found least useful, most participants (21.9%) noted nothing was least useful– it was all useful. The second most endorsed least useful component was family history of problematic drinking (16.7%). Higher alcohol consumption was associated with rating percentile ranking as most helpful (p= .007). Most participants (80.2%) reported interest in future PFIs including biological feedback. The present findings suggest that PFI information is accepted and valued by mandated students and including biological feedback could further strengthen the intervention's impact and influence.

Committee: Susan Kenford Ph.D. (Committee Chair); Christine Dacey Ph.D. (Committee Member); Nicholas Salsman Ph.D. (Committee Member) Subjects: Psychology

Master of Science, The Ohio State University, 2017, Genetic Counseling

Health behavior change is a complex and dynamic process, and remains difficult to predict. Many studies examine the theoretical framework that influences health behavior change; however, few exist to explain the effect of genomic counseling (GC) on health behavior change. The purpose of this study was to assess the impact of genomic testing and GC on health behavioral attitudes, intentions and outcomes for participants following receipt of multiple actionable complex disease reports. This study focuses on health behavior change related to health literacy, the transtheoretical model, social support theory and receipt of GC. Sixty-four participants completed surveys to assess health literacy, desires to change behaviors, confidence to change behaviors, stages of change and perceived utility of GC and genomic risk. No discrepancies in health literacy were found. Assessment of desire to change found that those who perceived the utility of genomic risk as most useful identified most with the maintenance stage of change. Additionally, participants who most identified with the contemplation and preparation stages of change had the most intended behavioral changes. Lastly, participants who received informational and instrumental support from GC least identified with being in the action stage of the transtheoretical model. These results provide evidence to support the theory that the stage(s) of behavior change in which a participant exists could influence their behavioral attitudes and modifications, and may be influenced by the types of social support provided by healthcare professionals.

Committee: Kevin Sweet MS, LGC (Advisor); Shelly Hovick PhD (Committee Member); Amanda Toland PhD (Committee Member) Subjects: Genetics; Public Health

MS, Kent State University, 2017, College of Education, Health and Human Services / School of Health Sciences

Nutrigenomics is a rapidly developing field of study involving the relationship between genetics and nutrition. Multiple companies are now offering personalized dietary advice based on the results of genetic testing. College students, who are educated and more familiar with new technology may provide valuable information about perceptions toward nutrigenomic technology while it is still in its early stages of development. The purpose of this study was to examine the knowledge and perception of college students toward genetic testing for personalized nutrition. Participants in this study were college students from Kent State University who completed an online survey administered through Qualtrics. The survey assessed perception toward nutrigenomics along with basic genetics knowledge. Analysis of the data revealed a general lack of genetics knowledge among college students. In addition, only 25% of participants had ever heard or read about nutrigenomic testing. The overall perception toward these developments was more positive than negative. There were significant differences in genetics knowledge and perception of nutrigenomics among various class ranks and majors. In addition, findings indicate a significant relationship between participation in college level nutrition and/or genetics courses, higher genetics knowledge and more positive perceptions toward nutrigenomics. Individuals who scored higher on the genetics knowledge assessment also displayed a more positive perception toward nutrigenomics. More research is needed to understand how college students perceive nutrigenomics and what factors affect their attitude toward these scientific developments. Future studies with a valid and reliable questionnaire are needed to confirm the findings of this study.

Committee: Eun-Jeong (Angie) Ha PhD (Advisor); Natalie Caine-Bish PhD, RD, LD (Committee Member); Nancy Burzminksi PhD, RD, LD (Committee Member) Subjects: Behavioral Sciences; Biochemistry; Biology; Education; Ethics; Food Science; Genetics; Health; Health Care; Health Education; Health Sciences; Medicine; Nutrition; Public Health Education; Science Education

Doctor of Philosophy, University of Akron, 2016, Computer Engineering

Obesity has reached epidemic proportions globally, with more than one billion adults overweight with at least three hundred million of them clinically obese; this is a major contributor to the global burden of chronic disease and disability. This can also be associated with the rising health care costs; in the USA more than 75\% of health care costs relate to chronic conditions such as Diabetes and Hypertension. While there are various technological advancements in fitness tracking devices such as Fitbit, and many employers offer wellness programs, such programs and devices have not been able to create societal scale transformations in the life style of the users. The challenge in keeping healthy people healthy and helping them to be intrinsically motivated to manage their own health is at the focus for this investigation on Personal Wellness Management. In this dissertation, this problem is presented as a decision making under uncertainty where the participant takes an action at discrete time steps and the outcome of the action is uncertain. The main focus is to formulate the decision making problem in the Goal-seeking framework. To evaluate this formulation, the problem was also formulated in two classical sequential decision-making frameworks --- Markov Decision Process and Partially Observable Markov Decision Process. The sequential decision-making frameworks allow us to compute optimal policies to guide the participants' choice of actions. One of the major challenges in formulating the wellness management problem in these frameworks is the need for clinically validated data. While it is unrealistic to find such experimentally validated data, it is also not clear that the models in fact capture all the inconstraints that are necessary to make the optimal solutions effective for the participant. The Goal-seeking framework offers an alternative approach that does not require explicit modeling of the partic (open full item for complete abstract)

Committee: Shivakumar Sastry Dr (Advisor); Nghi Tran Dr (Committee Member); Igor Tsukerman Dr (Committee Member); William Schneider IV Dr (Committee Member); Victor Pinheiro Dr (Committee Member) Subjects: Computer Engineering

Doctor of Philosophy (PhD), Ohio University, 2016, Instructional Technology (Education)

Learner autonomy is a fundamental phenomenon in the teaching and learning of languages. The growth of digital technology and the Internet appears to have changed the manifestation of learner autonomy, particularly in Less Commonly Taught Languages (LCTLs). The purpose of this phenomenological research study was to examine the experiences of LCTL instructors and students by discussing how LCTL instructors and students describe the phenomenon of learner autonomy, investigating what strategies are perceived to promote learner autonomy in LCTLs and report on how to create more opportunities for promoting learner autonomy. The researcher applied a qualitative phenomenological approach to gather and analyze data through memoing and interviewing nine participants. Three themes emerged from the data: description of learner autonomy; authentic language-learning experiences; and strategies for promoting learner autonomy. Overall, motivation, authentic experiences, and use of technology were identified to play a vital role in promoting learner autonomy. Data generated from this study lead to recommendations for utilizing personalized instructional design principles for learning that allows language learners to collaborate using technology tools that promote engagement, create an authentic language-learning environment for language learners, and exploiting iPedagogy opportunities presented by the 21st century technological tools that foster autonomy and encourage learner control.

Committee: David Moore Dr. (Advisor) Subjects: Bilingual Education; Educational Technology; Instructional Design; Language; Linguistics; Teacher Education

Doctor of Philosophy, Case Western Reserve University, 2016, Management

In an effort to deepen customer relationships (Relationship Marketing), marketers and online firms deliver personalized communications based on a consumers' digital footprint and other Big Data that they think will improve its effect; but the personalized messages are sometimes perceived to be “creepy” by the recipient. Marketers are admonished to not be creepy, but, there is not a unified definition of what creepy is or isn't, nor have the factors leading to perceived creepiness been clearly identified—there is a common feeling of discomfort, but no unified definition. The goal of this study is to address three research questions. First, what is creepy? Second, what factors lead to perceived creepiness? And third, can a scale to measure perceived creepiness be operationalized and used to validate those factors? I conducted a three-part; sequential, mixed methods study to define perceived creepiness and to identify the antecedents and consequences of perceived creepiness in personalized online messages. The study confirmed that transparency by the firm about their data collection, use and sharing practices and that enabling the consumer to exercise control over the collection, use and sharing of their personal information (including the ability to opt–out of personalized messages) are antecedents of perceived creepiness. Also, whether the message was “in context” or “out of context” had an effect on if the message was perceived to be creepy. It also suggests that trust in the sender has a direct effect on perceived creepiness; and perceived creepiness has a negative effect on customer satisfaction, which can harm brand reputation, sales, and revenue. This research makes a scholarly contribution by providing a theoretical framework for a Theory of Perceived Creepiness. It also makes a contribution to practice by providing marketers with an understanding of what leads to perceived creepiness, so that they can take action to avoid negative effects of personalized com (open full item for complete abstract)

Committee: Richard Boland Jr. (Committee Chair); Mary Culnan (Committee Member); Kalle Lyytinen (Committee Member); Casey Newmeyer (Committee Member) Subjects: Information Science; Management; Marketing; Mass Media

MDES, University of Cincinnati, 2015, Design, Architecture, Art and Planning: Design

Most products aimed at solving social problems were successful in terms of providing a solution to a social issue. However raising awareness of social problems, and involving people in continued support to help solve social problems is another challenge for designers. In this study, the principles of design for social impact will be suggested with specific examples of social charities that were successful in garnering support from the public. Current programs related to hunger issues in the US were analyzed to figure out new design opportunity areas. The principles of design for social impact applied to an object and its service to encourage continued support as part of everyone's daily life by reminding them of hunger problems.

Committee: Gerald Michaud M.A. (Committee Chair); Ramsey Ford M.Des. (Committee Member); Heekyoung Jung Ph.D. (Committee Member) Subjects: Design

Doctor of Philosophy, University of Toledo, 2015, Manufacturing and Technology Management

An emerging trend in the pharmaceutical industry is the high level of personalization of medicines that firms offer today. Such medications are expected to account for 50% of the amount spent on drugs by 2018. In conjunction with the growth of this new class of medications, firms are also continuing to serve markets for traditional (or small molecule) medications, which are often standardized or mass customized for consumer markets. Managing the diverse portfolio of medications can require different supply chain structures, specifically with respect to distribution channels. For example, the prostate cancer vaccine involves a reverse flow of raw material in the form of patient blood cells from the hospital/physician clinic to the pharmaceutical firm processing centers – a characteristic that is often not seen with traditional medications that are dispensed at the pharmacy or hospital. This has led to a new trend in the distribution channel practices for such medication, i.e. supply chain disintermediation, where the firm engages in a direct sales model, which means that the medication is shipped directly to the patient or the administrating facility (e.g. the physician's clinic/hospital) instead of being distributed through the traditional channel of wholesalers. In summary, firms today have a choice of structuring their supply chains to have a traditional intermediated distribution channel, a direct disintermediated distribution channel, or combination thereof. However, little research exists that can guide managerial decisions with respect to the appropriate supply chain structure given the portfolio of the firm's medication offerings. The firm's choices for product portfolio and supply chain structure for distribution channels raise a critical question of `what is the most appropriate supply chain disintermediation strategy given the firm's product portfolio?' Therefore, in this dissertation, the research objective is to address this central question. In address (open full item for complete abstract)

Committee: Sachin Modi (Committee Co-Chair); Ram Rachamadugu (Committee Co-Chair); Jenell Wittmer (Committee Member); Dong-Shik Kim (Committee Member) Subjects: Business Administration; Operations Research; Pharmaceuticals; Systems Design; Systems Science