Matching Items (12)
Filtering by
- Resource Type: Text
Description
Undeclared undergraduates participated in an experimental study designed to explore the impact of an Internet-delivered "growth mindset" training on indicators of women's engagement in science, engineering, technology, and mathematics ("STEM") disciplines. This intervention was hypothesized to increase STEM self-efficacy and intentions to pursue STEM by strengthening beliefs in intelligence as malleable ("IQ attitude") and discrediting gender-math stereotypes (strengthening "stereotype disbelief"). Hypothesized relationships between these outcome variables were specified in a path model. The intervention was also hypothesized to bolster academic achievement. Participants consisted of 298 women and 191 men, the majority of whom were self-identified as White (62%) and 18 years old (85%) at the time of the study. Comparison group participants received training on persuasive writing styles and control group participants received no training. Participants were randomly assigned to treatment, comparison, or control groups. At posttest, treatment group scores on measures of IQ attitude, stereotype disbelief, and academic achievement were highest; the effects of group condition on these three outcomes were statistically significant as assessed by analysis of variance. Results of pairwise comparisons indicated that treatment group IQ attitude scores were significantly higher than the average IQ attitude scores of both comparison and control groups. Treatment group scores on stereotype disbelief were significantly higher than those of the comparison group but not those of the control group. GPAs of treatment group participants were significantly higher than those of control group participants but not those of comparison group participants. The effects of group condition on STEM self-efficacy or intentions to pursue STEM were not significant. Results of path analysis indicated that the hypothesized model of the relationships between variables fit to an acceptable degree. However, a model with gender-specific paths from IQ attitude and stereotype disbelief to STEM self-efficacy was found to be superior to the hypothesized model. IQ attitude and stereotype disbelief were positively related; IQ attitude was positively related to men's STEM self-efficacy; stereotype disbelief was positively related to women's STEM self-efficacy, and STEM self-efficacy was positively related to intentions to pursue STEM. Implications and study limitations are discussed, and directions for future research are proposed.
ContributorsFabert, Natalie Shay (Author) / Bernstein, Bianca L. (Thesis advisor) / Kinnier, Richard (Committee member) / Dawes, Mary (Committee member) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2014
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014
Description
The American Heart Association (AHA) estimates that there are approximately 200,000 in-hospital cardiac arrests (IHCA) annually with low rates of survival to discharge at about 22%. Training programs for cardiac arrest teams, also termed code teams, have been recommended by the Institute of Medicine (IOM) and in the AHA's consensus statement to help improve these dismal survival rates. Historically, training programs in the medical field are procedural in nature and done at the individual level, despite the fact that healthcare providers frequently work in teams. The rigidity of procedural training can cause habituation and lead to poor team performance if the situation does not match the original training circumstances. Despite the need for team training, factors such as logistics, time, personnel coordination, and financial constraints often hinder resuscitation team training. This research was a three-step process of: 1) development of a metric specific for the evaluation of code team performance, 2) development of a communication model that targeted communication and leadership during a code blue resuscitation, and 3) training and evaluation of the code team leader using the communication model. This research forms a basis to accomplish a broad vision of improving outcomes of IHCA events by applying conceptual and methodological strategies learned from collaborative and inter-disciplinary science of teams.
ContributorsHinski, Sandra T. (Author) / Cooke, Nancy J. (Thesis advisor) / Roscoe, Rod (Committee member) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2017
Description
This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions.
ContributorsDemir, Mustafa, Ph.D (Author) / Cooke, Nancy J. (Thesis advisor) / Bekki, Jennifer (Committee member) / Amazeen, Polemnia G (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
Although knowledge about effective teaching and learning exists, and theories of change strategies are considered, the lack of the understanding of the behavior of engineering faculty during curricular change remains a major contributor against robust efforts for change. In this work, faculty adaptability is conceptualized as self-regulation during curricular change. Faculty participants were recruited from two divergent curricular change contexts: one that is prescribed with interdependence while the other is emergent with uncertainty. In this study, attitude toward context’s strength is conceptualized along the four dimensions of clarity, consistency, constraints, and consequences of the context, while faculty’s self-efficacy and willingness for adaptability are conceptualized along the three dimensions of planning, reflecting, and adjusting. This study uses a mixed method, quantitative-qualitative, sequential explanatory research design. The quantitative phase addresses the question of “How does faculty group in the first context differ from faculty group in the second context in terms of self-efficacy and willingness for planning, adjusting, and reflecting?” The qualitative phase addresses the question of “How do faculty respond to curricular change, as exhibited in their activities of planning, adjusting, and reflecting during change?” Findings point to differences in patterns of correlations between attitude toward context with both self-efficacy and willingness across the two contexts, even though analysis showed no significant differences between attitude toward context, self-efficacy, and willingness across the two contexts. Moreover, faculty participants’ willingness for adjusting, in both contexts, was not correlated with neither attitude toward context’s clarity nor constraints. Furthermore, in the prescribed context, Group A faculty (self-identified as Lecturers, Senior Lecturers, or Adjunct Faculty) showed higher willingness for planning, adjusting, and reflecting activities, compared to Group B faculty (self-identified as Assistant, Associate or Full Professors). Also, in the prescribed context, Group A faculty showed no overall significant correlation with attitude toward context. This study has implications on the way change is conceived of, designed, and implemented, when special attention is given to faculty as key change agents. Without the comprehensive understanding of the adaptability of faculty as key change agents in the educational system, the effective enacting of curricular change initiatives will remain unfulfilled.
ContributorsAli, Hadi (Author) / McKenna, Ann (Thesis advisor) / Bekki, Jennifer (Committee member) / Roscoe, Rod (Committee member) / Arizona State University (Publisher)
Created2021
Description
For decades, engineering scholarship has presented data to address the underrepresentation of Black womxn in the engineering doctoral community. American Society of Engineering Education (ASEE)’s Engineering by the Numbers Report (2021) statistically showed that only 57 Black womxn out of 10,037 scholars received engineering doctorates in 2021. Engineering scholars have theorized about constructs ranging from whiteness to explain the system, to doctoral socialization to explain the culture, to retention explain the experiences. Yet, even with the plethora of scholarship, the problem of underrepresentation has remained consistent with limited action towards change from the faculty, the program, or the institution. Therefore, I aim to address this problem by cultivating emotional resonance toward action within the engineering community regarding engineering doctoral program underrepresentation for Black womxn. Using Arts-Based Research (ABR) and Black Feminist Thought (BFT), this dissertation illustrates the engineering PhD spirit-murdering experiences of Black womxn. Six Homegirls intellectually contributed to this study by sharing their time and experiences through artistic expressions and homegirl conversations. Through the lens of BFT’s matrix of domination, the composite blog shows that spirit-murdering for these Homegirls: 1) is a targeted act that is dehumanizing 2) occurs because of the aloof nature and capitalist ideals of the engineering academy, and 3) causes further conflict in negotiating identities as Black, woman, professional, researcher, and student. Leaning on BFT’s grounding as an Afrocentric methodological approach, the composite poem illustrates that these Homegirls: 1) have a common, understood epistemology because of their shared experiences of being Black and woman in their current, multi-layered social locations, 2) identify strongly with their positionality and values while describing their outsider-within status, and 3) experience spirit-murdering in an emotional, intellectual, and spiritual way that then results in physical manifestations. Rooted in BFT’s ethic of caring, the hip-hop mixtape’s progression describes homegirl’s spirit-renewal tactics as: 1) owning their professional identity, 2) dispelling projected biases, stereotypes, and aggressions, 3) calling out inequities in their interpersonal relationships and program culture, 4) learning to set boundaries to protect themselves, and 5) standing on their ways of knowing and being.
ContributorsNicole, Fantasi (Author) / Coley, Brooke C. (Thesis advisor) / Bekki, Jennifer (Committee member) / Holly, Jr., James (Committee member) / Arizona State University (Publisher)
Created2023
Description
Despite an increasing number of women completing doctoral programs each year, there remains a deficit in the representation of women in the fields of science, technology, engineering, and mathematics (STEM). Moreover, Women of Color (WoC) in STEM doctoral programs face unique stressors and barriers that their male colleagues may not, such as microaggressions, racism, and sexism due to their marginalized identities. Although there is a growing body of research focusing on how WoC in STEM successfully navigate academic challenges and symptoms of distress present in rigorous doctoral training, there is limited research examining intrapersonal (i.e., academic resilience) and interpersonal (i.e., perceived social support) factors that may mitigate the negative associations of these experiences. To address these gaps in the literature, the present study gathered data from 174 WoC during December 2021 and January 2022. Results supported the hypothesis that academic challenges would be positively associated with symptoms of distress. Furthermore, based on research showing positive associations between both academic resilience and social support and psychological well-being, the study also examined whether academic resilience and perceived social support from friends and romantic partners. While academic resilience was not found to moderate the association between academic challenges and symptoms of distress, perceived social support was found to have moderating effects, such that low and high levels of perceived social support increased the strength of the positive association between academic challenges and symptoms of distress. Results of the present study have implications for doctoral programs and mental health practitioners working at university college centers and support for need for the enhancement of the structure and process of the doctoral program experience for WoC in STEM.
ContributorsHopkins, Jessica (Author) / Randall, Ashley K (Thesis advisor) / Bekki, Jennifer (Committee member) / Truong, Nancy (Committee member) / Arizona State University (Publisher)
Created2022
Description
Significant efforts are underway by engineering organizations to diversify their workforce. However, research findings on workplace diversity are mixed, with insufficient clarity into what makes heterogeneous work environments successful. Acknowledging the role of individual behavior in building diverse workplaces that are cohesive and productive, researchers have called for more theory-based investigation into individuals’ workplace behaviors and their determinants. This three-part study bridges the gap within an engineering context by leveraging Berry's acculturation framework (Berry, 1972, 2005) from cross-cultural psychology to examine the factors influencing engineers’ acculturation behaviors in workplaces. Acculturation refers to the process by which individuals adjust to people different from themselves in their daily interactions (Berry, 1972, 2005). Based on Berry’s framework, the study postulates four acculturation attitudes and behaviors for engineers—Integration, Assimilation, Separation, and Marginalization. Acculturation attitudes are based on acculturation preferences, such as the importance an individual places on retaining individual values and the importance an individual places on receiving acceptance from coworkers. These acculturation attitudes and perceived acceptance together influence behaviors. The first study designed and validated an instrument to measure engineers’ acculturation preferences, acculturation behaviors, and perceived acceptance from coworkers. The results provided complete to partial support for the expected correlations among factors. The second study conducted cluster and Chi-square analyses focusing on the two acculturation preferences. The results revealed four clusters corresponding to Berry’s acculturation attitudes and revealed findings, such as women’s preference for Integration attitude over men’s. The third study used a path model to gain insight into gender differences in engineers’ acculturation behaviors and revealed acceptance as a crucial factor. The results quantitatively substantiated prior findings, namely, that women engineers prefer Assimilation and Separation behaviors more and Integration behavior less mediated by factors related to acceptance.
The developed instrument and study findings offer researchers another lens to study organizations’ diversification efforts, along with other personal and contextual factors. The study findings could also help engineering organizations recruit employees with acculturation attitudes favorable to the organization’s diversity goals and design trainings that highlight the importance of coworker acceptance. Such interventions would facilitate the creation of more diverse work environments.
ContributorsAbhyankar, Rohini N. (Author) / Brunhaver, Samantha R (Thesis advisor) / Bekki, Jennifer (Committee member) / Jordan, Shawn (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head pose and affective state for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s non-verbal cues indicated that time delay, the variation of a student’s head pose relative to the computer screen and confusion had significantly statistical relation to cheating behaviors. Additionally, time delay, head pose relative to the computer screen, confusion, and the interaction term of confusion and time delay were predictors in a support vector machine of cheating prediction with an average accuracy of 70.7%. The current algorithm could automatically flag suspicious student behavior for proctors in large scale online courses during remotely administered exams.
ContributorsChuang, Chia-Yuan (Author) / Femiani, John C. (Thesis advisor) / Craig, Scotty D. (Thesis advisor) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
Description
Over the past century, the world has become increasingly more complex. Modern systems (i.e blockchain, internet of things (IoT), and global supply chains) are inherently difficult to comprehend due to their high degree of connectivity. Understanding the nature of complex systems becomes an acutely more critical skill set for managing socio-technical infrastructure systems. As existing education programs and technical analysis approaches fail to teach and describe modern complexities, resulting consequences have direct impacts on real-world systems. Complex systems are characterized by exhibiting nonlinearity, interdependencies, feedback loops, and stochasticity. Since these four traits are counterintuitive, those responsible for managing complex systems may struggle in identifying these underlying relationships and decision-makers may fail to account for their implications or consequences when deliberating systematic policies or interventions.
This dissertation details the findings of a three-part study on applying complex systems modeling techniques to exemplar socio-technical infrastructure systems. In the research articles discussed hereafter, various modeling techniques are contrasted in their capacity for simulating and analyzing complex, adaptive systems. This research demonstrates the empirical value of a complex system approach as twofold: (i) the technique explains systems interactions which are often neglected or ignored and (ii) its application has the capacity for teaching systems thinking principles. These outcomes serve decision-makers by providing them with further empirical analysis and granting them a more complete understanding on which to base their decisions.
The first article examines modeling techniques, and their unique aptitudes are compared against the characteristics of complex systems to establish which methods are most qualified for complex systems analysis. Outlined in the second article is a proof of concept piece on using an interactive simulation of the Los Angeles water distribution system to teach complex systems thinking skills for the improved management of socio-technical infrastructure systems. Lastly, the third article demonstrates the empirical value of this complex systems approach for analyzing infrastructure systems through the construction of a systems dynamics model of the Arizona educational-workforce system, across years 1990 to 2040. The model explores a series of dynamic hypotheses and allows stakeholders to compare policy interventions for improving educational and economic outcome measures.
This dissertation details the findings of a three-part study on applying complex systems modeling techniques to exemplar socio-technical infrastructure systems. In the research articles discussed hereafter, various modeling techniques are contrasted in their capacity for simulating and analyzing complex, adaptive systems. This research demonstrates the empirical value of a complex system approach as twofold: (i) the technique explains systems interactions which are often neglected or ignored and (ii) its application has the capacity for teaching systems thinking principles. These outcomes serve decision-makers by providing them with further empirical analysis and granting them a more complete understanding on which to base their decisions.
The first article examines modeling techniques, and their unique aptitudes are compared against the characteristics of complex systems to establish which methods are most qualified for complex systems analysis. Outlined in the second article is a proof of concept piece on using an interactive simulation of the Los Angeles water distribution system to teach complex systems thinking skills for the improved management of socio-technical infrastructure systems. Lastly, the third article demonstrates the empirical value of this complex systems approach for analyzing infrastructure systems through the construction of a systems dynamics model of the Arizona educational-workforce system, across years 1990 to 2040. The model explores a series of dynamic hypotheses and allows stakeholders to compare policy interventions for improving educational and economic outcome measures.
ContributorsNaufel, Lauren Rae McBurnett (Author) / Bekki, Jennifer (Thesis advisor) / Kellam, Nadia (Thesis advisor) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020