This work-in-progress innovative practice paper describes an approach and presents preliminary results of an effort by the NSF-funded Computing Alliance of Hispanic-Serving Institutions (CAHSI) to build research capacity of faculty at HSIs and students from underserved populations. A key factor in our nation's ability to innovate solutions to grand challenges and compete in a technology-enhanced world that rapidly changes is the involvement of individuals with different perspectives, experiences, and disciplinary knowledge. Diversifying representation in research cannot be achieved without involvement of HSIs, which enroll significant numbers of minoritized students in U.S. higher education. This paper describes a CAHSI-Google Institutional Research Program (IRP) that builds research capacity through partnerships between computing doctoral-granting CAHSI institutions and computing non-doctoral granting CAHSI institutions. This paper describes the IRP and its well-defined process to support faculty as they develop and refine research ideas and submit competitive proposals for funding through the IRP that includes a collaboration plan outlining coordination mechanisms and student professional development efforts.

This Innovative Practice paper describes the Local Research Experiences for Undergraduates (LREU) program that was established by the Computing Alliance of Hispanic-Serving Institutions (CAHSI) at Hispanic-serving institutions (HSIs) in 2021 to increase the number of students, particularly students from underrepresented populations, who enter graduate programs in computer science. Since its first offering in Spring 2022, the LREU program has involved 182 faculty and 253 students. The LREU program funds undergraduate research experiences at the students' home institutions with an emphasis on first-generation students and those with financial needs. The motivation for the program is to address the low number of domestic students, particularly Hispanics and other minoritized populations, who seek and complete graduate degrees. Research shows that participation in research activities predicts college outcomes such as GPA, retention, and persistence. Even though these studies inform us of the importance of REU programs, many programmatic efforts are summer experiences and, while students may receive support, faculty mentors rarely receive coaching or professional development efforts. What distinguishes the LREU program is the focus on the deliberative development of students' professional and research skills; faculty coaching on the Affinity Research Group model; and the learning community established to share experiences and practices and to learn from each other. Students, who are matched with faculty mentors based on their areas of interest, work with their mentor to co-create a research plan. Students keep a research journal in which they record what they have learned and identify areas for their growth and development as researchers. The LREU provides an opportunity for the LREU participants to cultivate a growth mindset through deliberate practice and reflection from personal, professional, social, and academic perspectives. The paper discusses the multi-institutional perspectives that help CAHSI understand the types of challenges faced in undergraduate research programs, how faculty mentors communicate and make decisions, and how mentors resolve challenges, allowing the research community to better understand students' and faculty experiences. In addition, the paper reports on research and evaluation results that documented mentors' growth in their knowledge of effective research mentoring practices and students' learning gains in research and other skills. The paper also describes the impact of the learning community, e.g., how it supports developing strategies for interaction with and mentoring students from underrepresented populations.

This research-to-practice paper aims to examine the Collective Impact Model and its application during a network meeting to illustrate its utility in supporting efforts in building a community among a complex system of partnerships and initiatives. We collected data from small group discussions among 56 CAHSI faculty staff and administrators using a Communities of Practice approach to collect evidence of community knowledge development. Emerging themes from data analysis included: a) successful initiatives for CAHSI students; and b) collectively identified challenges. These findings illustrate how the Collective Impact model supports CAHSI network members to be intentional in their efforts by assessing their progress toward a common goal, evaluating the effectiveness of their efforts, and making plans toward next steps. Moreover, capturing the dynamics of the Collective Impact Model allows CAHSI to promote multidimensional change by creating culturally responsive initiatives and learning opportunities for Hispanic students in CAHSI.

Historically, student caregivers have been described as parents over the age of 25. However, the role of a caregiver may extend beyond a parental responsibility to include caring for other family members and may include students who are of “traditional college age.” Failing to acknowledge and provide appropriate support for student caregivers can be particularly problematic for students in engineering and computing. Engineering and Computing programs often require extensive work that must be completed outside of class. Meeting these demands is particularly challenging for students with caregiving responsibilities. However, there are significant gaps in our understanding of college experiences and college outcomes of student caregivers enrolled in engineering and computing programs. To understand the experiences of undergraduate student caregivers in engineering and computing fields, this study explored the differences in non-cognitive and affective factors between students who have caregiving responsibilities and those who do not have responsibilities during college. We particularly focused on student caregivers in the College of Engineering at an R1 Hispanic-Serving Institution (HSI). The context of HSIs is important given that these institutions enroll a large number of post-traditional college students, students who have significant life responsibilities that are often at odds with the demands of college. Additionally, this study incorporates a Hispanic-servingness approach, a framework centered on institutions acknowledging the realities and needs of the students they serve. Using data from a single institution, we asked the following research question: To what extent are caregiving responsibilities related to undergraduate engineering and computing students' non-cognitive and affective factors? Our findings indicate that the experiences of students with caregiving responsibilities differ on five non-cognitive and affective factors: open-mindedness, engineering/computer science identity, help-seeking, motivation, and time management. Taken together, these findings draw attention to the importance of universities taking a holistic approach to developing student support services for Engineering/Computing student caregivers.

Involving diverse individuals who bring different perspectives, experiences, and disciplinary knowledge in solving problems is critical in our nation's ability to innovate and compete in a global economy. Unfortunately, the trends in the number of graduates with advanced degrees, in particular ethnically and racially diverse citizens and permanent residents, are insufficient to meet current and future national needs. This is exacerbated in computing, which is one of the least diverse fields. Despite the growth in numbers of Hispanics nationally and their representation in undergraduate studies, the number of Hispanic citizens and permanent residents who enter and complete graduate computing studies is disturbingly low. Studies report that Hispanic graduate students across all fields of study feel isolated and alienated, face lack of support, experience low expectations from faculty, and a negative racial/ethnic climate. Students often encounter a STEM culture centered on competition and selectivity, and this must be addressed to increase pathways to the doctorate to support our nation's economic and national security goals. This paper describes a collective effort of institutions with high enrollments of Hispanic students that have built partnerships among non-doctoral-granting and doctoral-granting institutions to increase representation of Hispanics in graduate studies. Led by NSF's Eddie Bernice Johnson Computing Alliance of Hispanic-Serving Institutions (CAHSI), the collective employs evidence-based practices grounded in the Hispanic-servingness literature to address the root causes.

Faculty diversity is a key concern across U.S. colleges and universities, especially in computer science (CS) departments, where there is both a faculty shortage and a lack of diversity among faculty. Given the crucial role CS faculty play in shaping the field, in terms of both producing knowledge and mentoring the next generation of computer scientists, developing diverse and robust pathways to the professoriate must be an institutional priority. This panel will offer interdisciplinary perspectives from scholarly experts in CS and higher education, leveraging panelists' knowledge about diversity and equity in pathways to the CS professoriate. Panelists will provide relevant empirical insights about students' academic pathways and institutional practices that may broaden access to faculty careers. Panelists will also engage in a conversation about reimagining pathways to the CS professoriate with equity-minded policies and practices.

The Computer Science education community is supported by numerous national-focused consortia dedicated to addressing persistent inequalities in student pathways. This panel, comprised of leaders from four of those consortia (CAHSI, STARS, ECEP and RPPforCS) will use four key aspects of improvement communities as a framework for discussing the role, potential and impact of these consortia in supporting the equitable growth of computer science education. While not designed to be Network Improvement Communities per se, we draw upon those frameworks to focus on four specific aspects: 1) guided by shared goals with attention to equity; 2) engaging community in deep understanding of the problems and associated systems; 3) application of improvement science; and 4) coordinated efforts of shared learning and measurements. This will lead towards an open discussion with the audience focused on what has been empirically learned about the value these consortia have for their members and the broader CS Education community.

One of the critical needs of the 21st-century workforce development is the recruitment, retention, and graduation of women in STEM fields. Research suggests that women drop out of academic programs and leave the workforce to deal with financial setbacks, tend to personal obligations and offer service in military programs. It is important these women, i.e. returning women, have pathways for reentry to college and opportunities to advance their careers. Some areas within STEM fields, such as Emerging Technology (EmTech) in computer science are expected to experience increases in job opportunities more quickly than traditional areas. The demands of these jobs can only be fulfilled by creating pathways for untapped STEM talent pools, including returning women. Therefore, we propose a panel to discuss the barriers and opportunities women face (re-)entering the STEM education and career paths, especially in EmTech fields. The panel of experts will provide different perspectives to spark conversation and reflection. The objective of the panel is to share experiences, advice, and ideas to advance the current state of knowledge about the complex challenges that women encounter and support structures for their reentry to the education and professional pipeline.

This Innovative Practice Work-In-Progress paper elucidates the approach of the NSF-funded CAHSI INCLUDES Alliance for creating change in students' competencies by an effort across eight institutions to support the delivery of one-and two-credit hour courses for three levels of problem solving in Computer Science: general problem solving, computational thinking in problem solving, and algorithmic thinking in problem solving. The courses were developed to address industry's need for improved problem-solving skills, incorporating consistent, deep collaboration with Google technical staff The first of its kind for CAHSI, the problem-solving courses are fewer credit hours than typical courses in order to fit within a traditional curriculum. The intent is to instill complementary problem-solving, computational thinking skills, and logical reasoning needed to succeed in computer science, and make this content available across different student populations at various stages in their academic pathways. Advanced problem solving prepares students for competitive interviews. The courses create opportunities to learn across academic levels, and create new student communities, mentorship opportunities, and social connections to support retention. The paper reports on the course design, student reflection, assessment and evaluation, and an ethnographic study of the courses.

Broadening the participation of women in computing has increasingly become a focus of computing education research over the past several years. To be sure, the field of computing has unique challenges that merit close examination. At the same time, social scientists have built large bodies of literature related to gender equity across disciplines that span several decades, much of it critical to how we understand (in)equity in computing. This panel will provide perspectives from scholars with expertise in a variety of disciplines, including computer science, higher education, and educational psychology. Panelists will share relevant research from their home disciplines and initiate a discussion on the future of computing education research.

Full paper, Research-to-Practice. With the nation's continuing shortage of scientists and engineers, there is an urgent need for diverse students, especially Hispanic students, to enter and complete science, technology, engineering and math (STEM) education at all levels. Contributing factors to the low representation in STEM areas are the lack of Hispanic youth's exposure to engaging and relevant experiences with science in their early stages of development and the lack of Latino scientists as role models that are visible, tangible, real, and inspiring to their communities. In addition, student experiences in math and science in the middle grades have emerged as a “filter” that prevents many students from pursuing advanced STEM courses in high school and, therefore, limits their knowledge of and interest in exploring STEM subjects and careers later in their education. This paper presents a research-based approach to attract middle school students to STEM fields in a majority Hispanic region, using a game-based learning platform. The platform integrates computational thinking in the area of water conservation at middle-school level by using simulations and data visualization in concurrent learning modules. The concepts introduced in the platform are universal enough to be successfully employed in other geographical regions with different student demographics.

This research-to-practice full paper describes the deliberate and arduous process we recently went through to develop a national survey to study the non-cognitive traits of undergraduate engineering and computing students. The goal of this survey is to characterize student profiles in order to develop and examine particular interventions to guide students toward success in engineering and computing majors. This survey measures non-cognitive attributes including personality, sense of belonging, engineering or computing identity, study skills, well-being, and a variety of other constructs that are not routinely measured in engineering populations nor integrated into admission decisions, advising processes, or academic curricula. Prior research indicates that these non-cognitive attributes are important for students' academic success and retention. However, no studies have examined a comprehensive set of non-cognitive traits holistically to understand how they influence student success. This collaborative project, funded by three linked NSF grants, merges the interests of researchers at three campuses in understanding and supporting students with varied non-cognitive profiles. As part of this research, we negotiated the content of a national survey, suitable for use on our own campuses as well as with other national partners, to probe more than a dozen constructs collectively describing student non-cognitive attributes. The construction of the survey itself was non-trivial, and involved significant negotiations among the researchers including initial collection of instruments with validity evidence to serve as a basis for discussion; an in-person kick-off meeting; multiple follow-up teleconferences; multiple rounds of inclusion/exclusion decisions based upon mutually-agreed upon guiding principles; pilot survey testing; pilot data evaluations such as exploratory factor analysis; and final decisions about instruments/items to include in the final version, all while considering survey length, distribution channels, and key IRB concerns. This paper details the 10-month effort to construct a survey that meets the research needs and intellectual curiosity of partners at three diverse campuses. In this process, we had to balance the different institutional contexts of the funded partner sites while also maintaining flexibility for national distribution. The deliberate processes we used may serve as a template for future survey creation, starting from constructs of interest, to selection of specific instruments (or sub-scales thereof), and factor analysis to consider further down-selection of individual items to include in the final survey. The outcomes of this paper may serve the engineering education community by highlighting previously undocumented processes in collaborative survey construction that introduce intellectual complexity or time delays into the development timeline.

The Sol y Agua project will augment the learning environment of middle school students with the creation of a computer game based on regional issues explored through information overload, virtual simulations, and real world data [1]. The game has a theme of water sustainability and stewardship with an emphasis on information analysis, negotiation, and decision-making. The game will also immerse students in themes concerning biodiversity, sustainability, and the human impact on the environment. The various activities will allow student to make informed decisions regarding water sources, maintenance, environmental factors, and sustainability, among others. The students will learn the impact of humans on the environment through data analysis and run experiments in a virtual laboratory with real data. Data visualization will allow students to interpret data in new, fun, and meaningful ways. The game has an emphasis in promoting the demographic of the Southwest United States and minorities towards developing careers in STEM, where they are largely underrepresented. The game activities have been developed using the Texas Essentials of Knowledge and Skills (TEKS) Standards [2].

Data Science, the field of data extraction, manipulation, and analysis in order to derive knowledge or insight, is becoming an essential component of effective management and planning for industrial, research, scientific, and social communities. With the goal of engaging middle-school students in the important field of Data Science, the Sol y Agua project is developing a computer game based on regional environmental issues explored through data analysis. The Sol y Agua project will augment the learning environment of middle school students by incorporating simulation and data science to existing curricula in the form of an interactive educational game. The game has a theme of water sustainability and stewardship with an emphasis on information analysis, negotiation, and decision-making. The game will also immerse students in regional issues concerning biodiversity, sustainability, and the human impact on the environment. This video game will allow students to analyze and interpret data, and make informed decisions about water conservation and sustainability. Simulation and data visualization allow students to perform simulated, environmentally-conscious planning and virtual experiments. Using simulations allows students to easily and inexpensively gather data from the virtual environment. Data visualization allows us to quickly represent this data in new, meaningful, and engaging ways.

Through support from the National Center for Women & Information Technology (NCWIT), the University of Texas at El Paso (UTEP) and the El Paso Community College (EPCC) began a program to collaborate on adoption of Peer-Led Team Learning (PLTL) at EPCC. The NCWIT-funded effort aims to transfer this effective retention practice to the EPCC in order to establish early connections with female students, create community, and provide activities that improve students' problem-solving skills. PLTL provides an active learning experience for students and creates leadership roles for undergraduates. For the peer leaders, the experience of working with faculty and guiding their peers through a challenging course is rewarding, and they learn communication, teaching, leadership, and interpersonal skills. Peer leaders become more confident about their career path,= and many continue to be involved in the department through undergraduate research positions. This is important for retention and advancement efforts, since the peer-leading experience influences the students' motivation to attend graduate school. This paper describes how the UTEP-EPCC partnership was structured, how the practice was transferred, and the challenges that were encountered. It also presents the evaluation results.

In order to develop the engineers of the future, engineering departments need to embrace innovative, student-centered practices. The development and sustained growth of organizational improvement practices like those needed to improve engineering education depend upon an institutions' or departments' collective and individualized attention to human resources, leadership, knowledge development, revenue development and opportunities for continuous engagement. The literature in engineering education related to sustainability and curricular change indicates initial training and dissemination is necessary though not sufficient for change to take root, that all change agents need mentoring, collaboration opportunities, and venues for sharing their work, that innovative practices may vary across settings, and that a systemic effort needs continuous attention to remain robust. The paper provides CAHSI as an example of sustained innovation, and details the ways in which CAHSI was designed for sustained impact in engineering education across partnering Hispanic Serving Institutions. The paper highlights programmatic considerations and evaluation design, and describes how the results can inform leadership regarding progress and needs for sustaining change.

Increasing enrollment and retention in science, technology, engineering, and mathematics (STEM) programs, particularly for students from underrepresented groups, has become a national priority. Institutions often have multiple funded and unfunded efforts that address student success and broadening participation. One challenge is that they are typically discipline specific with little interaction with complimentary efforts outside of the sponsoring department or college. Such isolation can hinder wider adoption of effective practices and the development of innovative programs based on lessons learned. This project, funded by the NSF Innovation through Institutional Integration program, focuses on defining the cyberinfrastructure and communities needed to support the discovery and integration of student-success projects. The paper is organized as follows: discussion of the motivation; presentation of the infrastructure and models created to connect data collected by various projects, units, and programs; and description of MetaShare, a metadata management system that captures metadata about artifacts in support of integration across projects.

Hispanics have the highest growth rates among all groups in the U.S., yet they remain considerably underrepresented in computing careers and in the numbers who obtain advanced degrees. Hispanics constituted about 7% of undergraduate computer science and computer engineering graduates and 1% of doctoral graduates in 2007--2008. The small number of Hispanic faculty, combined with the lack of Hispanic role models and mentors, perpetuates a troublesome cycle of underrepresentation in STEM fields. In 2004, seven Hispanic-Serving Institutions (HSIs) formed the Computing Alliance of Hispanic-Serving Institutions (CAHSI) to consolidate their strengths, resources, and concerns with the aim of increasing the number of Hispanics who pursue and complete baccalaureate and advanced degrees in computing areas. To address barriers that hinder students from advancing, CAHSI defined a number of initiatives, based on programs that produced promising results at one or more institutions. These included the following: a CS-0 course that focuses on adoption of a three-unit pre-CS course that uses graphics and animation to engage and prepare students who have no prior experience in computing; a peer mentoring strategy that provides an active, collaborative learning experience for students while creating leadership roles for undergraduates; an undergraduate and graduate student research model that emphasizes the deliberate and intentional development of technical, team, and professional skills and knowledge required for research and cooperative work; and a mentoring framework for engaging undergraduates in experiences and activities that prepare them for graduate studies and onto the professoriate. CAHSI plays a critical role in evaluating, documenting, and disseminating effective practices that achieve its mission. This paper provides an overview of CAHSI initiatives and describes how each addresses causes of underrepresentation of Hispanics in computing. In addition, it describes the evaluation and assessment of the initiatives and presents the results that support CAHSI’s claim of their effectiveness.

The difficulty of writing, reading, and understanding formal specifications is one of the main obstacles in adopting formal verification techniques such as model checking and runtime verification. Introducing concepts in formal methods in an undergraduate program is essential for training a workforce that can develop and test high-assurance systems. This paper presents educational outcomes and outlines an instructive component that can be used in an undergraduate course to teach formal approaches and languages. The component uses a model checker and a specification tool to teach Linear Temporal Logic (LTL), a specification language that is widely used in a variety of verification tools. The paper also introduces a novel technique that analyzes LTL specifications by using the SPIN model checker to elucidate the behaviors accepted by the specifications.

This paper presents an approach to support run-time verification of software systems that combines two existing tools, Prospec and Java-MaC, into a single framework. Prospec can be used to clarify natural language specifications for sequential, concurrent, and nondeterministic behavior. In addition, Prospec assists the user in reading, writing, and understanding formal specifications through the use of property patterns and visual abstractions. Prospec automatically generates specifications written in Future Interval Logic (FIL). Java-MaC monitors Java programs at runtime to ensure adherence to a set of formally specified properties. Safety properties of a program are specified in the formal language Meta-Event Definition Language (MEDL). Java-MaC generates runtime components from specifications. The components are used to instrument the target program and determine whether the execution of the program violates any of the safety properties. This paper describes an algorithm for translating FIL formulas into MEDL formulas. It provides the transformation rules used by this algorithm, and it demonstrates the general correctness of the translation.

A goal of the Geosciences Network (GEON) is to develop cyber-infrastructure that will allow earth scientists to discover access, integrate and disseminate knowledge in distributed environments such as the Web, changing the way in which research is conducted. The earth sciences community has begun the complex task of creating ontologies to support this effort. A challenge is to coalesce the needs of the earth scientists, who wish to capture knowledge in a particular discipline through the ontology, with the need to leverage the knowledge to support technology that will facilitate computation, for example, by helping the composition of services. This paper describes an approach for defining workflow-driven ontologies that capture classes and relationships from domain experts and use that knowledge to support composition of services. To demonstrate the capability afforded by this type of ontology, the paper presents examples of workflow specifications generated from a workflow-driven ontology that has been defined for representing knowledge about gravity data.

Prospec is a tool that assists practitioners in the elicitation and specification of system properties. Practitioners are guided by questions, definitions, and graphics. Prospec introduces the use of composite propositions to identify intended behavior when multiple conditions or events are considered. Multiple conditions or events may represent behavior such as sequences, concurrency, and non-determinism and may define the boundaries of scopes or type of patterns. Prospec is built upon the Specification Pattern System. The tool assists the analyst in making informed decisions about aspects of a specification that may have multiple interpretations. The end product of the tool is a formal specification in Future Interval Logic, Linear Temporal Logic, or Meta Event Definition Language.

A goal of runtime software-fault monitoring is to observe software behavior to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing additional defense against catastrophic failure. Although runtime monitoring has been in use for over 30 years, there is renewed interest in its application to fault detection and recovery, largely because of the increasing complexity and ubiquitous nature of software systems. We present taxonomy that developers and researchers can use to analyze and differentiate recent developments in runtime software fault-monitoring approaches. The taxonomy categorizes the various runtime monitoring research by classifying the elements that are considered essential for building a monitoring system, i.e., the specification language used to define properties; the monitoring mechanism that oversees the program's execution; and the event handler that captures and communicates monitoring results. After describing the taxonomy, the paper presents the classification of the software-fault monitoring systems described in the literature.

Runtime monitoring is aimed at ensuring correct runtime behavior with respect to specified constraints. It provides assurance that properties are maintained during a given program execution. The dynamic monitoring with integrity constraints (DynaMICs) approach is a runtime monitoring system under development at the University of Texas at El Paso. The focus of the paper is on the identification of instructions at the object-code level that require instrumentation for monitoring. Automated instrumentation is desirable because it can reduce errors introduced by humans, it provides finer control over monitoring, and it allows greater control over instrumentation. The paper also discusses two other technologies associated with DynaMICs: the elicitation and formal specification of properties and constraint; and tracing property or constraint violations to the software engineering artifacts from which the constraints and properties were derived.

Geospatial databases often contain erroneous measurements. For some such databases such as gravity databases, the known methods of detecting erroneous measurements-based on regression analysis-do not work well. As a result, to clean such databases, experts use manual methods which are very time-consuming. In this paper, we propose a (natural) "localized" version of regression analysis as a technique for automatic cleaning. We illustrate the efficiency of this technique on the example of the gravity database.

The benefits of working in a research group are clear: students develop domain expertise, gain an understanding and appreciation of the research process and its practice, and acquire team, communication, problem-solving, and higher-level thinking skills. Students with this experience are better equipped to make informed judgments about technical matters and to communicate and work in teams to solve complex problems. However, it is difficult to provide a quality experience to large numbers of students, particularly to students of differing abilities.The Systems and Software Engineering Affinity Research Group model provides a socialization mechanism and infrastructure that supports the development and management of large research groups that engage undergraduate and graduate students, who have a wide range of skill levels and experiences, in research and projects. This non-hierarchical model integrates students into both small research groups and an encompassing large research group, and uses structured activities to develop their research, technical, communication, and group skills.In this paper we introduce the model and report how the model meets independently developed Best Practice guidelines for student research experiences and we provide indicators of success for use by other projects.

Dynamic Monitoring with Integrity Constraints (DynaMICs) is a software-fault monitoring approach in which the constraints are maintained separately from the program. Since the constraints are not entwined in the code, the approach facilitates the maintenance of the application and constraint code. Through code analysis during compilation, the points at which constraint checking should occur are determined. DynaMICs minimizes performance degradation, addressing a problem that has limited the use of runtime software-fault monitoring. This paper presents the preliminary design of a DynaMICs snoopy-coprocessor system, i.e., one that employs a coprocessor that utilizes bus-monitoring hardware to facilitate the concurrent execution of the application and constraint-checking code. In this approach, the coprocessor executes the constraint-checking code while the main processor executes the application code.

This paper reviews the progress that has been made towards the definition of a new generation of computer assisted problem solving tool. When we solve problems with a computer we state the solution in terms of several languages, beginning with a requirements specification language and ending with a program. Beginning with a generalization of a programming language, this paper follows steps towards the removal of control and data structure information in a problem solution. The effort discussed here would result in the requirements specification language being the final language used in problem solving.