Crowdsourcing involves releasing tasks on the internet for people with diverse backgrounds and skills to solve. Its adoption has come a long way, from scaling up problem-solving to becoming an environment for running complex experiments. Designing tasks to obtain reliable results is not straightforward as it requires many design choices that grow with the complexity of crowdsourcing projects, often demanding multiple trial-and-error iterations to properly configure. These inherent characteristics of crowdsourcing, the complexity of the design space, and heterogeneity of the crowd, set quality control as a major concern, making it an integral part of task design. Despite all the progress and guidelines for developing effective tasks, crowdsourcing still is addressed as an ``art'' rather than an exact science, in part due to the challenges related to task design but also because crowdsourcing allows more complex use cases nowadays, where the support available has not yet caught up with this progress. This leaves researchers and practitioners at the forefront to often rely on intuitions instead of informed decisions. Running controlled experiments in crowdsourcing platforms is a prominent example. Despite their importance, experiments in these platforms are not yet first-class citizens, making researchers resort to building custom features to compensate for the lack of support, where pitfalls in this process may be detrimental to the experimental outcome. In this thesis, therefore, our goal is to attend to the need of moving crowdsourcing from art to science from two perspectives that interplay with each other: providing guidance on task design through experimentation, and supporting the experimentation process itself. First, we select classification problems as a use case, given their importance and pervasive nature, and aim to bring awareness, empirical evidence, and guidance to previously unexplored task design choices to address performance concerns. And second, we also aim to make crowdsourcing accessible to researchers and practitioners from all backgrounds, reducing the requirement of in-depth knowledge of known biases in crowdsourcing platforms, experimental methods, as well as programming skills to overcome the limitations of crowdsourcing providers while running experiments. We start by proposing task design strategies to address workers' performance, quality and time, in crowdsourced classification tasks. Then we distill the challenges associated with running controlled crowdsourcing experiments, propose coping strategies to address these challenges, and introduce solutions to help researchers report their crowdsourcing experiments, moving crowdsourcing forward to standardized reporting.

Within an achievement motivation theoretical framework, there are factors thought to most heavily influence performance and task difficulty selection. More specifically, motivational climates, feedback, confidence, and anxiety have all been identified as important factors influencing outcomes within performance settings. Much of the literature in the area of achievement motivation has focused on on the effects of mastery- and ego-oriented feedback on performance within academic settings and has received limited attention in the sport psychology literature within an athletic setting. Given the demonstrated effects of mastery- and ego-oriented feedback on performance, the importance of performance within the athletic context, and the scant literature examining the effects of feedback on athletic performance, the influence of feedback on sport performance needed to be empirically examined. The primary aim of this study was to provide a clearer understanding of the relationship of factors influencing athletic performance, with the ultimate goal of moving research toward a greater understanding of how optimal performance is achieved. As a result, this research may prove applicable to researchers, coaches, and athletes working toward optimal performance. In this study, I examined how mastery- and ego-oriented feedback influenced youth athletes' soccer performance, task difficulty selection, confidence, and anxiety. Youth soccer athletes (n = 71) participated in a soccer kicking task consisting of two trials. Between subjects ANCOVA analyses revealed athletes receiving mastery-oriented feedback performed significantly better on the soccer kicking task than athletes receiving ego-oriented feedback. No differences were discovered on task difficulty selection, confidence, or anxiety. Providing athletes mastery-oriented feedback before or after skill execution could be helpful in the development of athletic skill development and performance. Limitations of the present study and questions to examine in future research are also discussed.

Auditors face strong incentives to execute tasks efficiently and meet deadlines; these conditions are both conducive to – and rewarding of – implemental mindsets. However, an implemental mindset may deprioritize careful analysis and thoughtful decision-making, leading to suboptimal performance and audit quality. Conversely, deliberative mindsets promote critical thinking and open-mindedness – and research suggests auditors in a deliberative mindset perform complex tasks more effectively than auditors in an implemental mindset. Additionally, auditors encounter frequent reminders about the consequences of audit failures. This study examines how these factors (i.e., mindsets and consequence reminders) jointly influence auditors' performance on complex tasks. I predict that consequence reminders will be helpful to auditors in an implemental mindset but counter-productive to auditors in a deliberative mindset. Consistent with theory, results from a 2x2 experiment reveal that undergraduate student participants in a deliberative mindset outperform those in an implemental mindset in an error identification task. However, I find no evidence that a consequence reminder influences performance or moderates the effect of mindsets in this task. My results contribute to the emerging literature on the benefits of deliberative mindsets and can help guide future research in this area.

In the absence of treatment following strokes, both humans and model organisms demonstrate partial improvements in motor function. Several endogenous mechanisms, such as cortical reorganization, are hypothesized to cause this spontaneous biological recovery. Reorganization of the motor cortex occurs within a time sensitive period and involves both proximal and distal sites of the intact brain. Despite these advancements, whether the same or different cells are used in the reorganized cortex after stroke remains unknown. In order to identify the motor networks involved in recovery, our lab has begun using the inducible Arc-CreERᵀ²:Rosa-YFPᶠᐟᶠ mice. In conjunction with the bimanual string-pulling task, this inducible model allows for the labelling of active cells throughout stroke recovery; either pre, 2 days post-stroke (dps) and 2 weeks post-stroke (wps). Behavioural deficits on the string-pull task were observed at 2 dps and accompanied by a decrease in active cells in the ipsilesional secondary motor (M2) cortex of stroke mice. By 2 wps, stroke mice had partial recovery of motor function with no differences in active cells in the ipsilesional M2. Interestingly, ~40% of cell in the motor cortex of sham and stroke mice were activated more than once while performing the string-pull task until 2 wps. Deeplabcut kinematic analysis of the string-pull task was also unable to identify differences in motor performance between stroke and sham mice. In addition, irrelevant of stroke injuries, only 60% of cells co-expressed the pan-neuronal marker NeuN after surgeries. Together these findings suggest that 40% of cells are reactivated up to 2 weeks post-stroke during the performance of a motor task, despite the acute decreases in active cells in the ipsilesional M2 of stroke mice. DeepLabCut kinematic results also highlight the need to redefine kinematic outcomes to better assess the full spectrum of stroke deficits.

Load imbalance is a major source of performance degradation in parallel scientific applications. Load balancing increases the efficient use of existing resources and improves performance of parallel applications running in distributed environments. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. At a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Algorithmic and systemic load balancing strategies have complementary set of advantages. An integration of these two techniques is possible and it should result in a system, which delivers advantages over each technique used in isolation. This thesis presents a design and implementation of a system that combines an algorithmic fine-grained data parallel load balancing strategy called Fractiling with a systemic coarse-grained task-parallel load balancing system called Hector. It also reports on experimental results of running N-body simulations under this integrated system. The experimental results indicate that a distributed runtime environment, which combines both algorithmic and systemic load balancing strategies, can provide performance advantages with little overhead, underscoring the importance of this approach in large complex scientific applications.

This study examined an existing industrial workstation at an automobile assembly plant using computer aided ergonomics and digital human models. The purpose of this evaluation was the development of a methodology useful for evaluating workstations to identify potential design issues that could result in musculoskeletal injury in a real work environment. An ergonomic risk assessment was conducted on a lifting task while being performed both manually and using an assist device. JACK digital human modeling and ergonomics software were used to conduct a computer-based ergonomic analysis. Four analysis tools in JACK (static strength analysis, rapid upper limb assessment, metabolic energy expenditure analysis and NIOSH lift analysis) were used to evaluate the potential injury risk of the current method of task performance and there is any difference between using and not using the assist device. Muscle activity was measured by electromyography (EMG) to identify physiological indicators of fatigue. Also, Borg¡¯s Rate of Perceived Exertion (RPE) scale was administered to obtain psychophysical data. Results of this study revealed that there were relative stresses on the trunk and arm areas when the task was performed manually. The results also suggest although using the assist device decreased injury risk potentially, use of the assist device had an adverse impact on the productivity of the assembly line. Based on the findings of this study, the methodology used appears to be an appropriate ergonomic analysis tool for assessing and predicting potential risks associated with the design of industrial workstations. Furthermore this methodology can be extended to designing and redesigning industrial workstations.

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