As artificial intelligence (AI) becomes increasingly integrated into daily life, there is a critical need for developing AI literacy across all educational levels. However, current AI education remains largely confined to college-level computer science classrooms with limited access for K-12 learners. We present the AI Scholars Program, a novel approach that addresses the AI education gap by preparing college computing students to serve as AI education ambassadors in their communities and empowering K-12 teachers to adopt AI education practices in their classrooms. This experience report presents the curriculum and its outcomes after one round of refinement. The program offers structured AI learning through bi-weekly webinars, resources, and collaborative opportunities to form teams and conduct community outreach projects. Our program invited 63 scholars from 30 institutions across the U.S., including 51 college students and 12 K-12 teachers. Their outreach impacted over 230 K-12 learners. We examine program outcomes for participants and projects through pre/post surveys measuring computing attitudes and self-efficacy for teaching AI, scholar interviews, and outreach project reports. We share lessons learned and challenges for designing similar programs, highlighting the importance of involving educators for effective community-engaged AI education. The program creates a sustainable pipeline for college students to develop technical skills and leadership while addressing K-12 AI education shortages. We contribute insights for scaling AI literacy and broadening participation in computing.

As more colleges and universities develop programs to support first-generation or low-income undergraduate Computer Science and Engineering students, retention and completion rates have empirically risen for students within such group. Despite this success, financial support for these programs has become increasingly uncertain due to the recent shift in United States government priorities, tightening of corporate coffers, and state budget shortfalls. This panel will probe the experiences of four faculty from diverse institutions regarding the fundamental components that have been successful in their programs. We aim to clarify how institutions can provide quality support to first-generation and low-income students during these trying financial times.

Science undergraduate students benefit from gaining computing skills due to the growing overlap between scientific discovery and computational methods. One approach is to encourage students majoring in science to pursue a computing minor; however, only a few seem to undertake this endeavor. Financially disadvantaged students face additional barriers that may further deter them from this pursuit. Unfortunately, there is limited research on the benefits, limitations, and barriers related to earning a computing minor. This study aims to explore the perspectives of undergraduate college students from the United States in a scholarship program designed to aid low-income science majors in completing an information science and technology (IS&T) minor. Using an exploratory qualitative research approach, semi-structured interviews were conducted with seven students in a scholarship program. Based on the findings, three main recommendations for program stakeholders are proposed. First, explicitly explain the unique advantages of gaining computing knowledge for science majors. Second, ensure students have a foundational understanding of computing and related study skills. Third, guide students in strategically planning their course sequence to optimize their time and workload.

Multi-pronged programs that involve students in a combination of proven interventions (i.e., tutoring other students, building community, developing skills, etc.) constitute one pedagogical approach to increasing the number and diversity of computing professionals. In this manuscript, we evaluate the efficacy of one such multi-pronged program, the STARS Computing Corps, a Broadening Participation in Computing Alliance program funded by the National Science Foundation. These analyses improve upon previous efforts to assess the efficacy of STARS by examining dosage effects of the program, adding controls for students' initial intentions to pursue computing, and conducting these analyses at various points in a student's participation in STARS. We also conduct analyses to determine the efficacy of various STARS activities. Controlling for students' initial intentions to persist in computing, we find robust evidence that spending more time each week on STARS' activities positively predicts students' intentions to persist in a computing career, and that STARS has a heightened positive impact on Black and Hispanic students. We do not find evidence that the number of semesters a student spends in STARS is predictive of computing persistence, nor do we find differences in the efficacy of various STARS activities. In sum, these results suggest that STARS has a positive impact on students' intentions to persist in computing and that multi-pronged programs like STARS should focus on the intensity of participation (as opposed to the length of participation or a particular activity) to increase students' desire to persist in computing careers.

This full paper presents the Collaborative Active Learning and Inclusiveness (CALI) inventory, and an analytical model using the CALI inventory, demographic data, mindset surveys, and knowledge mastery assessment, to explore relationships between classroom climate and student experiences. The CALI inventory enables the investigation of the impact of the student experience in an active learning classroom by distinguishing the factors that characterize the structure, social learning, and inclusive practices. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. A CS Mindset Instrument was developed based on research that measured students' self-efficacy by evaluating the extent of variation in their self-perceived ability to accomplish a task, sense of belonging in computing, and professional identity development. Demographic data is collected that allows for an analysis using an intersectional lens to acknowledge the complexity of social and cultural contexts. The knowledge and mastery assessments capture changes in competency through pre-post mastery quizzes. The combination of CALI with other instruments, including those that characterize student mindset, identity, and levels of mastery, enables investigation of how various practices of inclusive and collaborative active learning have differential effects on students with different identities in computer science.

Introducing elementary students to computer science and computational thinking (CS/CT) can enhance their problem solving skills and enhance their confidence and sense of belonging in computing. Project moveSMART aims to introduce learning activities into elementary classrooms that address computer science concepts in a way that integrates with core curriculum requirements and promotes physical activity. In this paper, we explore an extension to an initial set of Project moveSMART computer science learning activities to introduce elementary students to machine learning concepts in a way that is integrated with required learning objectives covered in a Physical Education course. Specifically, students use the BBC micro:bit and its on board sensors to capture rhythmic movement data, explore and analyze patterns in the data, and use a learned "dance move recognition" application that uses their data in order to learn about machine learning in an age appropriate way. To demonstrate feasibility of supporting dance move recognition on the resource-constrained device, we developed a prototype, which is able to detect 5 different dance moves with a 96.6% accuracy.

In this paper, we define a learning activity for an elementary physical education classroom that simultaneously engages students in physical activity while introducing students to basic principles of reinforcement learning. Reinforcement learning is a sub-domain of machine learning in which an independent agent (in our activity, a student) takes some action or series of actions and receives a reward for the chosen action(s). While reinforcement learning intuitively maps to many activities in our daily lives, our learning activity involves a spy game. Students create sequences of spy moves that generate rewards based on their component moves and the orders in which they are performed. Students then iteratively expand their spy moves in an attempt to receive the maximum reward. The construction of the game will demonstrate that the rewards, while deterministic, do not always follow a greedy pattern, introducing students to basic algorithmic principles. Such an approach that combines physical activity with reinforcement learning connects artificial intelligence education within the broader scope of computing and students' everyday lives.

The current CS for All movement reflects a need to provide opportunities for every person to learn computer science, as a matter of equity. Many computing professionals have a desire to contribute to society; since access to computing education is limited in many ways, finding ways to use computing to give back is particularly valuable. In this Birds of a Feather, we will discuss strategies for individual students, faculty, and institutions to develop and lead efforts focused on improving inclusion, equity, and diversity in computing. Exemplary efforts that computing students, faculty, and computing professionals have used to broaden participation include mentoring, tutoring, K12 outreach, expanded research opportunities, and service learning. In this BoF, we invite participants to seek and share resources that can lower the barriers to engaging in outreach and research for broadening participation in computing. We will also discuss strategies for choosing projects that leverage local resources and opportunities, promote personal professional development, and contribute to institutional improvement.

Engaging student in Computer Science courses has been linked to higher performance and retention in the major. Yet many of the assignments that we deploy in our courses tend to be fairly academic exercises that do not engage students particularly well.

This full paper presents CALI 1.0: an inventory to capture the various components of collaborative active learning and inclusive practices in introductory CS courses, as a basis for studying their impact on a student’s ability to engage with course material and be successful in the course. Collaborative active learning is defined as a pedagogy where learners work with one another on activities that involve actively engaging with the course material through discussions, problem-solving, case studies, role plays, and other methods. CALI consists of three separate indices: Structure, Sociality, and Inclusiveness. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. In this paper, we present the inventory and results from a focus group of faculty to gauge responses to using this inventory to capture the teaching components of their courses. We present CALI as a tool that can be used to study how teaching practices, e.g. inclusive and collaborative active learning pedagogies, impact course experiences for students in different demographic groups as well as a reflective tool for use by faculty while designing their courses.

Academic computing departments are seeking ways to broaden participation in computing (BPC), and many are encouraging individual faculty, staff, and students to attend diversity-oriented conferences, like the Tapia and STARS Celebrations of Diversity in Computing and Grace Hopper Celebration of Women in Computing. The purpose of the STARS Ignite Workshop is to provide faculty and staff with a framework including the tools and knowledge needed to make such conference attendance beneficial both to the students attending the conference and to the sponsoring faculty and their department. In this workshop, participants will learn to recruit and lead a BPC purpose-driven student conference cohort and the implementation of a BPC event or program. Participants will be provided with opportunities to adapt sample materials (recruitment emails, applications, etc.) to their own needs, assess the BPC needs at their institutions, and learn how to determine if their BPC efforts are successful. A laptop and Google account are required for this event. Interested attendees should be willing to commit to leading a student conference cohort to a diversity-oriented computing conference and then implementing a BPC event or program with these students.

This workshop will provide instructors in early CS courses with tools and strategies for designing and building high quality courses by structure and content, that are student centered, aligned with stated learning outcomes, and with access to engaging learning materials. Workshop participants will be introduced to two software toolkits, CS Materials and BRIDGES, towards achieving these goals. These tools permit searches for learning materials that meet specific learning outcomes, while at the same time provide access to engaging materials that demonstrate core CS relevance to real world problems and applications. Workshop participants will be exposed to strategies for designing courses, materials and tools that can engage today's students and meet their expectations. Although this workshop will be based on CS Materials and BRIDGES, the lessons learnt are independently beneficial to participants.

The Internet of Things (IoT) devices exchange certificates and authorization tokens over the IEEE 802.15.4 radio medium that supports a maximum transmission unit (MTU) of 127 bytes. However, these credentials are significantly larger than the MTU and are, therefore, sent in a large number of fragments. As IoT devices are resource constrained and battery powered, there are considerable computations and communication overheads for fragment processing both on the sender and receiver devices, which limit their ability to serve real-time requests. Moreover, the fragment processing operations increase energy consumption by CPUs and radio transceivers, which results in shorter battery life. In this article, we propose CATComp—a compression-aware authorization protocol for constrained application protocol (CoAP) and datagram transport layer security (DTLS) that enables IoT devices to exchange small-sized certificates and capability tokens over the IEEE 802.15.4 media. CATComp introduces additional messages in the CoAP and DTLS handshakes that allow communicating devices to negotiate a compression method, which devices use to reduce the credentials’ sizes before sending them over an IEEE 802.15.4 link. The decrease in the size of the security materials minimizes the total number of packet fragments, communication overheads for fragment delivery, fragment processing delays, and energy consumption. As such, devices can respond to requests faster and have longer battery life. We implement a prototype of CATComp on Contiki-enabled RE-Mote IoT devices and provide a performance analysis of CATComp. The experimental results show that communication latency and energy consumption are reduced when CATComp is integrated with CoAP and DTLS.

Out-of-school time programs like summer camps have shown to be beneficial for exposing students to computer science, particularly in school districts where computing classes are not offered. The COVID-19 pandemic presented challenges for students who were already subject to the digital divide, as in-person camps came to a halt if they weren't prepared to pivot. In response, we created a virtual summer camp that provides informal computer science learning opportunities that were intentionally designed to increase the confidence of Black and Latina girls in computing and to promote positive perceptions of computer science education and career opportunities. Key to our approach is the recognition that representation in the camp's community of attendees, teachers, guest speakers, and in the content can foster confidence for Black and Latina girls in computing. In this paper, we draw on the intersectional computing framework and present the structural, instructional, and curricular design of the virtual program and present initial findings on the impact of the camp on computing confidence, intent to persist, social supports, and computing outcome expectations. Findings of a pre- and post-survey study of 107 camp attendees show that participating in the camp resulted in an increase in computing confidence as well as computing outcome expectations among Black and Latina girls.

As the COVID-19 pandemic began to unfold, reports of the impact on lives, public and private industry, and education systems began to emerge. Almost simultaneously, the long-standing pandemic of racism in the U.S. was called to the forefront. The policies, structures, and members of the computing community are not immune from the impacts of these dual pandemics. While we do not yet know the long-term effects on the computing community, several efforts have emerged to advance our understanding through data collection and analysis. This panel present preliminary findings from surveys conducted by multiple organizations that explore the impact of these dual pandemics on conditions of work and education in the computing community, with a focus on the impacts on populations that have been historically underrepresented in computing. The panel invites discussion about promising (and not-so-promising) practices that have emerged in response to these pandemics and next steps to support the computing community.

Computing proficiency is an increasingly vital component of the modern workforce, and computer science programs are faced with the challenges of engaging and retaining students to meet the growing need in that sector. However, administrators and instructors often find themselves either reinventing the wheel or relying too heavily on intuition, despite the availability of national curriculum standards. To address these issues, we present CS Materials, an open-source resource targeted at computing educators for designing and analyzing courses for coverage of recommended guidelines, and alignment between the various components within a course, between sections of the same course, or course sequences within a program. The system works by facilitating mapping educational materials to national curriculum standards. A side effect of the system is that it centralizes the design of the courses and the materials used therein. The curriculum guidelines act as a lingua franca that allows examination of and comparison between materials and courses. More relevant to instructors, the system enables a more precise search for materials that match particular topics and learning outcomes, and dissemination of high quality materials and course designs. This paper discusses the system, and analyzes the costs and benefits of its features and usage. While adding courses and materials requires some overhead, having a centralized repository of courses and materials with a shared structure and vocabulary serves students, instructors, and administrators, by promoting a data-driven approach to rigor and alignment with national standards.

Academic computing departments are seeking ways to broaden participation, and many are encouraging individual faculty, staff, and students to attend diversity-oriented conferences, like the Tapia and STARS Celebrations of Diversity in Computing and Grace Hopper Celebration of Women in Computing. Such conferences present opportunities to meet a broader community of people for professional development and networking, to be inspired by leaders in computing, and to celebrate diversity. However, while many institutions sponsor these conferences and support individual student attendance, students may not know how to leverage these opportunities effectively. We argue that leading a cohort of faculty/staff and students who attend a conference with the shared goal of broadening participation can provide lasting benefits for computing departments. This workshop will prepare faculty and staff to recruit and lead a team of students and leverage conference attendance to ignite broadening participation efforts. Through a hands-on collaborative process, the workshop provides the knowledge and tools needed to successfully lead a cohort, and helps attendees tailor the provided tools to their local strengths and needs to broaden participation in computing.

This workshop provides instructors with a hands-on introduction to CS Materials (https://cs-materials.herokuapp.com/), a software infrastructure for designing and aligning a course to national curriculum guidelines, as well as to other courses in the curriculum. The workshop will detail the underlying principles behind CS Materials, namely, that classifying class materials against national curriculum guidelines enables better design and alignment of CS Courses, and permit accurate searches for materials that meet specific learning outcomes and/or topics. The CS Materials workshop will provide a hands-on experience to instructors to use the system to classify their own course content against the ACM IEEE CS 2013 guidelines and contribute to a shared public resource for CS educators. Workshop attendees will learn to input learning materials, perform topic coverage analysis, check alignment within their own course, as well as other courses in their curriculum, and search for new materials for use in their classes. Attendees should come with a laptop and have readied some class materials from a class they teach and want to share and/or analyze. The project provides stipends for instructor to enter, classify, and share one of their courses on CS Materials.

Grounding computer science concepts in real-world and socially relevant problems can be a key to increasing students' motivation and engagement in computing. BRIDGES provides an infrastructure for use in early CS courses that allows students to easily integrate real-world data into their routine course assignments and visualize the data and data structures of their implementations. The BRIDGES API provides several key advantages, First, minimal effort is needed to accessing and using interesting real-world datasets in CS homework assignments. Available data sets in BRIDGES span multiple domains, including entertainment, science, geographic data, and literature. Second, BRIDGES can be used by students to create and explore a visualization of the data used and data structures implemented in their assignments. Such visualizations can be used to illustrate concepts of underlying algorithm or data structure, or important data features. Third, the BRIDGES Game API allows students to implement 2D games, to emphasize basic concepts, such as control structures, looping and logic, while providing a fun experience. Finally, students can explore the benchmarking of algorithms in BRIDGES by using real and large data sets, emphasizing algorithm performance and computational complexity. Workshop attendees will engage in hands-on experience with BRIDGES with multiple datasets and will have opportunities to discuss how BRIDGES can be used in their own courses.

The service-oriented nature of the Universal Plug-and-Play (UPnP) protocol supports the creation of flexible, open, and dynamic systems. As such, it is widely used in Internet-of-Things (IoT) deployments. However, the protocol’s service access mechanism does not consider security from the first principles and is therefore vulnerable to various attacks. In this article, we present an in-depth analysis of the service advertisement, discovery, and access methods of the UPnP protocol stack and identify security issues in an IoT network. Our analysis shows that adversaries can perform resource exhaustion, buffer overflow, reflection, and amplification attacks by exploiting the vulnerabilities of the UPnP protocol. To address these issues, we propose a capability-based security model for UPnP to ensure secure discovery, advertisement, and access of the UPnP services that considers the resource limitations of IoT devices. Our analysis shows the effectiveness of the proposed model against potential attacks, and our experimental evaluation highlights the feasibility of implementing our Secure UPnP (SUPnP) protocol in a network of IoT devices, incurring minimal network and performance overhead.

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.

Early programming courses such as CS1 are an important time to capture the interest of students while imparting critical technical knowledge. Yet many CS1 courses are being taught using toy assignments and activities that tend to make students uninterested or doubt the usefulness of the content. In this poster, we demonstrate an enriching experience for students by coupling interesting datasets with visual representations and interactive applications, without having to change the content of that course. Our approach utilizes extensions to BRIDGES, an API in use for sophomore level CS courses for the past 5 years. BRIDGES provides easy access to external datasets and helps build interactive applications. The assignments we present are all scaffolded in a way that can be directly integrated into most early programming courses to make routine topics compelling and exciting.

Allyship in academia is critical for creating inclusive communities that are welcoming to all students, but the perception of its benefits and challenges can vary depending on a number of factors. This session will explore perspectives of allyship in academia by bringing together a diverse group of faculty and students who can share a wide range of experiences and insights, and aims to facilitate a discussion among all attendees that leads to an exchange of ideas, the strengthening of our community, and progress toward our common goal of inclusion in computing.

This workshop provides instructors with a hands-on introduction to BRIDGES, a software infrastructure for programming assignments in early computer science courses, including introductory programming (CS1, CS2), data structures, and algorithm analysis. BRIDGES provides capabilities for creating more engaging programming assignments, including: (1) a simplified API for accessing real-world data sets, including from social networks; scientific, government, and civic organizations; and movie, music, and literature collections; (2) interesting visualizations of the data, (3) an easy to use API that supports creation of games that leverage real-world data, and, (4) algorithm benchmarking. Workshop attendees will engage in hands-on experience with BRIDGES with multiple datasets and will have the opportunity to discuss how BRIDGES can be used in their own courses.

During the 2018 RESPECT Conference, a group of us facilitated a conversation about inclusion in computing. The discussion focused on critical issues of equity and inclusion and how to engage people from all groups in learning key concepts in computing. For this SIGCSE panel, we propose to continue the conversation through a broader conversation around the terms "broadening participation" and the roles of relationships, identity (cultural and personal), and academic, political, and professional perspectives when supporting equity and inclusion in computing education. This panel seeks to disrupt the conversation about computer science education, and participants to this session will leave having had a safe space in which to discuss issues of identify, race, and privilege that are critical to spaces like SIGCSE.

This workshop introduces participants to the concepts and use of BRIDGES, a software infrastructure for programming assignments in data structures and algorithms courses. BRIDGES provides two key capabilities, (1) easy to use interface to real world datasets spanning social networks, entertainment (movies on IMDB, song lyrics), scientific data (real-time USGIS Earthquake Data), civic issues (crime data), and literature (books); and (2) a visualization of the acquired data can be used in assignments by students to populate their implemented data structures, including the capability to bring out attributes of the dataset. The visualizations are displayed on the BRIDGES website and are easily shared (with family, friends, peers, etc) via a weblink. Workshop attendees will engage in hands-on experience with BRIDGES and multiple datasets and will have the opportunity to discuss how BRIDGES can be used in their own courses, as well as partner with the BRIDGES team.

In recent years, interactive textbooks have gained prominence in an effort to overcome student reluctance to routinely read textbooks, complete assigned homeworks, and to better engage students to keep up with lecture content. Interactive textbooks are more structured, contain smaller amounts of textual material, and integrate media and assessment content. While these are an arguable improvement over traditional methods of teaching, issues of academic integrity and engagement remain. In this work we demonstrate preliminary work on building interactive teaching modules for data structures and algorithms courses with the following characteristics, (1) the modules are highly visual and interactive, (2) training and assessment are tightly integrated within the same module, with sufficient variability in the exercises to make it next to impossible to violate academic integrity, (3) a data logging and analytic system that provides instantaneous student feedback and assessment, and (4) an interactive visual analytic system for the instructor to see students/ performance at the individual, sub-group or class level, allowing timely intervention and support for selected students. Our modules are designed to work within the infrastructure of the OpenDSA system, which will promote rapid dissemination to an existing user base of CS educators. We demonstrate a prototype system using an example dataset.

This workshop introduces participants to the concepts and use of BRIDGES, a software infrastructure designed to facilitate hands-on experience for solving traditional problems in sophomore level computer science courses (data structures, algorithms) using data from real-world systems that are of interest to students, such as social networks (Twitter, Facebook), scientific or engineering datasets (USGIS Earthquake data), Google Maps, etc. BRIDGES provides easy access (typically function calls) to real-world data sets for use in routine data structures programming assignments, without requiring students to work with complex and varied APIs to acquire such data. BRIDGES also provides visualization capabilities, allowing the students to visualize the data structure they have created as part of their assignment. BRIDGES visualizations can be easily shared, via a web link, with peers, friends, and family. Workshop attendees will engage in hands-on experience with BRIDGES and multiple data sets, and will have the opportunity to discuss how BRIDGES can be used to support various introductory computer science courses. A laptop with internet connection is required to participate in hands-on activities.

The Connected Learner is a re-orientation of undergraduate computing education that focuses on connecting students to peers, the profession, and purpose. The College of Computing and Informatics at UNC Charlotte—comprised of three departments with an undergraduate enrollment of approximately 1,500 students—is a large research institution in an urban setting with a diverse student population. Within this unique context, the project aim is to build a sustainable practice of educational innovation across the undergraduate computing curriculum by increasing faculty awareness of teaching innovations, resources for pedagogical change, and support for teaching practices that engage students. The vision is to create an active learning environment that transforms the student entering the computing undergraduate program from a person with an interest in computing to a person with an affinity identity [1] as a computing professional through these ongoing connections. We employ a systems theory of change for the Connected Learner based on two foundational concepts: flipped classrooms and engagement theory. To achieve related goals of improving student retention and time to graduation, Connected Learner teaching strategies are being integrated across the undergraduate curriculum with an initial focus on introductory gateway courses. Change is occurring via infrastructure supports to sustain learning practices across our college through faculty development initiatives (hiring, training, mentoring, and incentives) that are designed to inform faculty about engagement pedagogies, motivate faculty to adopt these practices, and shift pedagogical attitudes. During the first year of the five-year project, quantitative and qualitative data from students and faculty was collected and analyzed. This data provides a baseline of student attitudes and performance as well as a baseline of faculty attitudes and teaching practices. In this paper, we present the background, context, organizational structure, and research questions for the project. Findings from Year 1 are discussed, including academic outcomes, project milestones, and student attitudes towards new teaching practices. Long-term project goals and expectations are presented.

Increasing need for computing expertise in our everyday lives and in the workforce, paired with declining enrollments in computing by women and underrepresented minorities have made it critical to provide students with experiences in computing before college. CS10K is a national effort to engage 10,000 high school teachers in teaching computer science across the United States. With CS10K projects working on this goal since 2012 having prepared less than 1000 teachers for teaching computer science in high school, there is a need to scale professional development opportunities to local communities. College and university computing faculty have the unique preparation and call for community engagement that make it a win-win to support local high school teachers in learning to teach computer science. We will use the STARS model of building university-based communities that broaden participation in computing, adapted to the context of supporting K12 teachers to become change agents and educators prepared to teach computer science. In this workshop, participants will learn how to implement scalable team-based professional development for K12 teachers new to teaching computer science. We will provide resources to recruit, plan, and support small groups of new teachers to teach the new CS Principles course that will become an Advanced Placement course in 2016-2017. Laptops for attendees are optional.

Although undergraduate enrollment in Computer Science has remained strong and seen substantial increases in the past decade, retention of majors remains a significant concern, particularly for students at the freshman and sophomore level that are tackling foundational courses on algorithms and data structures. In this work, we present BRIDGES, a software infrastructure designed to enable the creation of more engaging assignments in introductory data structures courses by providing students with a simplified API that allows them to populate their own data structure implementations with live, real-world, and interesting data sets, such as those from popular social networks (e.g., Twitter, Facebook). BRIDGES also provides the ability for students to create and explore {\em visualizations} of the execution of the data structures that they construct in their course assignments, which can promote better understanding of the data structure and its underlying algorithms; these visualizations can be easily shared via a weblink with peers, family, and instructional staff. In this paper, we present the BRIDGES system, its design, architecture and its use in our data structures course over two semesters.

The STARS Computing Corps is a national alliance with the mission to grow a diverse community of computing leaders. STARS serves as a framework for integrating civic engagement into college computing departments with the goals of broadening participation of underrepresented groups in computing, recruiting K-12 students into the computing pipeline, and retaining students in computing majors. The STARS approach to broadening the participation of women and underrepresented minorities in computing is based on research that has shown the value of creating a community and sense of identity. The Corps creates such a community across multiple institutions, including women's and historically black colleges and universities, with members that share the core values of becoming responsible leaders who use their computing skills for social benefit. Each cohort of students and faculty at a STARS member university collaborates with local K-12 schools and industry partners to conduct computing-related outreach, service, and research that can broaden participation in computing. These local cohorts help to build community within and across STARS member institutions, retain students in college degree programs in computing, recruit new students into computing, educate local K-12 teachers, counselors, students, and parents about computing, and build bridges with local industry and community organizations. This workshop will provide hands-on training for new schools to learn how to begin and build a STARS Computing Corps cohort on their own campus.

This demo introduces participants to the concepts and application of BRIDGES, a software infrastructure designed to facilitate hands-on experience for solving traditional problems in introductory computer science courses using data from real-world systems that are of interest to students, such as Facebook, Twitter, and Google Maps. BRIDGES provides access to real-world data sets for use in traditional data structures programming assignments, without requiring students to work with complex and varied APIs to acquire such data. BRIDGES also helps the students to explore and understand the use of data structures by providing each student with a visualization of operations performed on the student's own implementation of a data structure. BRIDGES visualizations can be easily shared (via a weblink) with peers, friends, and family. Demo attendees will see (and possibly engage in) hands-on experience with BRIDGES and will have the opportunity to discuss how BRIDGES can be used to support various introductory computer science courses. Additionally, the demo will complement our oral presentation of our work at SIGCSE, by providing hands-on demonstrations of BRIDGES.

Participatory sensing is a powerful paradigm that takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users’ willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users’ active and reliable participation. In this article, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.

The demand for computing professionals in the U.S. workforce is expected to increase over the next several years, while the number of students intending to major in computing has declined. In this BOF session, we focus on the use of service learning to address the growing concern of creating a sustainable pipeline for computing professionals, with an emphasis on broadening participation in computing. We center the discussion around our experiences with the STARS Computing Corps, which engages college students in service-learning to address the national computing talent shortage. The model has been shown to be effective in retaining college students in computing, and STARS alumni credit participation in STARS with helping to develop professional skills. This session will include interactive small group discussions on goals, obstacles, and strategies for applying service learning to broaden participation in computing, grow the K-12 pipeline, and prepare college students with skills demanded in the 21st century workforce.

The vision of the Internet of Things (IoT) will enable networked environments populated with vast amounts of data that can be exploited by humans. The volume of digitally available data in such emerging computing spaces presents an imminent need for search mechanisms that enable humans and applications to find relevant information within their digitally accessible physical surroundings. This paper presents Gander, a search engine for these pervasive computing spaces enabled by the IoT and characterized by large volumes of highly transient data. Gander is founded on a novel conceptual model of search that resolves queries about a user's here and now by leveraging proximally available resources in the here and now. We formally describe the model underlying Gander, describe the networking protocols that enable Gander's search, and provide a realization of Gander via an extensible framework. Employing this Gander framework, we describe a concrete middleware implementation for wirelessly networked environments. We evaluate this implementation of Gander through a user study that examines the perceived utility of myGander, a real-world mobile application enabled by the Gander middleware, and we benchmark the performance of Gander in large pervasive computing spaces through network simulation.

Recent technological advancements have led to the popularity of mobile devices, which can dynamically form wireless networks. Unfortunately, mobile devices are vulnerable to failure because of various factors, including physical damage due to deployment in harsh environmental conditions, limited energy, and malicious attacks. Detecting node failure is an important problem that has been widely studied; recent attention has focused on determining failure when nodes are mobile. Detection of node failure requires additional messages to be sent across the network, which is costly in terms of energy consumption. We contend that fault detection algorithms should be designed with consideration of the tradeoffs between cost and accuracy of fault detection. In this paper, we present two approaches to dynamically adapting a fault detection algorithm. We compare our adaptive approaches to existing approaches and evaluate the tradeoffs between cost and accuracy.

Queries are convenient abstractions for the discovery of information and services, as they offer content-based information access. In distributed settings, query semantics are well-defined, for example, queries are often designed to satisfy ACID transactional properties. When query processing is introduced in a dynamic network setting, achieving transactional semantics becomes complex due to the open and unpredictable environment. In this article, we propose a query processing model for mobile ad hoc and sensor networks that is suitable for expressing a wide range of query semantics; the semantics differ in the degree of consistency with which query results reflect the state of the environment during query execution. We introduce several distinct notions of consistency and formally express them in our model. A practical and significant contribution of this article is a protocol for query processing that automatically assesses and adaptively provides an achievable degree of consistency given the operational environment throughout its execution. The protocol attaches an assessment of the achieved guarantee to returned query results, allowing precise reasoning about a query with a range of possible semantics. We evaluate the performance of this protocol and demonstrate the benefits accrued to applications through examples drawn from an industrial application.

Queries are convenient abstractions for the discovery of information and services, as they offer content-based information access. In distributed settings, query semantics are well-defined, e.g., they often satisfy ACID transactional properties. In a dynamic network setting, however, achieving transactional semantics becomes complex due to the openness and unpredictability. In this paper, we propose a query processing model for mobile ad hoc and sensor networks suitable for expressing a wide range of query semantics; the semantics differ in the degree of consistency with which results reflect the state of the environment during execution. We introduce several distinct notions of consistency and formalize them. A practical contribution of this paper is a protocol for query processing that automatically assesses and adaptively provides an achievable degree of consistency given the state of the operational environment throughout its execution. The protocol attaches an assessment of the achieved guarantee to returned query results, allowing precise reasoning about a query with a range of possible semantics.

The progression of component-based software engineering (CBSE) is essential to the rapid, cost-effective development of complex software systems. Given the choice of well-tested components, CBSE affords reusability and increases reliability. However, applications developed according to this practice can often suffer from difficult maintenance and control, problems that stem from improper or inadequate integrate solutions. Avoiding such unfortunate results requires knowledge of what causes the interoperability problems in the first place. The time for this assessment is during application design. In this paper, we define problematic architecture interactions using a simple notation with extendable properties. Furthermore, we delineate a multi-phase process for pre-integration analysis that relies on this notation. Through this effort, potential problematic architecture interactions can be illuminated and used to form the initial requirements of an integration architecture.