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Students in geographically rural areas of the United States have less access to computer science education and are underrepresented in computer science majors and careers. At the same time, many rural occupations such as agriculture are becoming reliant on technology, and there is a need for skilled computer scientists with a rural background and skillset to develop effective tools and software that can be used in those occupations. In addition, the values of grit, determination, self-sufficiency, and perseverance often studied in rural populations are also attributed to successful computer scientists. Given the need for rural students to participate in computer science careers, and the overlap in rural values and the qualities of good computer scientists, why do rural students not see themselves as future computer scientists, and why are they not interested in computer science majors and careers? In this paper, we examine the geographical definition of "rural" as used by many researchers (based on the definition from the National Center for Education Statistics NCES) that is often applied homogenously across a school district or even entire county. We explore and validate a survey instrument used to measure "rural identity" of students at the individual level. In doing so, we discover a more broad and nuanced definition of "rural" that varies widely within individual schools. By analyzing this rich dataset, we build the case that defining individual students as "rural" based on geographical location is insufficient to account for variances in their interest in computer science careers and their own self-identity as someone who could be a computer scientist. We use this information to inform future research and propose new avenues for engaging "rural" students in computer science. 

The continuous rising of digital learning platforms have unarguably brought about a surge in the amount of data obtained from different learning environments, which presents a great opportunity for computer science teachers to gain an understanding of students’ coding processes. This is vital for enhancing student support as teachers can gain insights into students’ thought process, strategies and identify areas where students might be struggling while tacking programming tasks. Traditional assessment methods such as feedback after homework submissions or completed lab assignments often results in late and untimely intervention that would prevent early dropouts and massive failures as the students’ learning journey is neglected in the process. We leverage students’ keystroke data obtained from Python and Java-based introductory programming courses delivered through CODIO learning platform, to design an interactive code visualization and error detection platform using Streamlit. The application features an interface that reproduces students’ code snippets with JavaScript enabled syntax highlighting. It includes a combination of dropdown menu and an adjustable slider which enables an instructor to navigate through the timestamps and have a detailed view of students’ coding processes. The application also includes a navigation to an environment that enables instructors to run the generated code snippets for error detection, giving the instructors clear idea of the difficult part(s) of the course for intervention purposes. The intervention ultimately fosters a more supportive learning environment and helps boost students’ confidence. 

In the United States, 1 in 5 people, approximately 66.3 million individuals, live in a rural area. To address the growing need for computing professionals and the need for a computationally literate populace, we need to engage rural learners effectively. A first step in this direction is understanding the learning context for students engaging in computer science, and how that differs for a rural population. In this paper, we draw upon the National Survey of Science and Mathematics Education, the High School Longitudinal Study of 2009, and the 2021 American Community Survey, to underscore a lack of access to computer science learning contexts for students in these communities. We also explore how rural out-migration is compounding this challenge, and explore the roots of the rural out-migration trend. We then examine how multiple strains of research and scholarship identify rurality as either a place-based identification (i.e., where a student is from) or a distinct social identity. While convenient, geographic-based definitions lack important nuance in understanding rural populations and tend to emphasize heterogeneity in rural populations, especially regarding economic factors (i.e., what the communities produce). In contrast, identity-based definitions often emphasize commonalities across rural populations including a set of shared values, a sense of belonging to a rural community, emphasis on social bonds, and a distrust of solutions offered by government, academia, and technology which are often seen as misguided and antithetical to those shared values. In certain kinds of decision-making, this rural identity has even been shown to overshadow intersectional racial and ethnic identities. This is an important consideration as 22\% of the US rural population is composed of racial and ethnic minorities. Finally, we discuss strategies to engage with rural populations authentically and meaningfully. We offer as an illustrative example our Cyber Pipeline program, an outreach effort including a Creative Commons licensed, customizable, modular curriculum; extensive teacher preparation program; and ongoing support for K-12 teachers working to bring computer science into rural schools. We also describe reasons why these rural-dwelling teachers seek to provide computer science education for their students. We highlight the specific challenges of this program, as well as our identified promising practices, in the hopes of fostering similar programs across the United States. 

In the push to broaden participation in computer science (CS) within the United States, there have been a number of highly successful efforts to engage urban high schools and communities. As urban areas often have high concentrations of poverty and underrepresented populations, these efforts meet a well-known need, and have a strong potential impact. However, urban audiences are not the only ones to lack adequate computer science education opportunities. In the United States, 1 in 5 people live in a rural area [19], and studies consistently show that rural areas offer fewer opportunities for students to engage with computer science than their urban and suburban peers. While some of the challenges rural schools face are shared by urban schools, the rural schools also have unique challenges that must be understood before engaging in successful intervention efforts. This paper describes one effort to support rural schools, their teachers, and their students. We seek to share the lessons we have learned in the hope that other programs may benefit. 

The lack of computer science education in rural areas presents unique challenges in the present pursuit of achieving equitable access to computer science education. The increase in the recognition of the need for computer science education comes with a need for inclusion of rural areas, and a corresponding increase in the demand of competent computer science teachers and educators. Teacher training programs play an important role in meeting these demands. This paper evaluates the impact of a teacher training program with focus on professional identity, commitment, confidence and competence as it relates to the teaching of computer science. The research includes teachers from rural, suburban and town locales enrolled in three separate semester courses. Through a mixed-method design, it uses quantitative data obtained through surveys taken prior to and at the completion of the training program to measure the impact. A combination of p-values and effect sizes were used to measure the impact of the teacher training programs. The survey covers three different domains - Teacher and Computing Identity, Rural Identity and Teacher Mindset, and lastly, Teaching Perceptions and Computational Thinking. Qualitative data gathered through reflective journals provides insights into teachers’ backgrounds and teaching experiences as well as anticipated professional growth. Following the training, findings show that rural teachers reported positive shifts in their identities and teaching competencies and are more likely to advocate for more students to take computer science courses. Teachers from the rural locales also showed a marked improvement in confidence and commitment to teaching computer science. 

For chemical recycling of plastic wastes to be viable, chemical products generated in recycling need to find markets. A network model of the U.S. chemical manufacturing industry was used to assess at what cost points, and the extent to which, chemical products from thermal pyrolysis of polyethylene might find markets in the current U.S. chemical manufacturing industry. Network modeling determined the cost points at which the simulated industry network utilized the thermal pyrolysis products and which processes were displaced by the supply of recycled materials. The characteristic feature of the simulations is the large number of processes in the chemical manufacturing network that are impacted by the availability of a relatively small number of products from polyethylene recycling. In the case of polyethylene recycling, the capital cost requirements for expanding capacity to effectively utilize the recycled materials is greater than the capital required for the pyrolysis process. This suggests that identifying scenarios where recycled materials can be utilized in processes that have excess capacity will be a critical consideration in techno-economic analyses of recycling plastics. 

Abstract Methane emission reductions are crucial for addressing climate change. It offers short-term benefits as it holds high short-term reductions in radiative forcing. Efforts towards the reduction of methane emissions are already underway. In this study, we compared and analyzed the mitigation benefits of cutting large amounts of methane emissions from the oil and gas sector on short-time scales with reducing an equivalent amount of carbon dioxide using carbon capture and storage (CCS). Characteristics of CCS are that it would require substantial infrastructure development and that it incorporates deployment delays. Results illustrate that prioritizing quickly deployable methane emission reduction alternatives that necessitate minimal construction is an efficient approach to achieve near-term climate change relief. Graphical abstract