Failure to rescue (FTR) outcomes may be one consequence of the relationship be-tween healthcare provider behaviors and attitudes, organizational factors, and environ-mental factors that intersect to potentially threaten patient safety. Early warning systems (EWS) were designed as surveillance systems to reduce failure to rescue events and avoid morbidity and mortality. Challenges with EWS include lack of standardization, organiza-tional barriers, such as culture and supports, and human factor attributes such as intuition, expertise, and experience. The experience and perceptions of nurses using EWS technolo-gy as it relates to their clinical assessment, critical thinking, and decision-making skills has yet to be undertaken. This study adds to the body of EWS and FTR literature and the broader culture of safety literature in acute care environments. The purposes of this exploratory qualitative descriptive study was to explore the experiences of nurses using EWS in acute care practice settings and how they perceive it impacts on their critical thinking and clinical decision-making processes. The study identi-fied three informative findings: a) EWS has added value particularly with novice nurses or nurses new to practice settings, b) EWS provides benefits to nurses working in acute clinical environments that experience high volumes and high acuity of patients by alerting or reminding them about potential FTR situations, and c) Existing EWS may require mod-ification to improve adequacy, reduce redundancy, and reduce alarm fatigue. Based on the evidence reviewed, a qualitative study to increase our understanding of the experi-ence of nurses and their perception of the impact of EWS and related technology on their critical thinking and other nursing practice processes has the potential to contribute to a wider evaluation of EWS systems and to improve patient outcomes. / Thesis / Master of Science (MSc) / Before patients on general medical or surgical hospital units become so ill that they need to be transferred to an intensive care unit, they have abnormal vital signs and other physiological changes that can go unnoticed for 2448 hours. As these changes can be hard to detect and serious illness can begin very slowly at first, early warning systems have been developed to help health care professionals respond to patient’s conditions before they need to be in an intensive care unit. These early warning systems can be in the form of new technology and assist nurses with preventing a serious illness from becoming critical. It is not well research or understood how nurses experience such early warning systems technology and it is not well understood how nurses think this technology impacts their nursing practice. The overall aim of the study is to learn more about the experience and perception of nurses who use this technology, how they believe it informs their nursing practice and how it supports them in making clinical decisions about patient care.

Rationale: Individuals with addictive disorders commonly exhibit a shortened temporal window, which interferes with treatment focusing on long-term sobriety. Episodic Future Thinking (EFT) involves generating personalized cues related to anticipated, positive events at various future time points. EFT has been shown to reduce the reinforcing value of addictive substances; however, this has only been shown in non-treatment samples. Purpose: To examine the feasibility, cumulative, and sustained effects of implementing EFT in a treatment seeking addiction sample over a 1-week protocol on decision-making and alcohol motivation. Methods: Twenty-eight treatment seeking individuals were randomly assigned to either undergo an EFT intervention or a control Episodic Recent Thinking (ERT) protocol. Assessments were completed at baseline, end of week 1, and a 1-week follow-up. Measures included a delay discounting task, hypothetical alcohol purchase task, clinical outcome measures, and cognitive mechanism measures. Results: There were significant reductions in alcohol demand indices, delay discounting rates, and an increase in mindful attention awareness after both acute and extended exposure to EFT. Furthermore, the EFT group showed greater reductions compared to the ERT group after extended exposure to their cues. Conclusion: The results suggest that early implementation of EFT in a treatment seeking addiction sample is beneficial to counteract motivating factors for relapse. This study lays the foundation for future clinical trials for EFT as a supplemental therapy for addictions treatment. / Thesis / Master of Science (MSc) / People with substance use disorders have a significantly shortened time perspective compared to healthy controls. This means that these individuals struggle with thinking about future events beyond several days to a week. Shortened time perspective can be a significant barrier to addiction treatments that typically focus on long-term positive benefits of sobriety or low-risk use. This study examined whether mindful thinking about future events impacted decision-making and motivation for alcohol and drugs. The study used an experimental protocol known as Episodic Future Thinking (EFT) that involves participants interacting with personalized cues related to positive future events. Prior research using EFT in addiction samples has found that interacting with future cues significantly increases delay of gratification, reduces cigarette use, and decreases reinforcing value of alcohol. In this study, we recruited 28 participants with an alcohol use disorder (AUD). Participants practiced EFT training over a two-week protocol. We tested decision making, alcohol craving, and other variables following a single EFT protocol, and changes in these measures over repeated practice. We found significant changes in alcohol craving, decision making, and mindfulness awareness. The study provides proof-of-concept for using EFT in an AUD treatment population and lays the foundation for future clinical trials of EFT as a complement to existing addiction treatments.

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Physical inactivity is a major contributor to increased disease prevalence and reduced quality of life. Measuring behavioral economic demand for exercise may enable more effective physical activity intervention development. In study one, we developed the leisure-time-as-price exercise purchase task (LT-EPT), wherein participants (n = 175) indicate hypothetical likelihood to trade leisure time for access to exercise time. We observed weak to moderate correlations between demand indices (Q1%, α, BP1, and Pmax) generated from the LT-EPT and self-reported leisure and exercise time, demonstrating initial validation of the LT-EPT. In study two, we examine the effect of episodic future thinking (EFT; vivid, personalized prospection of future events) in adults not meeting physical activity guidelines (n = 127) on demand for exercise and delay discounting (sensitivity to delayed rewards). We observed reduced delay discounting in participants randomized to engage in EFT, but no difference between EFT and health information thinking (HIT) controls. In study three, we further examined the effect of EFT on demand for exercise in adults with type 2 diabetes and obesity participating in a 24-week randomized controlled trial (n = 71). All participants engaged in a multicomponent behavioral intervention focused on weight loss and glycemic control; additionally, participants were randomized to engage in EFT or HIT thrice daily beginning in week 3. We measured demand for exercise and delay discounting (among other outcomes) at weeks 0, 8, and 24, observing no differences between EFT or HIT groups in demand indices (Q1%, α) or delay discounting at any time point. In conclusion, early evidence suggests that the LT-EPT may be a valid method to measure behavioral economic demand for exercise; however, EFT may not be an effective intervention to increase demand for exercise. / Doctor of Philosophy / Physical inactivity poses a significant threat to our well-being, contributing to increased disease rates and a diminished quality of life. This dissertation details a novel method to measure how people value exercise and the effect of a behavioral intervention to increase exercise valuation. In the first study, we introduce the leisure-time-as-price exercise purchase task (LT-EPT), a tool designed to gauge individuals' willingness to trade leisure time for exercise time (i.e., exercise demand). Initial results show promising correlations between LT-EPT metrics and self-reported leisure and exercise time, providing a foundation for its potential as a valuable measurement tool. The second study examines the impact of episodic future thinking (EFT), a technique involving vivid and personalized visualization of future events, on exercise demand. While participants engaging in EFT showed increased preference for larger, delayed rewards over smaller, sooner rewards (i.e., reduced delay discounting), no significant difference was found between EFT and the health information thinking (HIT) control in terms of exercise demand. The third study expands our investigation to adults with type 2 diabetes and obesity undergoing a 24-week intervention. All participants engaged in a comprehensive behavioral program, while half were randomized to engage in EFT or HIT three times per day. No discernible differences were observed in exercise demand or delay discounting at any measurement point. In summary, our findings suggest that the LT-EPT may be a valid measure of exercise demand. However, the effectiveness of EFT in increasing demand for exercise remains inconclusive. These insights contribute to the ongoing efforts to develop more targeted and impactful interventions for promoting physical activity and improving overall health.

Parallel thinking is a mindset that enables computer scientists to think about and implement systems that allow activities to happen concurrently. This mindset is needed in designing and implementing a wide range of computer systems involving coordinated components (e.g., parallel, distributed, and multi-user systems). No matter what the coordinated component is, whether human or computer, the underlying issue is to imagine coordination between these components and manage the distribution and reintegration of coordinated work. The rapid development of multi-core technologies has attracted people's attention back to parallelism. Ubiquitous and pervasive computing further brings parallelism into the everyday experiences of non-computer scientists. Designing and developing for ubiquitous parallelism become an essential and heavy responsibility for every software designer and developer. This situation creates a new standard for every one working in the computing field; simply understanding the techniques and algorithm in parallel-distributed computing to support parallel computing resources is not enough; the ability to create support for parallel human activities is also needed. Therefore, the need to train CS students to have a "parallel thinking" mindset is more urgent than ever. This doctoral work approaches the pedagogy of parallel thinking by teaching CS students to model coordination for parallel human activities explicitly. Although most participants started with an undeveloped imagination for human coordination, they were able to improve by focusing on coordination issues in the context of a class. The research method was to study a semester-long experimental class in the Department of Computer Science at Virginia Tech through a qualitative design-based research approach. Multiple types of data were collected using methodological triangulation to maximize validity. The data analysis process was guided by Grounded Theory (GT) through a systematic set of procedures. The outcomes provide a rich, thick, and detailed description about how CS students conceptualize and approach parallel thinking. The research contributes to CS education, programmer cognition literature, and computer supported collaborative system design and development by elaborating and analyzing various challenges in coordinated system creation, and making suggestions about pedagogical solutions, and software infrastructure and tools design. / Ph. D.

Learners' affective states correlate with learning outcomes. A key aspect of instructional design is the choice of modalities by which learners interact with instructional content. The existing literature focuses on quantifying learning outcomes without quantifying learners' affective states during instructional activities. An investigation of how learners feel during instructional activities will inform the instructional systems design methodology of a method for quantifying the effects of individually available modalities on learners' affect. The objective of this dissertation is to investigate the relationship between affective states and learning modalities of instructional computing. During an instructional activity, learners' enjoyment, excitement, and motivation are measured before and after a computing activity offered in three distinct modalities. The modalities concentrate on visual and musical computing for the practice of computational thinking. An affective model for the practice of computational thinking through musical expression was developed and validated. This dissertation begins with a literature review of relevant theories on embodied cognition, learning, and affective states. It continues with designing and fabricating a prototype instructional apparatus and its virtual simulation as a web service, both for the practice of computational thinking through musical expression, and concludes with a study investigating participants' affective states before and after four distinct online computing activities. This dissertation builds on and contributes to extant literature by validating an affective model for computational thinking practice through self-expression. It also proposes a nomological network for the construct of computational thinking for future exploration of the construct, and develops a method for the assessment of instructional activities based on predefined levels of skill and knowledge. / Doctor of Philosophy / This dissertation investigates the role of learners' affect during instructional activities of visual and musical computing. More specifically, learners' enjoyment, excitement, and motivation are measured before and after a computing activity offered in four distinct ways. The computing activities are based on a prototype instructional apparatus, which was designed and fabricated for the practice of computational thinking. A study was performed using a virtual simulation accessible via internet browser. The study suggests that maintaining enjoyment during instructional activities is a more direct path to academic motivation than excitement.

Iteration, the repetition of computational steps, is a core concept in programming. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech's Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students' knowledge of iteration did not satisfy the course objectives in Spring 2018, because the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises for Fall 2018 and Spring 2019. For three semesters we conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). We found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted surveys in Fall 2018 and Spring 2019 from students to learn more about their recall, helpfulness, and reuse of the interactive visualizations. Finally, we analyzed data from the interactive exercises and page use to investigate students' usage behavior. / Master of Science / Iteration is a process of repeating a set of instructions or structures. An iterative process repeats until a condition is met or a specified number of repetitions is completed. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech’s Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students’ knowledge of iteration did not satisfy the course objectives in Spring 2018. In particular, the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises that were used during Fall 2018 and Spring 2019. We conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). To see if there was a statistically significant difference between the absolute score means of three groups, we used independent sample t-tests. Also we used paired sample t-tests to see if there was a greater knowledge gain after using our invention. By analyzing the results of the t-tests, we found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted student surveys in Fall 2018 and Spring 2019 to learn more about their opinions on recall, helpfulness, and reuse of the interactive visualizations. We analyzed data from the interactive exercises and page use to investigate students’ usage behavior.

Alcohol use disorder is common, and treatments are currently inadequate. Some of the acute effects of alcohol on the brain, such as altering the decision-making and future thinking capacities, mirror the effects of chronic alcohol use. Therefore, interventions that can address these shortcomings may be useful for reducing the negative effects of alcohol use disorder in combination with other therapies. The signature of those interventions may also be evident in the signature of large-scale, dynamic brain networks, which can show whether an intervention is effective. One such intervention is episodic future thinking, which has been shown to reduce delay discounting and orient people toward pro-social, long-term outcomes. To better understand decision making in high-risk individuals, we examined delay discounting in an adolescent population. When the decision-making faculties were challenged with difficult choices, adolescents made decisions inconsistent with their predicted preference, complemented by increased brain activity in the central executive network and salience network. Using these results and the hypothesis that the default mode network would be implicated in future thinking and intertemporal choice, we examined the neural effects of a brief behavioral intervention, episodic future thinking, that seeks to address these impairments. We showed that episodic future thinking has both acute and longer-lasting effects on consequential brain networks at rest and during delay discounting compared to a control episodic thinking condition in alcohol use disorder. Our failure to show group differences in default mode network prompted us to scrutinize it more carefully, from a position where we could measure the ability to self-regulate the network rather than its resting-state tendency. We implemented a real-time fMRI experiment to test the degree to which people along the alcohol use severity spectrum can self-regulate this network. Our results showed that default mode network suppression is impaired as alcohol use disorder severity increases. In the process, we showed that direct examination of resting-state networks with these methods will provide more information than measuring them at rest alone. We also characterized the default mode network along the real-time fMRI pipeline to show the whole-brain spatial pattern of regions associated and unassociated with the network. Our results indicate that resting-state brain networks are important markers for outcomes in alcohol use disorder and that they can be manipulated under experimental conditions, potentially to the benefit of the afflicted individual. / Doctor of Philosophy / Alcohol is the most widely-used mind-altering substance in the United States. Even though most people do not develop a problem with alcohol use, many people will at some point develop drinking patterns that classify as an alcohol use disorder. Brain damage from drinking can come from the toxicity of alcohol, but also as a result of behaviors associated with drinking too much, including injury, violence, accidents, and other health-related issues. Interventions at the behavioral level can be effective at curbing drinking patterns before damage accrues, and a better understanding of those interventions at the level of the brain may make them more effective. This work investigated the decision-making processes and the ability to think clearly about the future, two faculties that begin to become diminished in alcohol use disorder. In our first set of studies, we tested a brief behavioral intervention called episodic future thinking, which helps people orient themselves away from short-term rewards like alcohol and toward long-term goals that could happen if they stopped drinking as much. We showed that one hour-long, intensive session produced changes in the connectivity between the prefrontal cortex and the lower brain. We also generated data in a long-term experiment suggesting repeated reminders of the episodic future thinking intervention produce changes in large-scale brain networks that are disrupted in substance use disorders. In a separate set of experiments, we showed that people can gain control over one of these networks, called the default mode network, to the point of being able to control a brain-machine interface just by following simple instructions. However, we demonstrated that the degree to which someone can control this brain activity was associated with their drinking severity. In other words, the more people drank, in terms of volume and frequency, the less control they had over their own brain activity. This finding is important because many researchers have shown that activity in this brain region is related to many psychopathologies, including substance use disorders. Other researchers have been developing ways in which the ability to control this brain activity can be trained. While we did not find evidence of a training effect in a small group of healthy people (5), it may be the case that people impaired by alcohol use disorder can improve through practice or through cutting back on drinking. Ultimately, we hope that the research presented here will help to guide the development of treatments for alcohol use disorder to be more effective.

Skill sets such as understanding and applying computational concepts are essential prerequisites for success in the 21st century. One can learn computational concepts by taking a traditional course offered in a school or by self-guided learning through an online platform. Collaborative learning has emerged as an approach that researchers have found to be generally applicable and effective for teaching computational concepts. Rather than learning individually, collaboration can help reduce the anxiety level of learners, improve understanding and create a positive atmosphere to learning Computational Thinking (CT). There is, however, limited research focusing on how natural collaborative interactions among learners manifest during learning of computational concepts. Structured as a manuscript style dissertation, this doctoral study investigates three different but related aspects of novice learners collaboratively learning CT. The first manuscript (qualitative study) provides an overall understanding of the contextual factors and characterizes collaborative aspects of learning in a CT face-to-face classroom at a large Southeastern University. The second manuscript (qualitative study) investigates the social interaction occurring between group members of the same classroom. And the third manuscript (quantitative study) focuses on the relationship between different social interactions initiated by users and learning of CT in an online learning platform Scratch™. In the two diverse settings, Chi's (2009) Differentiated Overt Learning Activities (DOLA) has been used as a lens to better understand the significance of social interactions in terms of being active, constructive and interactive. Together, the findings of this dissertation study contribute to the limited body of CT research by providing insight on novice learner's attitude towards learning CT, collaborative moments of learning CT, and the differences in relationship between social interactions and learning CT. The identification of collaborative attributes of CT is expected to help educators in designing learning activities that facilitate such interactions within group of learners and look out for traits of such activities to assess CT in both classroom and online settings. / PHD / One of the overarching processes defining the future is the digital revolution, impinging on, reshaping, and transforming our personal and social lives. Computation is at the core of this change and is transforming how problems are defined, and solutions are found and implemented. Computer modeling, simulation and visualization software, Smart grid, and Software Defined Radio, are few examples where computation has allowed us to tackle problems from varied perspectives. Vast domains await discovery and mapping through creative processes of Computational Thinking (CT). CT is the thought process that enables us to effectively work in such a technology driven collaborative society. It provides us the ability to find the right technology for a problem and apply technology to resolve the problem. Skill sets such as understanding and applying computational concepts are essential prerequisites for success in the 21st century. One can learn CT by taking a traditional course offered in a school or by self-guided learning through an online platform. This doctoral study investigates three different but related aspects of how new learners are learning CT. The first qualitative study provides an overall understanding of circumstantial factors that influence the learning in a CT face-to-face classroom at a large Southeastern University. The second qualitative study investigates how students in groups (in the same classroom setting) can help each other to learn CT. And the third quantitative study focuses on users’ learning of CT in an online learning platform Scratch™. Together, the findings of this dissertation study contribute to the limited body of CT research by providing insight on new learner’s attitude towards learning CT, collaborative moments of learning CT, and the differences in the relationship between social interactions and learning CT. The identification of collaborative attributes of CT is expected to help educators in designing learning activities that facilitate such interactions within a group of learners and look out for traits of such activities to assess CT in both classroom and online settings.

Converging cognitive and neuroimaging evidence reveals that episodic memory and episodic future thinking (EFT) share component processes. Much less is known about the relationship between EFT and working memory (WM) processes. We hypothesized that WM capacity might provide a crucial componential cognitive role during EFT by supporting the translation of information from discrete episodic memories into a novel future event. We tested this hypothesis in two studies. In Study 1, we collected functional magnetic resonance imaging data during a dual-task interference paradigm that varied WM load and processing demands during EFT. Events imagined while actively maintaining bound item-location representations were less vivid than those imagined during low WM load control trials. Measures of functional and effective connectivity indicated that this behavioral effect corresponded with reduced coupling between the dorsomedial prefrontal cortex and right temporoparietal junction. Events imagined while simultaneously manipulating items in WM took longer to construct than events imagined during control trials and were associated with less functional coupling between the right hippocampus and posterior visuospatial regions. In Study 2, participants completed a similar WM dual-task while simultaneously recalling past events or imagining future events during scalp-recorded encephalography (EEG). As in Study 1, future events imagined while maintaining item-location representations were less vivid than control trials. This effect was specific to future events and corresponded to reduced theta reactivity over bilateral temporoparietal sites. Relative to episodic memory, EFT was associated with alpha synchronization over frontal and parietal sites as well as greater theta-gamma phase amplitude coupling in the right dorsolateral prefrontal cortex. In contrast, episodic memory was associated with greater cross-frequency coupling between frontal theta and occipital gamma oscillations. These results provide novel empirical support for previous theoretical accounts suggesting that WM capacity provides the cognitive workspace necessary to temporarily store and recombine details from discrete episodes into a future event representation. / PHD / This study used neuroimaging and behavioral techniques to identify how long-term and short-term memory processes interact to support the ability to imagine future events. The results suggest that short-term memory serves as a mental stage on which memories of the past are held and transformed into imagined scenes.