This work-in-progress (research-to-practice) paper describes the Allyship program that has been implemented at Hispanic-serving institutions (HSIs) to build student support structures, particularly for women and non-binary students. As technology permeates through all aspects of our world, broadening participation in computer science education is critical to securing sustainable national technology and economic development. Unfortunately, women, especially ethnic and racial minority women, have been underrepresented in computing fields for decades. The trends in the number of bachelor's degrees in computing awarded to Latina students have not improved over the past twenty years, consistently accounting for less than 2% of the total number of undergraduate degrees awarded in computing. Most programmatic practices generally feature broader STEM disciplines, are not discipline-specific, and do not specifically support women and non-binary students in computing, which is the least diverse STEM field. Furthermore, most studies of mentoring programs rely primarily on graduate students, faculty, and professional mentorship. To fill the gap in practices and knowledge about the role of near-peer mentoring, explicitly focusing on women and non-binary students in computing fields at Hispanic-Serving Institutions, the Computing Alliance of Hispanic-Serving Institutions (CAHSI) an NSF Eddie Bernice Johnson INCLUDES, and BPC Alliance piloted the Allyship program at two HSIs. This paper describes how CAHSI implemented this program in a way that aligns with the theory of community cultural wealth. The Allyship program builds relationships among first- and second-year Latinx students with upper-division Latinx students in computing to build an inclusive community and supportive departmental environment. The goal of the Allyship program is to increase departmental engagement, retention, and persistence in the major. Preliminary assessment results from a participant survey show that following participation, students reported higher scores of sense of belonging, self-efficacy, and interest in computing compared to the beginning of the semester. The findings provide insight into using a strengths-based approach in peer support programs that allows women and non-binary students at HSIs to leverage their cultural knowledge and assets. This peer support program would be particularly impactful at low-resourced HSIs, which have high Hispanic enrollment but lack sufficient institutional resources, as the institutions can make significant changes with relatively low costs to implement sustained peer-to-peer support programs.

This paper analyzes, from a servingness perspective, the experiences of students in a Hispanic Serving Institution (HSI) implementing personalized mentoring strategies as part of an NSF-funded, S-STEM program. The program objective was to increase the retention and success of low-income academically talented students. We discuss the results of using Individual Development Plans (IDP) to help students establish post-graduation goals and action plans to reach them. The IDPs also maintained a flexible structure that allowed students for morphing both their plans and envisioned outcomes, as they progressed in their chosen academic programs. Student outcome expectations were assessed at three stages of their development: in their first year, as juniors in what can be considered a mid-point of their development, and during their fourth year of program participation.

The prevalent gap between students from different socioeconomic statuses (SES) affects multiple aspects of our social functionality. Among its effects, the SES gap directly impacts the opportunities to which students have access while in college and beyond. This paper discusses the efforts and partial results of a program aimed at bridging such differences for low-income, academically talented students (LIATS) in a Hispanic Serving Institution (HSI). The reported approach leverages the relations with a group of industry partners to provide LIATS with scholarships, professional mentorship, work-shops, and on-the-job training opportunities while sharing with the in-dustry the students' e-portfolios, professional profiles, and resumes, developing a symbiotic relationship where both benefit. After three years of interactions, the results show how these opportunities helped LIATS develop their skills, leadership, and competitiveness as future STEM professionals. The experience also demonstrates that profes-sional growth opportunities are critical for engaging LIATS in real-life contexts where they collaborate and interact with industry part-ners, and for providing them with opportunities that help to bridge the SESgap.

Reflection is integral to learning and professional practice. We discuss how deliberate reflection can be integrated into coursework and how instructors can embed it. We will use an online problem solving activity individually and in small groups to provide a context for reflection, and practice guided reflection as problem solvers (emulating students) and as instructors. Discussion leaders will guide reflective discussions and connect to their experiences teaching problem solving courses. Collaborative problem solving and reflection activities can support the participation and success of all students across academic content areas, specifically underrepresented students. This session expects to engage educators interested in reflection to increase students' critical thinking skills. Leaders are CS faculty who taught CAHSI INCLUDES problem solving courses, designed in collaboration with Google, with reflection as an important part of solving problems. We will utilize a problem-solving activity to ground discussions in a shared experience, with time for solitary work, group work, and reflection on the problem and the processes used to solve it. Discussion leaders will guide reflective discussion and connect to their experiences teaching problem solving course content as part of the Google/CAHSI partnership. Groups will regather at the end to report and reflect about the experience of the BoF.

This Work in Progress Paper of the Research-to-Practice Category discusses a complementary learning tool to motivate students to pursue careers in Science, Technology, Engineering, and Math. Students from middle and high schools are experiencing a lack of interest, awareness and resources to pursue careers related to science and engineering. Knowing that education is a key factor for success, an initiative was developed to attract these students into STEM careers, by using the growing and emerging field of nanotechnology as a catalyst. In this research, an educational video game was developed to motivate students to pursue STEM careers and introduce them to the world of nanotechnology. The designed video game presents information about nanotechnology using different approaches, including accurate representations of nanotechnology and other concepts at nanoscale, an engaging storyline, and an interactive game experience to involve players into the lesson. All concepts and teaching strategies are based on the Constructivism and Mindset Learning Theories. The video game provides students the opportunity of having fun while learning from the interactive menu and platformer levels. It is expected that this educational video game significantly increases the number of students to pursue STEM related careers.

This Research Category Full Paper presents an approach for categorizing student misconceptions about dynamics and heat transfer using text classification. Research in educational engineering describes how science concepts can be ontologically categorized into two major categories (substances and processes) according to their nature. Students can acquire misconceptions by incorrectly categorizing concepts. Predicate tests help to determine when misconceptions have occurred and aid in customizing curriculum content. However predicate tests rely on time-consuming, manual labor. The main goal of this research was to show how predicate tests could be automated with text classification models using a previously annotated dataset. We compared classifier performance between WEKA's Support Vector Machines, Multinomial Naïve Bayes, Logistic Regression, and Bayesian Logistic Regression implementations using the emergent process and sequential process ontological categories as labels. We compared model performance using WEKA's N-Gram tokenizer with 3-grams vs. using Java's WordTokenizer to convert our word dataset to numerical vectors. Models were evaluated using 10-fold cross-validation considering accuracy, F-measure, and kappa coefficient as measures of performance. We have shown the feasibility of using text classification for misconception assessment. Our implementation of predicate test automation can play an important role in speeding up misconception assessment research and curriculum design research.

The changes in the defining ideas of the contemporary world, the exponential growth of knowledge, and the expansion of technological innovations have created a need for a critical examination of the convergences and connections between the Sciences, Engineering, and the Humanities. Today, there are areas of study so complex that they go beyond the confines of a single discipline. Examples of such areas include (1) the comparative study of concepts of mind, consciousness, and machines, (2) the critique of the technological culture through appropriate and alternative technology approaches, and (3) the questions surrounding cosmology, evolution, and beliefs. In response to this, we have developed interdisciplinary, team-taught, general education courses on artificial intelligence, appropriate technology, and the origins of the universe that respond to the above challenges, while fostering the ability of our students to use skill sets and concepts from different and divergent disciplines in order to examine such complex areas of study. The preliminary results indicate that, while there is a need for continuous retooling of the course model to better reflects the General Education goals and the university culture, this is a successful course model for our institution. The data also suggests that this type of interdisciplinary intervention in the beginning years of university study positively impacts the development of students' abilities in these areas.

This work describes workflow modeling using Workflownets and Generalized Stochastic Petri Nets (GSPN) for the Digital Publishing business process and how the dependability attributes are measured in a quantitative form. In our novel approach, these are measured from the workflow model itself, improving its analysis. Applying these measure concepts to the Digital Publishing pre-press process provides a workflow management based on its trustworthiness. The methodology for workflow modeling is introduced and the results for a case study on the preflight stage of the Digital Publishing workflow are presented.