Learning design competency frameworks published by professional organizations, exist for typical instructional design efforts. However, a review of literature revealed a lack of frameworks available for the creation of complex learning designs (CLDs). The goal of this research was to develop a competency framework for the creation of CLDs. Quantitative and qualitative methods were employed in the four phases of the design and development research approach In phase one, a survey based on the Educational Technology Multimedia Competency Survey (ETMCS) was sent to instructional designers who self-reported as having experience creating CLDs. The purpose of phase one was to identify competencies that instructional designers felt were most important to the creation of complex, technology-mediated learning designs. The preliminary CLD framework was constructed during phase two, based on analysis of the ETMCS survey results. Measures of central tendency were used to identify competencies considered essential and desirable. Additionally, competencies were categorized into seven domains In phase three, semi-structured interviews were conducted with a subset of survey participants. The purpose was to gain deeper insight into the participant’s perception of the design complexities involved with each of the competencies included in the preliminary framework. In phase four, the preliminary framework was internally validated using an expert panel employing the Delphi method to build consensus. Three rounds were required to achieve consensus on all competencies within the framework. This consensus resulted in 79 competencies including 30 essential and 49 desirable competencies from the set identified as the preliminary framework during phase two. Several conclusions emerged from the creation of this framework. Though technology is often a trigger for many types of CLDs, specific technologies are certainly desirable, but not essential. The research also revealed that communication and collaboration competencies are almost universally essential due to the complexity of the designs which typically necessitates the formation of multi-discipline teams. Without these competencies, the team’s cross-profession effectiveness is often hindered due to differences in terminology, processes, and team member geographic location.

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<p>Domain adaptation is one of the hottest directions in solving annotation insufficiency problem of deep learning. General domain adaptation is not consistent with the practical scenarios in the industry. In this thesis, we focus on two concerns as below.</p> <p>  </p> <p>  First is that labeled data are generally collected from multiple domains. In other words, multi-source adaptation is a more common situation. Simply extending these single-source approaches to the multi-source cases could cause sub-optimal inference, so specialized multi-source adaptation methods are essential. The main challenge in the multi-source scenario is a more complex divergence situation. Not only the divergence between target and each source plays a role, but the divergences among distinct sources matter as well. However, the significance of maintaining consistency among multiple sources didn't gain enough attention in previous work. In this thesis, we propose an Enhanced Consistency Multi-Source Adaptation (EC-MSA) framework to address it from three perspectives. First, we mitigate feature-level discrepancy by cross-domain conditional alignment, narrowing the divergence between each source and target domain class-wisely. Second, we enhance multi-source consistency via dual mix-up, diminishing the disagreements among different sources. Third, we deploy a target distilling mechanism to handle the uncertainty of target prediction, aiming to provide high-quality pseudo-labeled target samples to benefit the previous two aspects. Extensive experiments are conducted on several common benchmark datasets and demonstrate that our model outperforms the state-of-the-art methods.</p> <p>  </p> <p>  Second is that data privacy and security is necessary in practice. That is, we hope to keep the raw data stored locally while can still obtain a satisfied model. In such a case, the risk of data leakage greatly decreases. Therefore, it is natural for us to combine the federated learning paradigm with domain adaptation. Under the source-private setting, the main challenge for us is to expose information from the source domain to the target domain while make sure that the communication process is safe enough. In this thesis, we propose a method named Fourier Transform-Assisted Federated Domain Adaptation (FTA-FDA) to alleviate the difficulties in two ways. We apply Fast Fourier Transform to the raw data and transfer only the amplitude spectra during the communication. Then frequency space interpolations between these two domains are conducted, minimizing the discrepancies while ensuring the contact of them and keeping raw data safe. What's more, we make prototype alignments by using the model weights together with target features, trying to reduce the discrepancy in the class level. Experiments on Office-31 demonstrate the effectiveness and competitiveness of our approach, and further analyses prove that our algorithm can help protect privacy and security.</p>

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Athletics has been regarded as a means of encouraging youth to build character, discipline, and develop healthy habits. However, literature has emerged that asserts athletics do not prevent deviant behaviors, but instead, influence one to commit deviant acts. As such, this research examined effects of athletics on the commission of deviant behaviors via learning and techniques of neutralization theories. Subjects for this project included 325 college students from a southern regional university. Data were generated through the use of a self-report questionnaire, which measured variables pertaining to self-reported deviant behaviors including perceptions of peer deviance, neutralizing indicators, and sports participation. The findings suggest some support for each theoretical model, differential association and techniques of neutralization. Both theoretical models were supported, in general, with learning theory having the most support. When participation in sporting activity was considered, however, the results consistently showed no effect on various types of self-reported deviant behavior.

This study explored the psychological and sociological motivations of adult female and male obstacle course racers. A qualitative case study approach was used to explore the views, experiences, and motivations of obstacle course racing (OCR) participants. Descriptive statistics and cross tabulation was used to interpret responses to the 297 online questionnaires. A content analysis approach was used to analyze the qualitative data gathered from three focus groups with a total of 20 obstacle course racers. Three theories formed the basis of the study: Self-Determination Theory (SDT), Achievement Goal Theory (AGT), and Social Leaning Theory (SLT). Overall, findings supported previous research regarding motivations to participate in adventure racing and extreme sports. Individuals were guided more by intrinsic motives than extrinsic motives. Important motivations for obstacle course racers included the camaraderie among participants, connecting and socializing with other like-minded people, having fun, and having a physical challenge that allowed them to progress and keep on track with their health goals. Obstacle course racing was perceived as positively impacting participants’ health, mental wellness and their confidence in their physical abilities as well as in other areas of their lives. Findings from this study may inform future interventions to increase participation in OCR or to increase overall physical activity among adults by building on camaraderie, social connection, enjoyment, and self-efficacy.

The current study sought to explore the effects of eating behaviors on mental health and the internalization of attitudes toward one's appearance. This was performed through a survey distributed through a global online market research firm, Dynata, and gathered 495 eligible participants. Of those, 78 represented the plant-based group, and 417 represented the non-plant-based group. Each completed the survey containing sections with the Mizes Anorectic Cognitions Scale (MACS) to assess if they were at low or high risk for having/developing an eating disorder, a section determining if participants were plant-based (defined as a regimen that encourages whole, plant-based foods and discourages meats, dairy products, and eggs as well as all refined and processed foods (Tuso et al., 2013)) or not, and asking what their perceived benefits were from their plant-based (or lack of plant-based) diet, a section on the Sociocultural Attitudes Towards Appearance (SATAQ-4), and finally, the Depression, Anxiety, and Stress Scale (DASS). The status of participants being plant-based or non-plant-based was analyzed as this study sought to explore past research that found plant-based diets to be physically and mentally beneficial (Beezhold et al., 2014; Benefits of Plant-Based Diets, 2021; Daneshzad et al., 2019). A series of statistical tests were conducted on SPSS 28 to analyze which groups (high risk for E.D. and plant-based, high risk for E.D. and non-plant-based, low risk for E.D. and plant-based, or low risk for E.D. and non-plant-based) were statistically significant compared to one another. The findings revealed the plant-based group to contain higher percentage of high risk for eating disorder participants. The plant-based group, regardless of high risk, was negatively associated with higher levels of scores on four out of the five sections including internalization of attitudes towards appearance, weight-based pressures from family, weight-based pressures from peers, and higher levels of reported depression, anxiety, and stress. Pressures felt from the media did not display a statistically significant level of difference between any of the high/low risk and plant-based to high/low risk and non-plant-based. The results were interpreted using social learning theory, which proposes that humans have evolved an advanced capacity for observational learning, enabling them to acquire knowledge, attitudes, values, emotional proclivities, and competencies through information conveyed by a rich variety of actual and symbolic models (Bandura, 2002). This allowed for cause and effects to be hypothesized for why the plant-based group was negatively associated with worse mental health and internalization of attitudes towards appearance. Among these hypothesized causes included participants adopting a plant-based diet due to its growing social media popularity, users learning from observation and leading to aquired knowledge, attitudes, values, and beliefs on the diet. Furthermore, those with an obsession of clean-eating could have led many high-risk for E.D. participants to fulfill their internalized thin-ideal and pressure from family and peers regarding appearance through this popular diet they have observed through social media, which would be consistent with previous studies (Holmgren, 2017; Stewart & Ogden, 2020). Limitations include the small sample size of plant-based dieters without equal representation of low to high risk for E.D.s, along with the limitation on not knowing the reasoning why each participant is plant-based (ethical, environmental, health, diet purposes, or due to social learning and popularity) nor for how long they have adhered to this lifestyle. Future research should expand this study to more locations, analyze for differences based on age groups, and build upon the current study to allow for more generalizability.