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An experimental research study using a mixed-method analysis to was conducted to examine educational video game effects on mathematics achievement and motivation between sexes. This study examined sex difference in a 7th grade mathematics (Mathematics 2/Mathematics 2 Advanced) classroom (n=60) learning algebra. Attributes and barriers relating to educational video game play, preference, and setting characteristics were explored. To examine achievement and motivation outcomes, a repeated-measure (SPSS v14) test was used. The analysis included ethnographic results from both student and teacher interview and observation sessions for data triangulation. Results revealed a statistically significant academic mathematics achievement score increase (F =21.8, df =1, 54, < .05). Although, mathematics class motivation scores did not present significance (F =.79, df =1, 47, p > .05), both sexes posted similar data outcomes with regard to mathematics class motivation after using an educational video game as treatment during an eighteen-week term in conjunction with receiving in-class instruction. Additionally, there was an increase in male variability in standard deviation score (SDmotivationpre=8.76, SDmotivation post=11.70) for mathematics class motivation. Lastly, self-reported differences between the sexes for this limited sample, with regard to game design likes and dislikes and observed female game play tendencies, were also investigated. The data presented customization as a unified, but most requested, game design need between the sexes. Between sex differences were found only to be superficial other than a female delay in game acceptance with regard to time and game play comfort.

An approach to aerodynamic configuration optimization is presented for the high-speed civil transport (HSCT). Methods to parameterize the wing shape, fuselage shape and nacelle placement are described. Variable-complexity design strategies are used to combine conceptual and preliminary-level design approaches, both to preserve interdisciplinary design influences and to reduce computational expense. The preliminary-design-level analysis methods used to estimate aircraft performance are described. Conceptual-design-level (approximate) methods are used to estimate aircraft weight, supersonic wave drag and drag due to lift, and landing angle of attack. The methodology is applied to the minimization of the gross weight of an HSCT that flies at Mach 2.4 with a range of 5500 n.mi. Results are presented for wing plan form shape optimization and for combined wing and fuselage optimization with nacelle placement. Case studies include both all-metal wings and advanced composite wings. The results indicate the beneficial effect of simultaneous design of an entire configuration over the optimization of the wing alone and illustrate the capability of the optimization procedure. / Ph. D.

The workflow and process of graphic design is today streamlined by the usage of our tools such as softwares. However, this doesn’t mean that designers must restrict themselves from working outside an established practice largely determined by such software. Implementing programming and generative design as a method in the development of graphic design can lead to new insights and different perspectives. Proven to be an asset in the creation of visual material the method falls short as generative design is co-dependent on both the designer and user. Though complicated and expensive, generative design proves useful when working with realtime data and as a tool for generating new ideas. Working experimentally with under-utilized tools can help the development of new trends and methods within the field of graphic design.

The present study provides an example of implementing the difference in differences (DD) estimator for a two-group, pretest-posttest design with K-12 educational intervention data. The goal is to explore the basis for causal inference via Rubin's potential outcomes framework. The DD method is introduced to educational researchers, as it is seldom implemented in educational research. DD analytic methods' mathematical formulae and assumptions are explored to understand the opportunity and the challenges of using the DD estimator for causal inference in educational research. For this example, the teacher intervention effect is estimated with multi-cohort student outcome data. First, the DD method is used to detect the average treatment effect (ATE) with linear regression as a baseline model. Second, the analysis is repeated using linear regression with cluster robust standard errors. Finally, a linear mixed effects analysis is provided with a random intercept model. Resulting standard errors, parameter estimates, and inferential statistics are compared among these three analyses to explore the best holistic analytic method for this context.

Nonsuicidal self-injury (NSSI; i.e., the deliberate destruction of one’s own bodily tissue without suicidal intent and for reasons not socially sanctioned) is prevalent and associated with clinically serious consequences. There is a need for evidence-based, stand-alone treatments for this behavior as it presents across the full range of psychiatric disorders. Developing time-efficient and cost-effective interventions for NSSI has proven difficult given that the core components of treatment remain largely unknown. The aim of this study was to examine the specific effects on NSSI of mindful emotion awareness training and cognitive reappraisal, two transdiagnostic treatment strategies that directly address the functional processes that often maintain self-injury (i.e., relief or escape from aversive thoughts or feelings). Using a counterbalanced, combined series (multiple baseline and phase change) single-case experimental design, the unique and combined impact of these two four-week interventions was evaluated among diagnostically heterogeneous, self-injuring adults (N = 10; mean age = 21.3, range = 18 to 30 years). Hypotheses were that each intervention would produce clinically meaningful reductions in NSSI; adding the alternative intervention would have additive benefit for those who did not respond to the initial intervention alone; and reductions in NSSI would be maintained over a four-week follow-up phase. Results showed that 8 of 10 participants demonstrated clinically meaningful reductions in NSSI by the follow-up phase; six participants responded to one intervention alone, whereas adding the alternative intervention was associated with additive benefit for two participants. Group-based analyses indicated a statistically significant effect of study phase on NSSI (p < .001), with fewer NSSI urges and acts occurring after the interventions were introduced. The interventions were also associated with moderate to large reductions in anxiety (d = 0.89 – 1.09), depression (d = 0.79 – 1.09), and interference caused by symptoms (d = 0.61), and with improvements in skills-based mechanisms: mindful emotion awareness (d = 1.44) and reappraisal (d = 1.30). The results suggest that increasing mindful emotion awareness and cognitive reappraisal may be two key therapeutic strategies for reducing NSSI. Transdiagnostic, emotion-focused interventions delivered in time-limited formats can serve as practical yet powerful treatment approaches, especially for lower-risk self-injuring individuals.

In data science, machine learning methods, such as deep learning and other AI algorithms, have been widely used in many applications. These machine learning methods often have complicated model structures with a large number of model parameters and a set of hyperparameters. Moreover, these machine learning methods are data-driven in nature. Thus, it is not easy to provide a comprehensive evaluation on the performance of these machine learning methods with respect to the data quality and hyper-parameters of the algorithms. In the statistical literature, design of experiments (DoE) is a set of systematical methods to effectively investigate the effects of input factors for the complex systems. There are few works focusing on the use of DoE methodology for evaluating the quality assurance of AI algorithms, while an AI algorithm is naturally a complex system. An understanding of the quality of Artificial Intelligence (AI) algorithms is important for confidently deploying them in real applications such as cybersecurity, healthcare, and autonomous driving. In this proposal, I aim to develop a set of novel methods on the interface between experimental design and machine learning, providing a systematical framework of using DoE methodology for AI algorithms. This proposal contains six chapters. Chapter 1 provides a general introduction of design of experiments, machine learning, and surrogate modeling. Chapter 2 focuses on investigating the robustness of AI classification algorithms by conducting a comprehensive set of mixture experiments. Chapter 3 proposes a so-called Do-AIQ framework of using DoE for evaluating the AI algorithm’s quality assurance. I establish a design-of-experiment framework to construct an efficient space-filling design in a high-dimensional constraint space and develop an effective surrogate model using additive Gaussian process to enable the quality assessment of AI algorithms. Chapter 4 introduces a framework to generate continual learning (CL) datsets for cybersecurity applications. Chapter 5 presents a variable selection method under cumulative exposure model for time-to-event data with time-varying covariates. Chapter 6 provides the summary of the entire dissertation. / Doctor of Philosophy / Artificial intelligence (AI) techniques, including machine learning and deep learning algorithms, are widely used in various applications in the era of big data. While these algorithms have impressed the public with their remarkable performance, their underlying mechanisms are often highly complex and difficult to interpret. As a result, it becomes challenging to comprehensively evaluate the overall performance and quality of these algorithms. The Design of Experiments (DoE) offers a valuable set of tools for studying and understanding the underlying mechanisms of complex systems, thereby facilitating improvements. DoE has been successfully applied in diverse areas such as manufacturing, agriculture, and healthcare. The use of DoE has played a crucial role in enhancing processes and ensuring high quality. However, there are few works focusing on the use of DoE methodology for evaluating the quality assurance of AI algorithms, where an AI algorithm can be naturally considered as a complex system. This dissertation aims to develop innovative methodologies on the interface between experimental design and machine learning. The research conducted in this dissertation can serve as practical tools to use DoE methodology in the context of AI algorithms.

As frequently as it occurs before elections that politicians give a united image of their potential coalition, previous studies have manly focused on explaining why coalitions of parties come to existence in the first place. However, previous scholars have not studied whether the demonstration of unity within a political bloc before an election affects the voters. The purpose of this study is to examine this aspect of politics through the following hypotheses:  H0: The demonstration of unity within a bloc before an election has no influence on how voters vote. H1: The demonstration of unity within a bloc before an election increases voters' willingness to vote for the most united bloc. The hypotheses were examined through quasi-experimental design and the study was conducted on university students. The result indicates that the demonstration of unity in a political bloc before an election has no significant effect on voting behavior, thus confirming the H0. However, further studies are suggested before confirming the result.

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