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- Creators: Bekki, Jennifer
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
Description
A defining feature of many United States (U.S.) doctoral engineering programs is their large proportion of international students. Despite the large student body and the significant impacts that they bring to the U.S. education and economy, a scarcity of research on engineering doctoral students has taken into consideration the existence of international students and the consequential diversity in citizenship among all students. This study was designed to bridge the research gap to improve the understanding of sense of belonging from the perspective of international engineering doctoral students.
A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.
The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.
A multi-phase mixed methods research approach was taken for this study. The qualitative strand focused on international engineering doctoral students’ sense of belonging and its constructs. Semi-structured interview data were collected from eight international students enrolled at engineering doctoral programs at four different institutions. Thematic analysis and further literature review produced a conceptual structure of sense of belonging among international engineering doctoral students: authentic-self, problem behavior, academic self-efficacy, academic belonging, sociocultural belonging, and perceived institutional support.
The quantitative strand of this study broadened the study’s population to all engineering doctoral students, including domestic students, and conducted comparative analyses between international and domestic student groups. An instrument to measure the Engineering Doctoral Students’ Quality of Interaction (EDQI instrument) was developed while considering the multicultural nature of interactions and the discipline-specific characteristics of engineering doctoral programs. Survey data were collected from 653 engineering doctoral students (383 domestic and 270 international) at 36 R1 institutions across the U.S. Exploratory Factor Analysis results confirmed the construct validity and reliability of the data collected from the instrument and indicated the factor structures for the students’ perceived quality interactions among domestic and international student groups. A set of separate regression analyses results indicated the significance of having meaningful interactions to students’ sense of belonging and identified the groups of people who make significant impacts on students’ sense of belonging for each subgroup. The emergent findings provide an understanding of the similarities and differences in the contributors of sense of belonging between international and domestic students, which can be used to develop tailored support structures for specific student groups.
ContributorsLee, Eunsil (Author) / Bekki, Jennifer (Thesis advisor) / Carberry, Adam (Thesis advisor) / Kellam, Nadia (Committee member) / Arizona State University (Publisher)
Created2020