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This paper presents progress and insights from the NSF-funded “Transforming STEM Education using an Asset-Based Ecosystem Model” (Eco-STEM) project at California State University Los Angeles, a minority-serving institution where over 70% of students are Hispanic/Latiné, Pell-eligible, and first-generation. Historically, the College of Engineering, Computer Science, and Technology (ECST) has implemented various intervention programs - preparatory courses, cohorting, tutoring, workshops, and peer-mentoring - to support students from their transition to college through graduation. While these efforts have led to incremental improvements, they have not delivered the transformative outcomes we envisioned. A key realization from these interventions is the need for a new approach that meets our students “where they are”. This prompted a shift from operating through the lenses of a rigid, "factory model" of education—which assumes uniformity in student input and output—to an adaptable ecosystem framework that leverages its agents' assets and community cultural wealth. The Eco-STEM project focuses on developing structures and tools to allow the current system, constrained by factory-like processes, to evolve into an asset-based ecosystem that better serves the diverse needs of its agents—students, faculty, and staff. Key initiatives include: (1) the Faculty Fellow Community of Practice, a year-long cohort engaging in discussions on topics such as identity, teacher identity, and cultural wealth, culminating in Action Research Teaching (ART) projects; (2) the Lecturer Faculty Workshops, providing condensed versions of the Faculty Fellow Community of Practice experience; (3) the Educational Ecosystem Health Survey, which uses validated constructs to assess the well-being of the system's members; (4) a new Peer Observation Tool and Process focused on formative, growth-oriented feedback for faculty; (5) a new Student Opinion Survey designed around the ecosystem model, examining classroom climate, structure, and vibrancy; and (6) the Mental Model Survey, which assesses faculty perspectives on academia through the lens of ecosystem and factory educational paradigms. This paper briefly discusses the tools and strategies developed, lessons learned through implementation, and team member reflections on how creating educational spaces that value and adapt to the unique strengths of students, faculty, and staff can lead to thriving outcomes for all. 

This Research paper describes the development of the Eco-STEM Student Opinion Survey as a tool designed to aid in the development of a healthy STEM educational ecosystem for students, faculty, and staff at a majority-minority Hispanic-Serving Institution. An important aspect of this endeavor is to obtain meaningful feedback from students about their experiences in STEM classrooms. However, current institutional student opinion surveys lack important context instructors require to make decisions as they intentionally construct inclusive classroom spaces. The Eco-STEM project is developing a student opinion survey and process designed to provide meaningful feedback to instructors. Climate, structure, and vibrancy, three aspects that are critical to evaluating the health of any healthy educational ecosystem, were used to develop the survey. This work is situated in the engineering education community’s effort to create more inclusive classroom environments. The Eco-STEM Student Opinion Survey contains three component parts: a Demographic Survey, a Values Survey, and an Experiences Survey. The Demographic Survey includes items previously shown by the Eco-STEM project to have significant impacts on perceptions of ecosystem health for our students, such as race/ethnicity, gender, living situation, and household income level. The Demographic Survey will be administered to students in their first semester, and participants will be provided with their previous responses each semester and given the opportunity to update them. The Values Survey has been developed based on the Eco-STEM project conceptualization of a healthy educational ecosystem, one that focuses on classroom climate, structure, and vibrancy. The Values Survey measures students’ views on the importance of each aspect. Like the Demographic Survey, it will be administered to students in their first semester and then updated each semester as desired. Instructors will receive reports on their students’ responses to the Demographics and Values Survey at the beginning of each semester, which will provide them with a basis for intentional decision-making and the establishment of an inclusive classroom space. Finally, at the end of each semester, students will be asked to respond to the Experience Survey for each course in which they were enrolled. This survey is also structured around the proposed constructs of climate, structure, and vibrancy. Reports provided to instructors on each of their classes at the end of the semester will provide useful feedback on which to reflect and design intentional changes for future courses. In this paper, we describe the development of the three component parts of the Eco-STEM Student Opinion Survey as well as the proposed process of implementation. We also present the results of confirmatory factor analyses on a pilot study of the Values and Experiences Surveys, which measures the construct reliability for the proposed constructs of climate, structure, and vibrancy. Evidence of validity will enable the institutionalization of a new process that is centered around the voices of our students and supports the evolution of an educational ecosystem in which all can thrive. 

Ethics education and societal understandings are critical to an education in engineering. However, researchers have found that students do not always see ethics as a part of engineering. In this paper, we present a sociotechnical approach to teaching ethics around the topic of surveillance technology in an interdisciplinary, co-designed and co-taught course. We describe and reflect on our curricular and pedagogical approach that uplifts cross-disciplinary dialogue, social theoretical frameworks to guide ethical thinking, and highlighting collective action and resistance in our course content and praxis to inspire students. Through a reflexive thematic analysis of student reflection writing, we examine the ways students relate society and technology, generate ethical skills and questions, and are motivated to act. We find that, in fact, this approach resonates with student experience and desire for discipline-specific ethical analysis, and is highly motivating. 

Abstract The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allowing individual, biased media consumers to expose themselves only to what already confirms their beliefs, leading to polarized echo-chambers that further deepen polarization. This work introduces extensions to the recent Cognitive Cascades model of Rabb et al. to study this dynamic, allowing for simulation of information spread between media and networks of variably biased citizens. Our results partially confirm the above polarization logic, but also reveal several important enabling conditions for polarization to occur: (1) the distribution of media belief must be more polarized than the population; (2) the population must be at least somewhat persuadable to changing their belief according to new messages they hear; and finally, (3) the media must statically continue to broadcast more polarized messages rather than, say, adjust to appeal more to the beliefs of their current subscribers. Moreover, and somewhat counter-intuitively, under these conditions we find that polarization is more likely to occur when media consumers are exposed to more diverse messages, and that polarization occurred most often when there were low levels of echo-chambers and fragmentation. These results suggest that polarization is not simply due to biased individuals responding to an influx of media sources in the digital age, but also a consequence of polarized media conditions within an information ecosystem that supports more diverse exposure than is typically thought. 

Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.”