Master of Arts (M.A.), University of Dayton, 2014, Psychology, General

Vigilance research often includes measuring the observer's subjective workload. The most commonly used NASA Task Load Index (NASA-TLX), requires several minutes to administer; typically at the end of an experiment. A more recently developed workload measure, the Simplified Subjective Workload Assessment Technique (S-SWAT), may provide researchers with further insights into perceived workload throughout a vigilance task. To date, no studies have measured workload using the S-SWAT in a vigilance experiment, specifically in the area of workload transitions. To date, only one study has examined perceived workload during a vigilance transition task; this study used the NASA-TLX. The goal of the present research was to explore the usefulness of the S-SWAT, determine how S-SWAT ratings compare to NASA-TLX ratings, and identify any effects on performance that the S-SWAT might create. Results showed that the S-SWAT had no impact on performance; this supports the potential of the S-SWAT as an instrument for better understanding the impact of task changes on perceived workload during a vigil. The S-SWAT, which is administered multiple times over the course of a vigil, provides more detail and helps identify trends of perceived workload, over time, compared to a single collection with the NASA-TLX. However, it is important to note that workload ratings on the NASA-TLX were higher when the S-SWAT was used in higher workload condition as compared to the ratings from when it was used in a low workload condition or when a control group reported workload only at the end of the vigil. While further research is needed to better understand the impact that the S-SWAT has on perceived workload, this study provides some evidence that the S-SWAT may be a useful measure throughout a vigilance task for gaining more insight into the workload experienced by observers.

Committee: Susan Davis (Advisor); F. Thomas Eggemeier (Committee Member); William Moroney (Committee Member) Subjects: Experimental Psychology

MA, University of Cincinnati, 2005, Arts and Sciences : Psychology

This study tested predictions derived from resource-depletion and effort-regulation models of performance effects associated with transition from a high to a low level of task demand. Demand transition was accomplished by shifting participants from a dual-task condition in which they performed concurrent tracking and vigilance tasks (induction phase) to a single task condition in which only tracking was required (transition phase). When the tracking task was difficult, dual-task participants performed more poorly than single-task controls during both phases of the study, a result consistent with expectations derived from the resource-depletion model. Just the opposite effects occurred when the tracking task was easy, a result consistent with expectations derived from the effort-regulation model. Clearly, task difficulty is a critical factor to consider in testing models of the performance effects associated with workload transition. Participants in all experimental conditions reported the experiment to be stressful.

Committee: Dr. Joel Warm (Advisor) Subjects:

Master of Science in Engineering, University of Akron, 2018, Mechanical Engineering

During spring 2016, a Hazardous Weather Testbed (HWT) was run to improve the Probabilistic Hazard Information (PHI) prototype, as part of the FACETS (Forecasting a Continuum of Environmental ThreatS) program. Each week of the 3-week testbed, three National Weather Service forecasters were trained on the PHI prototype tool to produce dynamic, probabilistic hazard information for severe weather threats. Archived and real-time weather scenarios were used to test this new paradigm. The forecasters' mental workload was evaluated after each scenario using the NASA-Task Load Index (NASA-TLX) questionnaire. Forecaster screen recordings were also compiled to complete task analysis on forecaster interaction with the tool. This study summarizes the trends in mental workload experienced by forecasters while using the PHI prototype system. Six sub dimensions of mental workload, mental demand, physical demand, temporal demand, performance, effort and frustration were analyzed separately to derive top contributing factors to workload. The average mental workload was 46.6 (out of 100, std: 19, range 70.8). Top contributing workload factors include using automated guidance, PHI object quantity, multiple displays and formulating probabilities in the new paradigm. Task analysis found a significant increase in time to produce PHI objects from previous years. Tornado objects took specifically more time to produce. The paradigm change from deterministic to probabilistic forecast created opportunities for forecasters to communicate continuous, dynamic information. This challenged forecasters to constantly interrogate storm development and predict threat development over time.

Committee: Chen Ling PhD (Advisor); Shenyong Wang PhD (Committee Member); Sergio Felicelli PhD (Committee Chair) Subjects: Industrial Engineering; Mechanical Engineering; Systems Design

PhD, University of Cincinnati, 2008, Arts and Sciences : Psychology

The present study tested predictions from resource-depletion (Gluckman, Warm, Dember, & Rosa, 1993) and effort-regulation (G. Matthews & Desmond, 2002) models of performance effects associated with transitions from a high to a low level of task demand. The former is supported when transitioned participants do more poorly on the post-shift task than non-shifted controls; the latter is supported in the opposite case. A compensatory tracking study by Ungar (2005) suggested that the absolute level of task difficulty is a critical factor to consider when testing these models. His data supported the resource-depletion model when the task was difficult and the effort-regulation model when the task was easy. The present study was carried out to determine if these effects also extend to the vigilance or sustained attention domain. Demand transition was accomplished by shifting participants from a dual-task condition in which they shared a vigilance task with a concurrent tracking task to a single-task condition in which they performed the vigilance task alone. The absolute level of task difficulty was manipulated by varying signal salience, high (easy), low (hard). Task type, simultaneous (SIM: comparative judgment) or successive (SUC: absolute judgment) was employed as a potential moderator variable. The results with the SUC task duplicated the earlier findings of Ungar (2005)- the resource exhaustion model was supported with low salience signals and the effort-regulation model was confirmed with high salience signals. In the context of the SIM task, no evidence was found to support the resource-exhaustion model; the effort regulation model was supported regardless of signal salience. Thus, the present study extends Ungar's (2005) findings to the vigilance domain and also shows that task type is a key factor to consider when studying demand transitions in this area. In addition to these findings, measurements made with the Dundee Stress State Questionnaire (DSSQ; G. Matthew (open full item for complete abstract)

Committee: Joel Warm PhD (Committee Chair); Gerald Matthews PhD (Committee Member); Michael Riley PhD (Committee Member) Subjects: Occupational Safety; Psychology

PhD, University of Cincinnati, 2007, Arts and Sciences : Psychology

The effects of vehicle automation, drive difficulty, workload transitions, and subjective state on drivers' performance efficiency and mood were assessed. This combination of driving variables is likely to play a key role in automotive safety in the future as in-vehicle technologies increase. Two levels of drive difficulty (straight, curved) were combined factorially with three levels of vehicle-automation (manual, continuous-automation, intermittent-automation) to produce six experimental conditions (N = 20 per condition). In the straight roadway condition, road curvature was absent from the drive. The road in the curved condition was a continuous set of ‘s-curves,' which required participants to make constant steering inputs. Participants in the continuous-automation condition drove in a simulated automated vehicle, which controlled drivers' lateral position and longitudinal speed. Participants in the intermittent-automation condition drove in a mix of manual and automated vehicle control, which required frequent control transitions. Participants in the manual condition completed the drive without automated vehicle control. Performance during the experiment was assessed on several indices, including a monitoring task which required participants to detect pedestrian hazards. Participants completed the Driver Stress Inventory (DSI; Matthews, Desmond, Joyner, & Carcary, 1997) a measure of stress vulnerability in a driving context, the Dundee Stress State Questionnaire (DSSQ, Matthews, et al.., 1999; 2002), a measure of subjective mood state, and the NASA-Task Load Index (TLX; Hart & Staveland, 1988), a measure of the perceived mental workload associated with a task. Results of the experiment indicated that curved roadways impaired driver performance, but did not influence workload. Automation facilitated performance, but the effect was transient and only observed in the continuous-automation condition. Automation did not reduce driver workload; perceived workload was (open full item for complete abstract)

Committee: Dr. Gerald Matthews (Advisor) Subjects:

Doctor of Philosophy, University of Akron, 2024, Counseling Psychology

Health service psychology (HSP) students are at a high risk of burnout and physical and mental health problems (El-Ghoroury et al., 2011; Rummell, 2015). Research has begun to explore environmental factors within training programs that cause or prevent burnout (e.g., Kovach Clark et al., 2009; Swords & Ellis, 2017). Using the Job Demands-Resources (JD-R) model of burnout (Bakker & Demerouti, 2017; Demerouti et al., 2001), the present study assessed the relationships between demands, resources, and burnout symptoms in a sample of HSP doctoral students. Structural equation modeling was used to test the JD-R model and compare the relative contributions of perceived workload, weekly work hours, sense of community, work environment, and relationships with academic advisors and clinical supervisors to students' symptoms of exhaustion and disengagement. Analyses of variance were used to explore demographic group differences to better understand the experiences of diverse students, especially those with minoritized identities. Participants reported high levels of burnout symptoms, especially exhaustion. The hypothesized JD-R model, which contains unique and separate pathways representing the processes by which job demands sap energy and job resources promote engagement, did not yield interpretable parameters and thus was not a good fit to the data. However, an alternative model containing additional pathways between job demands and resources and burnout symptoms fit the data well and collectively predicted about half (50.8%) the variance in exhaustion and about a third (31.8%) of the variance in disengagement. The results demonstrated HSP doctoral students' experiences of burnout are highly linked to environmental factors. High demands impair students' health and create exhaustion, while lacking resources impair motivation and create disengagement. Perceived workload, sense of community, and the work environment had the largest effects on burnout sym (open full item for complete abstract)

Committee: Margo Gregor (Advisor); Joelle Elicker (Committee Member); Ingrid Weigold (Committee Member); Varunee Faii Sangganjanavanich (Committee Member); John Queener (Committee Member) Subjects: Clinical Psychology; Counseling Psychology; Education; Health Sciences; Multicultural Education; Occupational Health; Psychology; School Counseling

Doctor of Education , University of Dayton, 2024, Educational Administration

Burnout has become prevalent in various professional domains, including the field of education. Principals, at the forefront of educational institutions, face immense pressure and responsibilities. Recognizing the importance of addressing burnout, the aim of this study was to contribute to the existing body of research regarding burnout and the six areas of worklife. This study involved the use of a quantitative research approach based on surveys and statistical analyses to determine relationships among demographics, areas of worklife, and burnout levels among Alpine Creek Public Schools (ACPS) principals. A diverse range of demographic factors were considered, including age, gender, educational background, and years of experience. Moreover, the study involved a focus on the six areas of worklife—workload, control, reward, community, fairness, and values—to understand their influence on burnout. The study yielded statistically significant relationships connecting workload, control, and fairness with emotional exhaustion, depersonalization, and personal accomplishment. The data obtained from this study will serve as a valuable resource for ACPS leaders devising an action plan to support principals in the management of their complex jobs. By understanding the underlying factors contributing to burnout, ACPS leaders can implement targeted strategies to alleviate emotional exhaustion and depersonalization and enhance personal accomplishment among principals. These strategies may include workload management, fostering a sense of control, providing adequate rewards and recognition, promoting a supportive community, ensuring fairness, and aligning organizational values with the worklife of principals. By addressing emotional exhaustion and depersonalization and fostering personal accomplishment, ACPS leaders can enhance the worklife experiences of their principals, ultimately benefiting the entire educational system.

Committee: Kevin Kelly Ph.D (Committee Chair); Larry Irvin Ed.D (Committee Member); Meredith Wronowski Ph.D (Committee Member) Subjects: Education

Doctor of Education , University of Dayton, 2022, Educational Leadership

The study's focus is on special education delivery of service guidelines in practice and aims to research the factors that intervention specialists navigate when managing caseloads. Potential contributors to the study include intervention specialists, general education teachers, building administrators, and central office administration. The purpose of the study is to highlight gaps missing from current practices when determining what professional development needs should be taught to new or seasoned teachers to prepare them for the uprise in caseload numbers of students per one intervention specialist teacher. This participatory action research will evaluate teachers' current resources and determine gaps that might be present based on teacher interviews. The research questions this study seeks to address are: How can distribution with special education caseloads be managed equitably given 1.) current delivery of service caseloads 2.) professional development preparedness for special educators and (3) knowledge of disability diversity. Data collection occurred through interviews with intervention specialist teachers, school principals, a special education coordinator, and the director of student services. The themes associated with the research are caseload distribution, special education professional development, and available resources. Through these findings, the researcher suggested an action plan to develop IEP teams to fulfill the barriers associated with restricting intervention specialists in determining and managing their caseloads as one provider. The research suggested that teams working together will benefit all stakeholders when determining how to best educate students within their least restrictive environment.

Committee: Corinne Brion (Committee Chair); Kevin Gorman (Committee Member); Joni Baldwin (Committee Member) Subjects: Special Education

Master of Science (MS), Wright State University, 2022, Human Factors and Industrial/Organizational Psychology MS

This study examines the effects of automation on the human user of that automation. Automation has been shown to produce a variety of benefits to employees in terms of performance and a reduction of workload, but research in this area indicates that this might be at the cost of situational awareness. This loss of situational awareness is thought to lead to “out-of-the-loop” performance effects. One way this set of effects has been explained is through the “lumberjack” analogy, which suggests these effects are related to degree of automation and automation failure. This study recreates the effects of automation on mental workload, performance, and situational awareness by altering the characteristics of automation in a UAV supervisory control environment; RESCHU was chosen because of its complexity and the ability to manipulate levels of control within the task. Afterwards, it will be discussed whether the effects align with the predictions of the lumberjack analogy. Participants were assigned to one of two automation reliability groups, routine or failure, and all participants experienced all three degrees of automation – manual/low, medium, and high. Scores collected for mental workload, situational awareness, and performance were compared across groups and conditions. Results indicated differences in performance for both degree of automation and reliability, but no interaction. There was also a main effect of degree of automation on raw NASA-TLX scores, with a few main effects reported for individual subscales.

Committee: Assaf Harel Ph.D. (Committee Chair); Ion Juvina Ph.D. (Committee Member); Gregory Funke Ph.D. (Committee Member) Subjects: Psychology

Doctor of Philosophy, University of Akron, 2021, Mechanical Engineering

This dissertation investigates the development of a new severe weather warning paradigm through analysis as a Socio-Technical System (STS). This system implements the use of automation, collaboration and human interaction to communicate severe weather risks to end users and decision makers continuously in real time. This is the first time such a system has been developed for use in severe weather forecasting and there are few examples similar in adjacent industries for risk identification and communication. The studies included in this dissertation apply the well researched and reliable methods to a new research area of socio-technical systems. The successful completion of these studies yielded valuable human factors and systems engineering related findings to improve the the usability and reliability of the system, and overall system optimization. This research demonstrates the use, of NASA-TLX (Task Load Index) and Hierarchical Task Analysis (HTA) analysis, well accepted research methods within a socio-technical system. This provides specific and reliable methods for others to complete socio-technical system analysis that addresses some of the shortcomings of available STS methods.

Committee: Chen Ling Phd (Advisor); Shengyong Wang Phd (Committee Member); Guo-Xiang Wang Phd (Committee Member); I-Chun Tsai Phd (Committee Member); Sergio Felicelli Phd (Committee Member) Subjects: Systems Design

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

Office computer users experience work-related musculoskeletal disorders including pain in the neck, shoulders, and lower back. Previous researchers revealed that there were associations between the cognitive workload imposed by computer tasks and the increased biomechanical load which could eventually lead to adverse symptoms. However, there are limited data that describe how the different components of cognitive workload are associated with changes in computer users' biomechanical response to the work process. At the same time, although furnishings with lumbar support and relevant sitting guidelines have been provided in many office settings, there is limited evidence showing more supportive furniture is effective in reducing the risk of musculoskeletal disorders (MSDs) among office computer users. This study investigated: 1) whether computer users are sitting in the suggested neutral position and using the backrest when working on different types of computer tasks; 2) how the causal (task complexity and time pressure) and assessment factors (mental demand, mental effort, and task performance) of cognitive workload are related with the variations in computer users' biomechanical responses; and 3) whether using a footrest can be used to promote the use of backrest in computer tasks. The first stage of this dissertation was an observational study in which computer users' sitting postures were observed and recorded discretely as the observed individuals worked on different types of computer tasks. The findings revealed that chairs' back supports were not being used effectively that the users did not rest their whole back against the backrest. Following the observational study, a laboratory experiment was conducted to investigate how the computer tasks that varied in their level of cognitive workload, which was assessed in terms of mental demand, mental effort, and task performance, are associated with the variations in the computer users' biomechanical responses (open full item for complete abstract)

Committee: Steven Lavender (Advisor); Carolyn Sommerich (Committee Member); Michael Rayo (Committee Member) Subjects: Biomechanics; Design; Industrial Engineering; Occupational Health

Master of Science (MS), Ohio University, 2019, Industrial and Systems Engineering (Engineering and Technology)

This study used various statistical tools to identify key factors contributing to adverse patient outcomes at a mid-western hospital. Nurses working in an acute care hospital setting are faced with a broad set of task demands that can vary several times during a normal work shift. Task demands presented by patients must be actively monitored to ensure that proper care is being provided. In addition to challenges presented by patients, nurse fatigue can pose a threat to patient outcomes. Understanding risk factors that contribute to increased nurse fatigue can provide solutions to reduce the impact that fatigue has on nurse performance and patient outcome. This was comprised of two phases: 1) a database analysis of current data collected at a mid-western hospital, and 2) three focus groups to identify nurse perceptions pertaining to task demand and fatigue. The adverse patient outcomes (APOs) analyzed in this study were medical administration errors (MAEs) and patient falls. A comparison of the data from both phases was then conducted to determine whether data reflected in the database correlated with nurse perceptions. This study found significant results with respect to APOs in the following workload factors such as: unit capacity, hours worked, CMI, patient age, and nursing unit type. 

Committee: Diana Schwerha (Advisor) Subjects: Engineering; Health Care; Health Care Management; Industrial Engineering

PhD, University of Cincinnati, 2019, Engineering and Applied Science: Computer Science and Engineering

Modern hardware architectures and advances in database technology are driving increased adoption of logical data warehouses (LDWs) that complement traditional physical data warehousing approaches. The successful design of an LDW depends on the ability to quickly integrate and transform data at run time and can benefit immensely from the judicious replication of high value data to maximize spatial locality and leverage premium hardware and database resources for the most important operations. Identifying and collocating high value data is a non-trivial task whose solution touches a number of existing research areas but has not been adequately explored or answered, particularly in the context of vendor offerings such as SAP HANA, an in-memory, columnar, massively parallel processing (MPP) database management system (DBMS) platform that supports multi-node, multi-temperate storage configurations. We investigate this problem by adopting a graph-based perspective of query workloads and searching for clusters or communities within the workload that can be leveraged to inform data placement. We demonstrate that within the constraints of a given storage budget on a columnar system functioning as the core of an LDW, it is possible to achieve improved utilization of in-memory capabilities by carefully choosing which data elements should be replicated to hot, warm, and cold storage tiers. Analytical queries are mined to identify relationships and access patterns using clustering algorithms to arrive at subgraphs whose nodes (data elements) offer the most benefit from being collocated. We introduce new algorithms to address the preprocessing of the workload, identification of clusters, and assignment of clusters to appropriate storage tiers allowing the LDW to deliver results more efficiently by covering a higher percentage of its query workload using the fastest storage devices.

Committee: Raj Bhatnagar Ph.D. (Committee Chair); Gowtham Atluri Ph.D. (Committee Member); Karen Davis Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Nursing Practice , Case Western Reserve University, 2019, School of Nursing

Aims and objectives: to identify nurses' perceptions of barriers and facilitators to the implementation of the Workload Acuity Scale (WAS) at a facility located in the Middle East and to describe differences between nurses' perceptions working in acute care and critical care areas. Background: Precise and accurate determination of workload acuity is imperative to adequately assign workload in the delivery of nursing care. Nurses at this facility were expressing concerns of unequal assignments; therefore, the hospital leadership identified an organization wide system to measure patient acuity accurately through the “Workload Acuity Scale”. The data from the WAS will be used to create assignments for nurses in patient care delivery. Successful implementation of the WAS requires an understanding of the barriers and facilitators to the WAS integration. Method: The Theoretical Domains Framework guided this pre-implementation project. A survey was developed to capture nurses' perceived barriers and facilitators to inform the plan for future implementation of the WAS. Nurses completed a survey that consisted of 17 items using a 1 to 5 Likert scale and two open-ended questions. Results: Three major themes emerged as barriers to the implementation of the WAS that included knowledge, skills, and resources in terms of human resources and time. The facilitators identified were the availability and accessibility of computers to document patient care activities and intention of nurses who will ensure the successful implementation of the WAS through accurate and timely documentation of patient care activities. Conclusion: The findings of this project demonstrate that a pre-implementation step is critical to the development of an implementation plan that addresses perceived barriers and facilitators from end users. Our findings confirm that nurses at this facility want to successfully implement the WAS to improve their staffing levels but need support to overcome th (open full item for complete abstract)

Committee: Mary Dolansky (Committee Chair); Carol Savrin (Committee Member); Iyaad Hasan (Committee Member) Subjects: Health Care; Health Care Management; Nursing

Doctor of Philosophy, University of Toledo, 2018, Engineering (Computer Science)

A strong emphasis on safety in commercial and military aviation is as old and as significant as the field of aviation itself. With the growing role of autonomy in aviation, the future of flight comprises of two general directions: manned and unmanned. Manned aircraft is the more established area, in which a human flight crew serves as the main driving force in ensuring an aircraft's safety and success. Within this time-tested concept, the most significant bottleneck of safety lies within a crew managing tasks of high mental workload. In recent years, autonomy has aided in easing cognitive workload. From there, the challenge lies within applying a seamless blend of human and autonomous control based on the needs of one's mental load. Meanwhile, the field of unmanned aerial vehicles (UAVs) poses its own unique challenges of integrating into a shared airspace and transitioning from remote human-centric control to fully autonomous control. In such a case, minimizing discrepancies between predicted UAV behavior and actual outcomes is an ongoing task to ensure a safe and reliable flight. While manned and unmanned flight safety may seem distinctly different in these regards, this dissertation proposes an overarching common theme that lies within the ability to effectively model inputs and outputs through machine learning to predict potential safety hazards and thereby improve the overall flight experience. This process is conducted by 1) evaluating different machine learning techniques on assessing cognitive workload, 2) predicting trajectories for autonomous UAVs, and 3) developing adaptive systems that dynamically select appropriate algorithms to ensure optimal prediction accuracy at any given time. The first phase of the research involves the manned side of flight safety and does so by examining effects of different machine learning techniques used for assessing cognitive workload. This begins by comparing the different algorithms on four different datasets i (open full item for complete abstract)

Committee: Vijay Devabhaktuni PhD (Committee Chair); Mansoor Alam PhD (Committee Member); Ahmad Javaid PhD (Committee Member); Devinder Kaur PhD (Committee Member); Weiqing Sun PhD (Committee Member); Lawrence Thomas PhD (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, The Ohio State University, 2018, Computer Science and Engineering

Research for large-scale system is challenging because deploying a large system needs a great amount of resources. My approach to address this problem is based on the observation that most large-scale systems follow a "centralized metadata and sharded data" design, which provides opportunities to extend work at small scales to large scales: for data processing, optimization in one shard can automatically be extended to all shards because they all execute the same protocol; for metadata processing, repetition of behaviors from different data shards allows us to extrapolate their requests to the centralized metadata server, making it possible to stress test a metadata server with limited number of machines. First, to optimize data processing, we focus on replication protocols, because they are widely used in today's distributed systems to protect data against failures and replicating data brings a significant cost. Typically, stronger replication protocols that can tolerate more kinds of errors require more replicas. In our work, we propose a general approach to reduce the replication cost of asynchronous state machine replication protocols, while maintaining their availability properties. For example, our approach can reduce the number of replicas of Paxos from 2f+1 to f+1. Second, to optimize metadata processing, we find the key is to be able to evaluate metadata servers: to reduce overhead on centralized metadata servers, existing systems tries to minimize traffic to the metadata servers, but this brings a challenge that their problems are hard to observe at small scales. In our work, we propose PatternMiner, a tool that extrapolates system's messages at a large scale based on logged system's messages to metadata service at small scales. And then we can play generated workload on the targeted metadata service to measure its throughput and analyze its behavior at large scale. Our evaluation on two types of metadata services of YARN framework, HDFS NameNode and (open full item for complete abstract)

Committee: Yang Wang (Advisor); Xiaodong Zhang (Committee Member); Feng Qin (Committee Member); Spyros Blanas (Committee Member) Subjects: Computer Engineering; Computer Science

Master of Science in Industrial and Human Factors Engineering (MSIHE) , Wright State University, 2018, Industrial and Human Factors Engineering

An ideal work environment supports a culture of high performance, low mental workload, and quick turnarounds. The impact of noise on three types of tasks in a lab work environment were examined while attempting to identify correlations between a subject's personality and their tolerance to noise. Neuroticism, agreeableness, conscientiousness, and extroversion correlated significantly with subjective (NASA-TLX) and physiological mental workload measures (heart rate variability and eye-tracking). The results show that task type impacts the performance, task duration, and mental workload. Although the physiological workload measures showed significant impact, the parameters standard deviation of R-R intervals and LF/HF ratio agreed with the NASA-TLX scores while the parameters RMSSD value and standardized mean of R-R intervals disagreed. Noise level nearly showed statistical significance with task duration and LF/HF ratio; however, more research is necessary to completely rule out the influence of noise level on the human participants.

Committee: Mary Fendley Ph.D. (Advisor); Frank Ciarallo Ph.D. (Committee Member); Matthew Sherwood Ph.D. (Committee Member) Subjects: Biomedical Engineering; Industrial Engineering; Personality Psychology

Master of Science in Industrial and Human Factors Engineering (MSIHE) , Wright State University, 2018, Industrial and Human Factors Engineering

The development and use of consumer grade drones is becoming a larger part of our society for many different applications. There has been a great amount of discussion and constant review of proper operation of consumer drones including proper methods of control. In turn, regulation of such devices has been inconsistent. This study aims to better understand the effects of the three primary control interface methods (line of sight, video aided, and first-person view) on flight performance, situational awareness, and perceived mental workload of the operator. Secondarily, this study aims to provide design recommendations for future interfaces. This study shows that the first-person view control interface results in a longer flight time around a course, higher mental workload, and lower situational awareness when compared to line-of-sight and video aided control. The use of line-of-sight control performed superiorly in all areas, and the video-aided interface was very close behind.

Committee: Subhashini Ganapthy Ph.D. (Advisor); Mary Fendley Ph.D. (Committee Member); Sasanka Prabhala Ph.D. (Committee Member) Subjects: Engineering; Industrial Engineering; Neurosciences

DNP, Otterbein University, 2018, Nursing

IMPACT OF STUDENT NURSES; CLINICAL ON THE WORKLOAD OF RNs ON A MEDICAL-SURGICAL UNIT OF A CRITICAL ACCESS HOSPITAL ABSTRACT This pilot study explored Registered Nurses (RNs) perceptions of the impact of student nurses' clinical on the workload of medical-surgical RNs in a Critical Access Hospital (CAH). The real-life experience of students in the clinical learning environment has been seen as a valuable part of nursing student education. With the increase in the number and size of nursing programs leading to a shortage of clinical sites, maintaining a positive relationship with clinical sites is extremely important to nursing programs. Previous research has found that nurses often have ambivalent feelings regarding nursing students on their units. Purpose: Explore RNs” perceptions of the impact of student nurses' clinical on the workload of medical-surgical RNs in a Critical Access Hospital. Background: Nursing programs in rural areas often utilize rural hospitals, many of which are Critical Access Hospitals (CAHs). CAHs have features and challenges that make their nurses' work environment unique. With a shortage of clinical sites and the challenging work environment of RNs in CAHs, it is vital that nursing programs maintain a good relationship with these nurses. No studies have been reported specific to medical-surgical RNs and students in a CAH. Method: Mixed-method, descriptive, pilot study. The quantitative portion utilized a 38 item, electronic, adapted version of the Nursing Students' Contributions to Clinical Agencies (NSCCA). The qualitative portion of the study utilized RN interviews, guided by semi-structured open-ended questions. Population: Regularly scheduled, day-shift RNs on a medical-surgical unit of a Critical Access Hospital. Results: RNs perceived the impact of student nurses' clinical as positive. RNs with less than 10 years of experience viewed students more positive than RNs with more than 10 years of experience. Conclus (open full item for complete abstract)

Committee: Jacqueline Haverkamp (Advisor) Subjects: Community College Education; Nursing

Master of Science, University of Toledo, 2017, Electrical Engineering

There has been tremendous growth in the quality of communication in the human-computer interaction field. Some of the focus areas have included intelligent adaptive interfaces, and multi modality. An emerging topic in this field of research involves optimal collaboration between humans and machines to achieve a particular goal. One approach to such a goal involves sliding-scale autonomy, in which a machine is designed to dynamically adjust between different levels of autonomy based on a variety of factors, such as the skill level, workload, and behavior of the human operator. This thesis proposes a system to dynamically predict skill level and workload for pilots on a flight simulator using classification and regression algorithms, respectively. The proposed system uses the pilot's heart rate variability and flight control data. The flight control data includes pilot interactions, such as throttle and aileron, and flight sensor data, such as latitude and longitude. A user study on fifteen pilots was conducted, each flying the same five predefined routes on a flight simulator. The results indicate that the flight control data alone is sufficient to provide a near perfect classification of a pilot's skill level of either expert or novice. On the other hand, it was found that a combination of flight control and heart rate data produced a more accurate estimate of mental workload and effort. The findings provide the first step towards a sliding-scale autonomous system for airplane pilots.

Committee: Kevin Xu (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ahmad Javaid (Committee Member); Scott Pappada (Committee Member) Subjects: Computer Science; Electrical Engineering; Engineering