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1. Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

   https://doi.org/10.3390/land9120519  

   Magliocca, Nicholas R. (December 2020, Land)

   null (Ed.)

   The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain. 

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2. Correlations between graduate student writing concepts and processes and certainty of career trajectories

   https://doi.org/10.1109/FIE.2018.8658678  

   Berdanier, Catherine G.P.; Zerbe, Ellen (October 2018, 2018 IEEE Frontiers in Education Conference (FIE))

   At the graduate level, most milestones are based on the ability to write for an academic audience, whether that be for dissertation proposals, publications, or funding opportunities. Writing scholars often discuss the process by which graduate students learn to join their academic “discourse communities” through academic literacies theory. Graduate attrition researchers relate the feeling of belonging with persistence in doctoral programs; however, there has not to date been any research that directly studies engineering writing attitudes and perceptions with student career trajectories, persistence, or attrition. To meet this need, this paper presents research from a larger study analyzing graduate level engineering writing and attrition. The explicit objective of this paper is to present quantitative data relating current graduate engineering students' attitudes, processes, and concepts of academic writing with the certainty of their career trajectory. Five scales measuring aspects of writing were deployed to engineering programs at ten research intensive universities across the United States, with a final total of n=621 graduate student respondents that represent early-career, mid-career, and late-career stages of the graduate timeline. Results indicate that graduate student processes and conceptions of engineering writing correlate with the likelihood of pursuing careers in various engineering sectors after completing their graduate degree programs. 

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3. A Framework of Digital Twins for Modeling Human-Subject Word Formation Experiments

   He, Hao; Liu, Xueying; Kuhlman, Chris J; Deng, Xinwei (December 2024, IEEE)

   Agent-based models (ABMs) are used to simulate human-subject experiments. A comprehensive understanding of these human systems often requires executing large numbers of simulations, but these requirements are constrained by computational and other resources. In this work, we build a framework of digital twins for modeling human-subject experiments. The framework has three modules: ABMs of player behaviors built from game data; extensions of these models to represent virtual assistants (agents that are exogenously manipulated to create controlled environments for human agents); and an uncertainty quantification module composed of functional ANOVA and a Gaussian process-based emulator. The emulator is built from the extended ABM; we focus on emulator validation. By incorporating experimental data and agent-based simulation data, our proposed framework enhances the virtual representation of the dynamics in human-subject word formation experiments, which we consider a digital twin. Networked anagram experiments are used as an exemplar to demonstrate the methods. 

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4. Characterizing Doctoral Engineering Student Socialization: Narratives of Mental Health, Decisions to Persist, and Consideration of Career Trajectories

   https://doi.org/10.1109/FIE43999.2019.9028438  

   Hocker, E.; Zerbe, E.; Berdanier, C.G.P. (October 2019, IEEE Frontiers in Education)

   This research full paper explores interview data with N=36 engineering graduate students to understand the factors and characteristics of graduate socialization, with the effort of better preparing students to succeed in doctoral programs. This research is motivated by the alarming fact that nearly one-third of engineering doctoral students will not finish their PhD programs; however, little research has been conducted on the various factors that can lead to attrition or enhance persistence in graduate engineering programs. This paper presents the results from the interview phase of a larger study investigating doctoral engineering socialization, attrition, persistence, and career trajectories. The participants for this study come from large research-intensive universities across the United States, and were sampled for maximum variation in a number of different categories, including stage in their doctoral program, gender, and race. Upon collecting and analyzing interview data from our participants through constant comparative and content analysis methods, several themes arose including concerns for mental health in engineering graduate students and uncertainties with joining the culture of academia in their future careers. Further, although the participants for this study are currently graduate students who anticipate completing their PhDs, nearly half of the participants discussed strongly considered leaving at some point. This study adds to the body of literature surrounding engineering attrition and the underlying issues driving engineering PhDs away from academic engineering careers. 

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5. Persistence at what cost? How graduate engineering students consider the costs of persistence within attrition considerations

   https://doi.org/10.1002/jee.20528  

   Sallai, Gabriella M.; Bahnson, Matthew; Shanachilubwa, Kanembe; Berdanier, Catherine G. P. (May 2023, Journal of Engineering Education)

   Abstract BackgroundWhile previous work in higher education documents the impact of high tuition costs of attending graduate school as a key motivator in attrition decisions, in engineering, most graduate students are fully funded on research fellowships, indicating there are different issues causing individuals to consider departure. There has been little work characterizing nonfinancial costs for students in engineering graduate programs and the impact these costs may have on persistence or attrition. Purpose/HypothesisFramed through the lens of cost as a component of the expectancy–value theory framework and the graduate attrition decisions (GrAD) model conceptual framework specific to engineering attrition, the purpose of this article is to characterize the costs engineering graduate students associate with attending graduate school and document how costs affect students' decisions to persist or depart. Design/MethodData were collected through semistructured interviews with 42 engineering graduate students from R1 engineering doctoral programs across the United States who have considered, are currently considering, or have chosen to depart from their engineering PhD programs with a master's degree. ResultsIn addition to time and money, which are costs previously captured in research, participants identified costs to life balance, costs to well‐being, and identify‐informed opportunity costs framed in terms of what “could have been” if they had chosen to not go to graduate school. As these costs relate to persistence, students primarily identified their expended effort and already‐incurred costs as the primary motivator for persistence, rather than any expected benefits of a graduate degree. ConclusionThe findings of this work expand the cost component of the GrAD model conceptual framework, providing a deeper understanding of the costs that graduate students relate to their persistence in engineering graduate programs. It evidences that motivation to persist may not be due to particularly strong goals but may result from costs already incurred. Through this research, the scholarly community, students, advisors, and university policymakers can better understand the needs of engineering graduate students as they navigate graduate study. 

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