Doctor of Philosophy, The Ohio State University, 2023, Industrial and Systems Engineering

As the push for increased artificial intelligence (AI) and machine learning (ML) in safety-critical domains continues to grow stronger, calls for responsible AI have emphasized the need to mitigate the potential for automation to contribute to catastrophic accidents and inequitable outcomes. These conversations have illuminated a growing consensus that explainability is not just helpful but necessary for any automation deployed in high-stakes systems. However, conversations around responsible AI and explainable AI (XAI) imply a number of assumptions about the resultant joint human-machine cognitive system: (1) improved automation will generally perform better than people and, in the rare circumstances it does not, (2) people will be able to detect and correct automation mistakes, if they are sufficiently motivated and thorough; therefore, (3) focusing on improving the performance of automation alone will improve the performance of the joint system. This research explores these assumptions about modern AI-infused automation. 267 nursing students used various configurations of a patient data display to anticipate patient decompensation events (i.e., a rapid collapse of a patient's health) five minutes into the future. Components of the display included a complex patient data visualization, a logistic regression algorithm trained to predict decompensation, and custom annotations meant to provide a visual explanation of the algorithm prediction. Nursing students interacted with four different combinations of these components which emulated four technology archetypes: (A) representation aids, (B) black-box algorithms, (C) observable algorithms, and (D) salience algorithms. Students completed a randomized sequence of 10 patient cases (5 urgent, 5 non-urgent) and on each case reported their concern for the patient (on a scale of 0-10), an explanation of their concern, and what they thought the algorithm was “concerned” about. With only the base representation aid (n (open full item for complete abstract)

Committee: Michael Rayo (Advisor); David Woods (Committee Member); Samantha Krening (Committee Member); Mark Moritz (Committee Member) Subjects: Artificial Intelligence; Cognitive Psychology; Industrial Engineering; Systems Design

Master of Science, The Ohio State University, 2023, Industrial and Systems Engineering

As automation and technology has grown, machines are no longer tools and new human-machine architectures have emerged that move beyond the definition of a traditional team and are better defined through joint activity. Current design philosophies, such as Human-Centered Design (HCD), continue to look at systems as collections of components, which is insufficient to understand the interdependencies and complexities that are necessary to support these new work architectures. Joint activity design (JAD) has been introduced to fill this gap and explicitly design for joint activity. However, adoption of JAD and similar techniques has been slow, and it is unclear what factors are contributing to this lack of proliferation. In this study, we aimed to determine the challenges for implementation of JAD artifacts and what may have facilitated their use. By analyzing 5 case studies of design projects that span multiple organizations, we found that JAD artifacts were rarely used as intended due to a high cost and a high uncertainty on how to implement them. Whereas these artifacts were often used within smaller subunits, they were rarely used across the larger design function. Across the larger design function, the support was not sufficient to maintain common ground, which exacerbated many of the challenges that resulted in sacrificing the final design's ability to support joint activity. In the future it would be valuable to get a larger selection of cases from other organizations and more evidence needs to be collected on the value of JAD methods over HCD methods.

Committee: David Woods (Committee Member); Michael Rayo (Advisor) Subjects: Design; Engineering; Industrial Engineering; Systems Design

Master of Science, The Ohio State University, 2022, Industrial and Systems Engineering

Decades of cognitive systems engineering research has revealed that implementing human-machine teams into complex environments can consequently result in challenges that negatively impact human-machine teams. Such challenges and conflicts amongst team members can readily be observed in human-machine teams where agents are assigned heterogeneous tasks because the agents' individual goals may have a tendency to conflict and compete with one another in their shared environment. This conflict may also be magnified if the agents of our heterogeneously tasked human-machine team do not share a common goal and are not equipped with the resources to manage their differences. In our study, we set out to determine how the performance of our heterogeneously tasked agents in our simulated human-machine team was impacted in our full-motion video and intelligence analysis. By using joint-performance activity graphs, various statistical analyses, constant comparative analysis, and human-machine teaming heuristic analysis, we were able to determine that the performance of our human-machine team was not significantly different from the performance of our participants who worked alone. This led us to the conclusion that the machine agent insufficiently aided their human agent's decision making during the full motion video analysis and the design of the machine failed to adhere to known Human-Machine Teaming heuristics. Lastly, this holistic analysis revealed that the machine agent acted as if it did not have any knowledge of the ultimate goal of their human agent, and due to its limited capabilities, the machine was unable to contribute information in relation to the overarching goal. Even though the architecture of the human-machine team in this study failed to adhere to various human-machine teaming heuristics, failing to adhere to and implement the team so that both the agents' individual tasks meaningfully contributed the shared goal was determined to be the most criti (open full item for complete abstract)

Committee: Michael Rayo (Advisor); Samantha Krening (Committee Member); Michael Rayo (Committee Member) Subjects: Design; Engineering; Industrial Engineering; Systems Science

Master of Science (MS), Wright State University, 2021, Human Factors and Industrial/Organizational Psychology MS

Four interfaces were developed to factorially apply two principles of ecological interface design (EID; direct perception and direct manipulation) to a flexible manufacturing system (FMS). The theoretical foundation and concepts employed during their development, with findings related to more significant issues regarding interface design for complex socio-technical systems, are discussed. Key aspects of cognitive systems engineering (CSE) and EID are also discussed. An FMS synthetic task environment was developed, and an experiment was conducted to evaluate real-time decision support during supervisory operations. Participants used all four interfaces to supervise and maintain daily part production at systematically varied levels of difficulty across sessions. Significant results provide evidence that the incorporation of direct perception and direct manipulation in interface design produced an additive effect, allowing for greater support for the supervisory agents.

Committee: Kevin B. Bennett Ph.D. (Advisor); Scott Watamaniuk Ph.D. (Committee Member); John Flach Ph.D. (Committee Member) Subjects: Experimental Psychology; Psychology; Systems Design

Doctor of Philosophy, The Ohio State University, 2020, Industrial and Systems Engineering

Responding to anomalies in the critical digital services domain involves coordination across a distributed system of automated subsystems and multiple human roles (Allspaw, 2015; Grayson, 2019). Exploring the costs of this joint activity is an underexamined area of study (Woods, 2017) but has important implications for managing complex systems across distributed teams and for tool design and use. It is understood that anomaly recognition is a shared activity between the users of the service, the automated monitoring systems, and the practitioners responsible for developing and operating the service (Allspaw, 2015). In addition, multiple, diverse perspectives are needed for their different views of the system and its behavior and their ability to recognize unexpected and abnormal conditions. While the collaborative interplay and synchronization of roles is critical in anomaly response (Patterson et al, 1999; Patterson & Woods, 2001), the cognitive costs for practitioners (Klein et al, 2005; Klinger & Klein, 1999; Klein, 2006) can be substantial. The choreography of this joint activity is shown to be a subtle and highly integrated into the technical efforts of dynamic fault management. This work uses process tracing to take a detailed look at a corpus of five cases involving software engineers coping with unexpected service outages of varying difficulty. In doing so, it is noted that the practices of incident management work very differently than domain models suggest and the tooling designed to aid coordination incurs a cognitive cost for practitioners. Adding to the literature on coordination in ambiguous, time pressured and non co-located groups, this study shows that adaptive choreography enables practitioners to cope with dynamic events – and dynamic coordination demands. These demands can also be a function of the coordination strategies of others – in particular when they shift costs of coordination across time and organizational boundaries.

Committee: David Woods (Advisor); Michael Rayo (Committee Member); Philip Smith (Committee Member) Subjects: Computer Science; Engineering

Ph.D., Antioch University, 2020, Antioch New England: Environmental Studies

In response to mounting wicked environmental problems and an outdated U.S. educational system, this dissertation investigates transformative place-based education as an adaptive boundary system that connects individual learning to positive social-ecological change. The research approaches learning through a systems lens through a novel framework of Social-ecological Systems, Place-based Education, and Transformative Learning Theories. A three-part mixed methods approach, including content analysis of the literature, phenomenological interviews with experts in place-based education, and fuzzy-logic cognitive mapping with educators at three schools practicing the principles of place-based education, is used to examine transformative place-based education from the perspectives of current U.S. place-based educators. Twenty-four components of a transformative place-based learning system are identified in the research and used in constructing fuzzy-logic cognitive maps. Four essential themes emerge from the data that are discussed as levers that link individual learning to positive social-ecological change: active engagement, context and connection to place, collaborative real-world problem solving, and courageous leadership. The research indicates that most central to transformative place-based learning experiences is real-world problem solving. The causal relationships between real-world problem solving and almost every other learning system component, including agency, change, and place, creates the strongest link in this study between individual learning and positive social-ecological change.

Committee: Jimmy Karlan Ed.D. (Committee Chair); Lisbeth Willey Ph.D. (Committee Member); Nikki Gamrath Ed.D. (Committee Member) Subjects: Education; Educational Theory; Environmental Education; Environmental Science; Environmental Studies

Doctor of Philosophy (PhD), Wright State University, 2009, Human Factors and Industrial/Organizational Psychology PhD

This research aimed at understanding bounded rationality – that is, how simple heuristics result in satisfactory outcomes – in a naturalistic setting where agents have to meet environmental demands with limited resources. To do so, two methodological approaches were taken, an observational and an experimental study of U.S. emergency physicians who have to provide a satisfactory level of care while simultaneously coping with uncertainty, time and resources constraints. There are three major findings. First, based on observations of 12 resident and 6 attending physicians at two Midwestern emergency departments (ED), ED physicians use at least two general heuristics. One heuristic exploits symptom-disease relationships with the goal to rule out ‘worst cases' that would require immediate medical attention. The other heuristic aims at identifying diseases that are commonly associated with a set of symptoms. Thus, whereas the former heuristic emphasizes medical safety by aiming at identifying even unlikely ‘worst cases,' the latter stresses efficiency by aiming at separating typical worst from common benign cases to allocate resources appropriately. Second, the selection of general heuristics is situated in an environmental context. This context is reflected in epidemiological constraints that delimit the range of patients' potential medical problems as well as sociocultural constraints that delimit the range of potential, desirable, or required care solutions. ED physicians' exploit these constraints to actively (re)formulate the problem to-be-solved and select strategies that satisfy requirements for safe and efficient care. Third, based on observations and data from 39 clinicians-in-training who participated in the experimental study, emergency care delivery is the solution of medical problems in a socially dynamic setting. ED physicians aim at understanding their patients' needs and circumstances to obtain salient information about potential (medical) problems and, ul (open full item for complete abstract)

Committee: John Flach, PhD (Advisor); Glenn Hamilton, MD, MSM (Committee Member); Valerie Shalin, PhD (Committee Member); Scott Watamaniuk, PhD (Committee Member); Tamera Schneider, PhD (Committee Member) Subjects: Psychology

Doctor of Philosophy, The Ohio State University, 2012, Industrial and Systems Engineering

Interest in the application of Unmanned Aerial Vehicles (UAV) has emerged as a result of the increased capacity to connect distant operators. No longer do pilots need to be collocated with the air vehicles they control. High-bandwidth communication channels afford the opportunity to remotely control these platforms. The anticipated advantages of this approach are obvious. First, removing the crew from the platform represents significant weight reductions that can be exploited in the form of greater range or bigger payloads. The elimination of the cockpit affords greater flexibility in design of the airframe that can enhance the low observability characteristics thereby increasing survivability. With no need to worry about the g-tolerance of a pilot, restrictions on maneuverability are relaxed. Finally and most notably is the removal of human from harm's way. UAVs can enter hostile airspace without the risk of friendly loss of human life. However, these potential benefits come with a heavy cost. Given the foreseen high potential benefits of UAVs, the challenge for developers has shifted from one of demonstrating their value on the battlefield to one of increasing the efficiency of their use. Given the increased demand for such assets, the using community must find ways to reverse the manning ratio for their use. Current concepts of operation require multiple operators for every unmanned system, a ratio will be difficult to sustain if the projected increase in use is to be believed. If this expanded use of UAVs is to be realized, a more desirable manning concept must be identified. However, coordinating control of multiple UAVs at a distance has proven difficult. Several human related issues remain. Research is ongoing addressing many of the traditional human integration problems such as workload, situation awareness, human-computer interface, human interaction with automation, etc. The current research investigates potential sources of adaptive capacity that could e (open full item for complete abstract)

Committee: David Woods PhD (Committee Chair); Philip Smith PhD (Committee Member); Emily Patterson PhD (Committee Member); Umit Catalyurek PhD (Committee Member) Subjects: Engineering

Master of Science, The Ohio State University, 2012, Industrial and Systems Engineering

This paper explores business continuity and resilience engineering by examining exercises one socio-technical organization used to assess the consequences of breakdowns in with vital digital infrastructure. The first exercise, which is implementation of disaster recovery plans, was conducted with practitioners and technologists at the sharp end of the organizational hierarchy. The second exercise, a risk-based scenario to test large-scale business continuity (aggregate execution of multiple disaster recovery plans), was conducted with administrators and business leaders at the blunt end of the organizational hierarchy. These two exercises together compose the organization's processes to build resilience. The results contrast how an organization implements disaster recovery tests at smaller, more manageable scales with the use of risk-based simulations at larger scales to prepare to respond to challenge events. The results also demonstrate how a company can build resilience by preparing to manage disruptions to minimize downtime and quickly restore normal business operations.

Committee: David Woods (Advisor); Phil Smith (Committee Member) Subjects: Engineering; Industrial Engineering; Systems Design

Doctor of Philosophy, The Ohio State University, 2025, Environmental Science

Sustainable agriculture requires a holistic understanding of soil health, farmer decision-making, and the influence of carbon farming stakeholders on land conservation efforts. This work integrates three studies that explore key components of agricultural sustainability at a local, regional, and national scale. Specifically, it assessed the impact of various management practices on soil biological health for farmers in Ohio and Michigan, investigated the cognitive processes driving farmer decision-making across the Midwest, and determined the network structures shaping carbon farming conversations and actions across the United States. Effective soil health assessments must be responsive to short-term management changes and account for soil texture variability to provide site-specific recommendations. A study examining nine soil biological health indicators found that while most indicators did not vary significantly by soil texture, soil protein, nematode abundance, and β-1,4-N-acetylglucosaminidase (NAG) were the exceptions, showing significant variation across fine, medium, and coarse textured soils. Regression analyses revealed that livestock presence, nitrogen application, and crop rotation diversity significantly influenced soil health indicators, emphasizing the role of management in shaping soil biological health. These findings underscore the deeply complex nature of agricultural soils, as multiple factors—including inherent soil properties and year-to-year management decisions—impact soil health measurements. By improving soil health assessment and interpretation on-farm, this research enhances researchers' ability to communicate soil science effectively, ultimately supporting better long-term soil resilience and conservation strategy adoption. However, to fully translate these insights into actionable change, we must also understand how farmers navigate these annual management decisions and what drives their choices. Farmer decision-making in agroecosyste (open full item for complete abstract)

Committee: Matthew Hamilton (Advisor); Nicholas Kawa (Committee Member); Robyn Wilson (Committee Member); Alia Dietsch (Committee Member); Christine Sprunger (Committee Member) Subjects: Agriculture; Behavioral Sciences; Environmental Science; Soil Sciences

Master of Science, The Ohio State University, 2023, Industrial and Systems Engineering

Deploying new automated or autonomous capabilities has become increasingly prevalent, transforming everyday practices. With the transformation comes increased complexity and the need for appropriate safety evaluations of automated systems. The introduction of novel capabilities can have significant implications for the behavior of the automated system, ultimately adding to the system's complexity and making it challenging to foresee and evaluate potential safety issues. The goal of this work is to provide processes for designers/engineers to recognize when safety cases need to be revised. To achieve this objective, the thesis develops a visual representation and framework that supports determining whether safety cases are sufficient. The visual representation reveals several risks associated with automation and design considerations that support human-automation interactions. The framework will encompass resilience engineering concepts through the evaluation of evidence regarding the system's reliability, robustness, and resilience. This will assist in providing designers/engineers with the ability to recognize potential risks within the system and whether claims are sufficiently backed by evidence. The visual representation and framework are applied to an aviation scenario that exhibits the processes designers/engineers need to consider. Findings from this process can support further improvements in the design and safety of complex automated systems.

Committee: Martijn IJtsma (Advisor); David Woods (Committee Member) Subjects: Industrial Engineering; Systems Design

Master of Science, The Ohio State University, 2022, Environment and Natural Resources

Complex interactions among biophysical and social systems present challenges for environmental problem-solving. Addressing climate change is a prime example. Climate change can be described as a systems problem characterized by a highly interconnected system structure that produces undesirable outcomes. This complexity highlights the importance of employing systems thinking. Systems thinking departs from the traditional approach of breaking down a system into its separate components, and instead accounts for the complex and dynamic interactions between them, enabling the evaluation of outcomes of decisions and interventions at multiple scales. Accordingly, higher educational institutions are increasingly incorporating systems thinking in their curriculum and pedagogy to produce a qualified cadre of systems thinkers capable of addressing the complex problems they will encounter in their careers. More specifically, systems thinking has been identified as a fundamental element of environmental education as it trains students to integrate information across multiple scales, allowing them to develop effective solutions for today's multidimensional environmental challenges. However, there are still significant knowledge gaps on how to promote and assess systems thinking in formal educational settings. This thesis contributes to the literature in several ways. In chapter two, I apply a systems approach to identify network-derived indicators of systems thinking by analyzing 35 cognitive maps of university students. The key contribution of this work is the development of a novel conceptual framework that integrates three fundamental dimensions of systems thinking – system components, system structure, and system function. Using hierarchical clustering, I identify and distinguish between simple versus complex systems thinking based on how cognitive maps with similar results cluster together. Subsequently in chapter three, I examine how different factors pertaining to a stude (open full item for complete abstract)

Committee: Matthew Hamilton (Advisor); Eric Toman (Committee Member); Robyn Wilson (Committee Member) Subjects: Climate Change; Environmental Education; Systems Science

Master of Science, The Ohio State University, 2022, Industrial and Systems Engineering

Human societies are themselves tangled layered networks of human, biological, and technological systems. In the last decade alone, these networks have experienced massive shock events in the form of natural disasters, infrastructure failures, and pandemics. These events will only continue and will likely include unexpected new forms of shocks in the future. Researchers across disparate disciplines have recently begun to investigate resilience in society, and how our built systems contribute to or degrade it, but there has not yet been an attempt to field a system informed by resilience research in a relevant real-world setting. This paper describes an effort to bolster the capabilities of public health work by using resilience engineering principles to generate a new extra-organizational capability amid the COVID-19 pandemic. This new capability attempts to engage the public as a part of a societal scale team engaged in a joint activity to identify positive COVID-19 cases. Our work reveals insights about is required to generate new non-standard capabilities for the benefit of society. These, in turn, reveal some of the dynamics of a society enduring a critical disruption, which future work into societal resilience will need to consider to be successful.

Committee: David Woods (Committee Member); Michael Rayo (Advisor) Subjects: Engineering; Experiments; Industrial Engineering; Public Health; Sociology; Systems Design; Systems Science; Technology

Master of Science, The Ohio State University, 2022, Industrial and Systems Engineering

The manual delineation of the gross tumor volume (GTV) for Head and Neck Cancer (HNC) patients is an essential step in the radiotherapy treatment process. Methods to automate this process have the potential to decrease the amount of time it takes for a clinician to complete a plan, while also decreasing the inter-observer variability between clinicians. Deep learning (DL) methods have shown great promise in auto-segmentation problems. For HNC, we show that DL methods systematically fail at the axial edges of GTV where the segmentation is dependent on both information from the center of the tumor and nearby slices. These failures may decrease trust and usage of proposed Auto-Contouring Systems if not accounted for. In this paper we propose a modified version of the U-Net, a fully convolutional network for image segmentation, which can more accurately process dependence between slices to create a more robust GTV contour. We also show that it can outperform the current proposed methods that capture slice dependencies by leveraging 3D convolutions. Our method uses Convolutional Recurrent Neural Networks throughout the decoder section of the U-Net to capture both spatial and adjacent-slice information when considering a contour. To account for shifts in anatomical structures through adjacent CT slices, we allow an affine transformation to the adjacent feature space using Spatial Transformer Networks. Our proposed model increases accuracy at the edges by 12% inferiorly and 26% superiorly over a baseline 2D U-Net, which has no inherent way to capture information between adjacent slices.

Committee: Samantha Krening (Advisor); Michael Rayo (Committee Co-Chair) Subjects: Artificial Intelligence; Oncology; Systems Design

Doctor of Philosophy (PhD), Wright State University, 2021, Computer Science and Engineering PhD

Autonomous agents in a multi-agent system coordinate to achieve their goals. However, in a partially observable world, current multi-agent systems are often less effective in achieving their goals. In much part, this limitation is due to an agent's lack of reasoning about other agents and their mental states. Another factor is the agent's inability to share required knowledge with other agents and the lack of explanations in justifying the reasons behind the goal. This research addresses these problems by presenting a general approach for agent goal management in unexpected situations. In this approach, an agent applies three main concepts: goal reasoning - to determine what goals to pursue and share; theory of mind - to select an agent(s) for goal delegation; explanation - to justify to the selected agent(s) the reasons behind the delegated goal. Our approach presents several algorithms required for goal management in multi-agent systems. We demonstrate that these algorithms will help agents in a multi-agent context better manage their goals and improve their performance. In addition, we evaluate the performance of our multi-agent system in a marine life survey domain and a rover domain. Finally, we compare our work to different multi-agent systems and present empirical results that support our claim.

Committee: Michael T. Cox Ph.D. (Committee Co-Chair); Mateen M. Rizki Ph.D. (Committee Co-Chair); Matthew Mollineaux Ph.D. (Committee Member); Michael Raymer Ph.D. (Committee Member); Tanvi Banerjee Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2021, Human Factors and Industrial/Organizational Psychology PhD

While many researchers have investigated the performance consequences of automated recommender systems, little research that has explored how these systems impact the de- cision making process. The purpose of this dissertation is to examine how people process information from an automated recommender system and raw information from the en- vironment using Systems Factorial Technology (SFT). Participants completed a speeded length judgment task with a reliable but imperfect aid. Experiment 1 focused on whether people process all the available information or are selective in their information search under certain circumstances (e.g., with performance incentives and with more experience with automation failures in training). Results indicate that participants likely use only one source of information, alternating between the automated aid and the environmental infor- mation. Additionally, performance incentives and less experience with automation failures can lead to slower but not necessarily more accurate performance with an automated aid. Experiment 2 focused on whether display design (e.g, proximity of information and density of information) can encourage serial or parallel processing of information. Unsurprisingly, the results indicate that integrating information on the display allows participants to process information more efficiently. Implications of this research not only sheds light on how peo- ple gather and process information with an automation aid but also how we might design systems to improve decision performance.

Committee: Ion Juvina Ph.D. (Advisor); Debra Steele-Johnson Ph.D. (Committee Member); Valerie L. Shalin Ph.D. (Committee Member); Joseph W. Houpt Ph.D. (Committee Member) Subjects: Cognitive Psychology; Psychology

Master of Science, The Ohio State University, 2021, Environment and Natural Resources

Growth in outdoor climbing recreation has resulted in social and ecological impacts to the complex systems in which the activity occurs, necessitating adaptive management strategies that address the impacts and respond to changing conditions. Use of the Adaptive Management Framework (AMF) can aid land managers in developing policies that reach desired social and ecological outcomes (Williams & Brown, 2014). An assessment of current policies and a representative model of the social-ecological system in which climbing occurs could help land managers conduct systems-informed policy planning as part of the AMF. This study uses text to data analysis to identify and compare how social and ecological variables are described by existing climbing management plans (CMPs) across six land manager groupings (International, National Park, Forest Service, State, Coop/coalition, and Local/private). Causal link statements derived from collected CMPs were then analyzed to construct a cognitive map of the system to test 5 different policy scenarios. Results indicated the outdoor climbing system consists of 31 variables and 99 relationships among all variables. No significant difference was found across policy scenarios tested, suggesting a need for further development of the model through the refinement of the state of variables and the degree of relationship they have over other variables in the system. By establishing a model of the outdoor climbing system for policy planning, this study lays a foundation for the incorporation of systems thinking to the adaptive management of outdoor climbing.

Committee: Alia Dietsch (Advisor); Matthew Hamilton (Committee Member); Ramiro Berardo (Committee Member) Subjects: Environmental Law; Environmental Science; Environmental Studies; Natural Resource Management; Public Policy; Recreation

Master of Science, The Ohio State University, 2021, Industrial and Systems Engineering

Capable computational technology is a necessary but not a sufficient condition for engineering a high-performing human-machine team (HMT). The interactions between human and machine agents can have substantial and surprising positive or negative effects on the overall performance of the system. This implies two requirements for robust HMT design: a set of design strategies to explicitly support joint cognitive functions and reliable techniques to evaluate how this support affects overall system performance. This study proposes simultaneous inference and data (SID) displays as a novel design technique to enable improved human-machine performance and joint activity graphs as a technique to evaluate this performance from a limited testing set. SID displays provide increased observability to computational algorithms by annotating a base data display with how the algorithm is interpreting the underlying data. Joint activity graphs compare performance of the joint HMT against the reference of the machine alone, extrapolating how each will perform outside the set of discrete testing cases. The results show SID displays enabled the HMT to significantly diverge from increasingly incorrect machine guidance, leading to substantial improvements in performance, especially when the machine guidance was wrong. Therefore, SID displays and joint activity graphs appear to be promising techniques for designing and evaluating joint cognitive systems that mitigate the negative consequences of incorrect machine guidance.

Committee: Michael Rayo (Advisor); David Woods (Committee Member) Subjects: Artificial Intelligence; Industrial Engineering; Systems Design; Systems Science

Doctor of Philosophy, The Ohio State University, 2020, Social Work

Mental health and risky health behaviors issues are a growing concern among Asian American adolescents in the United States. Prior studies have shown that Asian American adolescents are at greater risk for depressive symptoms and substance use, particularly when they face difficulties adjusting to a new or unwelcoming culture. Research has indicated that social cohesion, sense of belonging, and community safety are significant predictors for the level of immigrant adolescents' depressive symptoms. A number of studies have found that depressive symptoms have a strong influence on substance use among Asian American adolescents. Therefore, the pathway of social cohesion, a sense of belonging, community safety, depressive symptoms, and substance use needs attention based on an understanding of unique Asian culture. This study tested the influence of social cohesion, sense of belonging, and community safety on depressive symptoms and substance use. A systematic scoping review of the impact of acculturation on depressive symptoms and substance use. And a hierarchical multiple linear regression was performed to examine the regression effect in the relationship of social cohesion and community safety to depressive symptoms. Lastly, structural equation modeling (SEM) was conducted to examine in the relationship of social cohesion, sense of belonging, and community safety to substance use mediated by depressive symptoms among Asian American adolescents. A systematic scoping review results showed that Asian American adolescents reported high depressive symptoms but low substance use. Family, school, peer relationships, and neighborhood and community environments were strong acculturation related protective factors for depressive symptoms and substance use. Hierarchical multiple linear regression analysis research result showed that higher social cohesion and community safety level is statistically significantly associated with Asian American adolescents' depressive sympt (open full item for complete abstract)

Committee: Joseph Guada (Advisor); Kathryn Maguire-Jack (Committee Member); Arati Maleku (Committee Member); Susan Yoon (Committee Member) Subjects: Social Work

Master of Science (MS), Wright State University, 2018, Human Factors and Industrial/Organizational Psychology MS

This work describes an Ecological Interface Design (EID) comparison of five displays (Alphanumeric, 2D and 3D Aggregate, Radial, and Treemap) on accuracy and latency performance for simple cyber network data analysis tasks. Twenty students from the Computer Science and Engineering Department at Wright State University participated for compensation. Questions (n = 12) ranged from global to specific aspects of the data and required two types of responses: numerical estimates and binary visual judgments. EID principles of attunement and specificity (Bennett & Flach, 2011) guided the interpretation of results. Participants answered faster when the display's visual forms (vertical extent, area, or angle) aligned with Cleveland's (1985) principles of graphical perception (i.e., attunement), and when the displays reflected the task structure of the question (i.e., specificity). Performance was best using the vertical extent displays. This research emphasizes the importance of using EID to create graphical displays to support cyber network defense analysts.

Committee: Kevin Bennett Ph.D. (Advisor); John Flach Ph.D. (Committee Member); Adam Bryant Ph.D. (Committee Member) Subjects: Psychology; Systems Design