Computing educators strive to align curricula with workforce needs yet rarely articulate how professional competencies, behaviors, and responsibilities develop across different educational pathways. As the community moves beyond the 2027 curriculum revision cycle, bridge pathways spanning vocational, undergraduate, graduate, and non-computing entry routes are rapidly expanding. At the same time, widespread AI use requires curricula to more explicitly address professional judgment, accountability, and ethical reasoning, in addition to technical skills. This working group brings participants together to map, compare, and synthesize real curriculum and pathway artifacts using shared academic and workforce frameworks. Participants will build a competency-oriented pathway framework, identify recurring bridge pathway designs and gaps, and produce practical, post-2027 guidance that supports coherent, scalable, and internationally transferable computing curricula.

Several countries legally require computing programs to ensure their graduates can develop accessible computing solutions. Computing Science Curricula 2023 (CS2023), developed by a joint task force from ACM, IEEE-Computer Society, and AAAI, likewise mandates coverage of accessibility and provides guidance on how to integrate accessibility into computing curricula. Yet the accreditation criteria used by ABET's Computing Accreditation Commission (CAC) -- which accredits more than 650 computing programs in 25 countries -- do not currently require graduates from these programs to know about computing accessibility. Adding accessibility to the CAC accreditation criteria would be transformative in closing the growing gap of accessibility skills in the computing industry, and ensuring new computing technology is accessible to all.

What do computing industry practitioners—varying in age, experience, and culture—think of undergraduate computer science (CS) education? What do they value the most based on their own experiences and what do they see in the graduates they hire or work with? These were some of the questions addressed by a collaborative effort of eight universities across the US. This effort ultimately led to a collaboration with the Computing Research Association (CRA), which launched a national-scale Practitioner-to-Professor (P2P) Survey in 2024 that provides useful information for CS educators. Unlike broad all-major surveys, P2P focuses on CS with the detail needed for actionable feedback. This special session summarizes the results from the 2024 P2P administration, including several surprising outcomes, and engages participants to reflect on how the P2P results could help their programs improve their graduate readiness for long-term career success. Attendees will also learn how they can propose questions for future P2P surveys.

The academic preparation of computing graduates ready for successful long-term careers remains challenging due to a variety of factors, with most universities still unable to incorporate competencies into their education. Competencies refer to the knowledge, skills, and professional dispositions expected from these graduates during task performance. This panel presents three different perspectives on how work experiences may be incorporated into the program curriculum to develop the needed competencies.

A growing number of institutions choose to apply for and maintain ABET accreditation to distinguish their computing programs in terms of curriculum, academic rigor, and overall quality. Such programs need to track ABET accreditation criteria changes to ensure their programs remain continually compliant.

Computer Science Curricula 2023 (CS2023) was released in early 2024 by the Joint Task Force of the ACM, IEEE-Computer Society, and AAAI. Viewed from an educational perspective, CS2023 reflects the decennial changes in computer science (CS) since the release of the previous curricular guidelines, Computer Science Curricula 2013. CS2023 comprises curricular content and curricular practices: the former includes updates to the computer science knowledge areas and a competency model framework while curricular practices cover other aspects such as program design and delivery issues.

Modern society has a compelling need for computer science (CS) graduates to have sufficient knowledge and skills in Security. The recently released Computer Science Curricula 2023 (CS2023) by the ACM, IEEE Computer Society, and AAAI has a revamped Security knowledge area that inherently aims to make security unavoidable in a program curriculum. That is, by incorporating appropriate topics from the CS2023 Security Area, a CS program can prepare graduates capable of designing and developing secure computing infrastructure for modern societal functioning As there are challenges in modifying existing CS program curricula, proposing security-related changes is easier said than done.

Model curricula for baccalaureate computer science (CS) have been published regularly from 1968 through 2013. In early 2021, the ACM, IEEE-Computer Society, and the Association for the Advancement of Artificial Intelligence (AAAI) constituted a task force to revise these curricula, which have now been released as Computer Science 2023 Curricula (CS2023). The CS2023 curricular guidelines inform educators and administrators on the what, why, and how to cover undergraduate CS over the next decade. Like past guidelines, CS2023 provides curricular content - a knowledge model largely backward compatible with CS2013, supplemented by a competency framework influenced by Computing Curricula 2020 (CC2020) - and complementary curricular practices, which include articles by international experts on program design and delivery. Ongoing drafts of CS2023 were disseminated via the CS2023 website, along with regular publications or presentations at various computing education venues.

A joint task force of the ACM, IEEE-Computer Society, and AAAI has updated the computer science curricular guidelines last published in 2013. Included in the updated guidelines, referred to as CS2023, is a revised knowledge model and a new framework for building a customized competency model of the computer science curriculum. Acknowledging that one size does not fit all, the guidelines provide flexibility for computer science programs to structure their curriculum around the competency area(s) they wish to target. Given the pervasiveness of computing in everyday life, the guidelines emphasize the importance of society, ethics, and issues of the computing profession throughout the curriculum. The role of mathematics has been expanded in the guidelines, particularly in response to the latest developments in artificial intelligence. The curricular guidelines will be accompanied by articles written by experts on curricular issues such as ethics, computing for the social good, and accessibility. In the special session, the CS2023 curricular guidelines will be presented, and feedback solicited on facilitating their adoption and adaptation. The session is aimed at computer science educators, administrators, and professionals interested in computer science education.

A web-based learning platform is useful as it allows students with limited or no programming background to conduct in-depth hands-on practice in data science. Background: The need for data science coursework for non-computing majors has grown in recent years, given the demand in various disciplines. However, a substantial number of current data science courses are inappropriate for non-computing majors as they typically require a long chain of prerequisite courses in computer science and mathematics. Moreover, courses designed for computing majors do not match the preparation and interests of students majoring in other disciplines. Outcomes: This paper presents a platform for Learning Data Science (DSLP), a web-based platform, which assists in the teaching and learning of data science topics by students with limited or no coding experience, including those that have completed a high school AP Computer Science Principles (CSP) class or an equivalent CSP course increasingly offered in many colleges. Application Design: The platform helps students understand fundamental data science concepts and techniques, as well as provides them with an in-depth hands-on experience that goes beyond their coding capabilities. The platform offers various data visualization supports to help students understand data and analysis results. Students can use the platform to work on in-house datasets or their own data. This allows students to focus more on how to solve data science problems in various domains than how to write code. The platform also has several unique features that make it particularly helpful for teaching and learning data science topics such as code exemplification and sandbox, informative instructions, and progress monitoring. Findings: The platform has been used multiple times in data science courses for non-computing majors offered at the authors' institution. Preliminary student feedback indicated that the platform is effective in terms of improving student understanding and interest in the topics.

Vignettes are short stories along with a set of questions that engage the reader to comment on the story. Vignettes have been used in professional academic programs (e.g., teacher preparation and medical education), for professional development in various fields (e.g., teaching ethics in psychology and medicine), and in various research fields for data collection. In this work, vignettes are used to elicit students' understanding of dispositions in computing education. Professional dispositions enable behaviors that are valued in the workplace, such as adaptability or self-directedness. They are often explicitly stated in computing job postings. While the relevance of dispositions is widely recognized in the workplace, only recently have curricular guidelines for computing programs recognized professional dispositions as an integral part of competencies and as complementary to knowledge and skills. There is scarce literature on the use of vignettes in teaching undergraduate computing, or on how best to foster dispositions in students. In this project, four faculty from four diverse institutions in the U.S., along with three consulting experts, have collaborated to design and evaluate the use of vignettes in the classroom. This paper documents researchers' efforts to gain insights into students' perceptions of dispositions through the use of vignettes. Such insights may guide educators to identify pedagogical strategies for fostering dispositions among students. This paper presents an iterative process for vignette design with continuous review by researchers and focus group members. The vignettes in this study use stories of situations which demonstrate the application of a disposition, drawn from various fields and walks of life to represent diverse groups and experiences. Students are presented with the vignette story and asked to identify the disposition illustrated. To elicit students' understanding of dispositions in terms of their personal behaviors, students are asked to describe a situation in which they have experienced the disposition. Lessons learned in the design and use of vignettes are discussed.

Dispositions, along with skills and knowledge, form the three components of competency-based education. Moreover, studies have shown dispositions to be necessary for a successful career. However, unlike evidence-based teaching and learning approaches for knowledge acquisition and skill development, few studies focus on translating dispositions into observable behavioral patterns. An operationalization of dispositions, however, is crucial for students to understand and achieve respective learning outcomes in computing courses. This paper describes a multi-institutional study investigating students' understanding of dispositions in terms of their behaviors while completing coursework. Students in six computing courses at four different institutions filled out a survey describing an instance of applying each of the five surveyed dispositions (adaptable, collaborative, persistent, responsible, and self-directed) in the courses' assignments. The authors evaluated data by using Mayring's qualitative content analysis. The result was a coding scheme with categories summarizing students' concepts of dispositions and how they see themselves applying dispositions in the context of computing. These results are a first step in understanding dispositions in computing education and how they manifest in student behavior. This research has implications for educators developing new pedagogical approaches to promote and facilitate dispositions. Moreover, the operationalized behaviors constitute a starting point for new assessment strategies of dispositions.

In Fall 2021, a preliminary study was conducted to gain insight into students' perceptions of the importance of professional dispositions to their computing courses and career. Students filled out a pre-survey, post-assignment reflection exercises, and a post-survey. We found that 1) students rated dispositions as being maximally important for the course and their career on the pre- and post-surveys; 2) students rated dispositions not relevant to the course lower than those that were relevant; and 3) students rated their application of dispositions in course assignments lower than they had rated the importance of the dispositions for success in the course in the pre-survey.

A Joint Task Force of the ACM, IEEE-Computer Society, and AAAI commenced work in 2021 to revise the Computer Science curricular guidelines that were last updated in 2013. Planned for publication in 2023, the revised guidelines (CS2023) cover curricular content and curricular practices. Curricular content includes updates to the CS2013 knowledge areas, a new sunflower model of what constitutes core computer science topics, a proposal for packaging knowledge areas into courses, and a competency model of the curriculum. Curricular practices cover computer science program design and delivery issues, including social aspects, professional practices, and programmatic considerations. In the special session, the latest CS2023 draft will be presented and feedback solicited. The session is targeted toward educators, administrators, and professionals interested in computer science curricular issues.

Dispositions are cultivable behaviors desirable in the workplace. Examples of dispositions are being adaptable, meticulous, and self-directed. The eleven dispositions described in the CC2020 report should not be confused with the professional knowledge of computing topics, or with skills, including technical skills, along with cross-disciplinary skills such as critical thinking, problem-solving, teamwork, or communication. Dispositions, more inherent to human characteristics, identify personal qualities and behavioral patterns important for successful professional careers.

In the past decade, academic computing curricular guidelines have shifted from specifying knowledge and occasionally technical skills to establishing the overall competence expected of graduates. For instance, Computing Curricula 2020 (CC2020) guidelines identify competency as knowledge, skills, and dispositions where “dispositions” correspond to the behavioral and professional characteristics driven by employer needs and captured by industry-driven frameworks, such as the Skills Framework for the Information Age (SFIA). Computing programs thus must also ensure that graduates have these characteristics to improve initial employment and long-term career prospects. This paper aims to understand and achieve consistency between academia and industry curricular frameworks. The CC2020 dispositions map to the responsibility characteristics for SFIA Level 3, the level appropriate for a new graduate. As the mapping is not one-to-one, the paper reviews the extent to which each SFIA responsibility characteristic requires and enables the CC22020 dispositions, identifying potential shortcomings and, conversely, the importance of each disposition as it supports the responsibility characteristics. The developed mapping is validated by relating the CC2020 dispositions to the SFIA behavioral factors, the principal 21st Century Skills and relevant competency-based educational frameworks. Thus, dispositions in competency-focused curricula map to the actual competencies sought by employers. Finally, the paper postulates that future computing curricula must further develop the CC2020 dispositions and relate them to SFIA to guide academic programs in their preparation of career-ready graduates to reduce the current “skills gap”.

This Innovative Practice Full Paper addresses the assessment of dispositions which, along with knowledge and skills, form the three legs of competency needed to perform a task in context, as described in recent computing curricular reports, particularly ACM/IEEE Computing Curricula 2020 (CC2020). Here, dispositions in CC2020 express the behavioral characteristics of competence, such as being adaptable, collaborative, or inventive. Instructors have assessed knowledge from the start of computing programs and have paid increased attention to assessing skills in recent decades. However, dispositions and their role within competency is relatively new, with little guidance available for assessing dispositions. Lately, computing instructors have begun to understand the importance of evaluating dispositions during the performance of tasks in the real world or in the context of the industry-based global Skills Framework for the Information Age (SFIA). Hence, this paper develops a criterion-based approach for use by educators in assessing competence based on a reflective portfolio of "real-world" achievements. Building on concepts developed by the UK Institute of Coding and other recent reports, this work demonstrates how this assessment approach relates to industry-based competency frameworks such as SFIA and the European e-Competence Framework (eCF). The paper also explores using the criterion-based approach in a classroom environment to help students focus on particular dispositions. Its main contribution is to advance the competency focus in academic computing programs and future computing curricula.

No abstract available.

Professional accreditation in medicine and religious organizations started in the 1800s; business and engineering followed in the early 1900s. Program accreditation in computing commenced in the 1980s after computer science, informatics, and information systems programs had become widespread. In 2008, accrediting bodies in eight countries signed the Seoul Accord to set up mutual recognition of professional computing degrees. Although competency-based learning has existed for centuries, it has only made headway in computing in the last dozen years. Computing Curricula 2020 defined competency as the amalgamation of knowledge, skills, and dispositions. This working group report examines professional accreditation in computing, exploring aspects of professional accreditation criteria that support competency-based learning. The report will help educators understand professional accreditation and competency-based learning worldwide. Finally, it will also guide future efforts contributing to competency-based accreditation.

A Joint Task Force of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers~-~Computer Society (IEEE-CS), and Association for the Advancement of Artificial Intelligence (AAAI) was constituted in early 2021 to begin the decennial process of revising the Computer Science curricular guidelines, which were last released as Computer Science Curricula 2013 (CS2013). This special session will present the first draft of the revised curricular guidelines, currently referred to as CS202X, and solicit feedback. The CS202X draft will include revisions to CS2013 Knowledge Areas, a proposed competency model being incorporated into the curricular guidelines, and other updates. Targeted towards educators, administrators and others interested in Computer Science curricular issues, this session will be led by the co-chairs and members of the CS202X Steering Committee as part of their process to engage the community and solicit feedback.

The presenters will demo a web-based Data Science Learning Platform (DSLP) that makes data science education accessible to students with limited or no programming background. The DSLP platform offers students with several benefits such as: (1) learn a web-based user interface to perform data science tasks without requiring coding, (2) explore popular Python data science libraries (e.g., Pandas, Matplotlib, Numpy, or Scikit-Learn) through real-time code exemplification to prepare them for advanced data science topics, (3) become familiar with the on-site user guide and helpful tips to make the platform easy to use, (4) write their own code within a sandbox, and (5) monitor their own progress by tracking their platform usage. The demo will walk through the steps of using the DSLP to perform various data science tasks and the participants will be able to try out the features mentioned above. The demo will also cover the design of course materials, including hands-on practices and lab assignments using the DSLP platform. The typical participants include instructors who are interested in teaching introductory-level data science to high school students or non-computing college majors with little or no programming background. Participants need to have a laptop with access to the Internet to attend the hands-on exercises workshop. The laptop should have a current web browser (e.g., Safari or Chrome) installed to access the web-based learning platform. This demo describes work supported by the National Science Foundation under Award 2021287.

Data science knowledge and skills have become indispensable to STEM and non-STEM disciplines alike. As a result, it has become crucial for students in non-computing majors to learn data science techniques, particularly in the context of their own disciplines. A majority of current university data science coursework, however, requires sufficient depth in programming and statistical skills related to managing, manipulating, and analyzing data, which reduces their usefulness for entry-level non-computing majors. This workshop presents a set of hands-on exercises to introduce data science to entry-level non-computing majors. The exercises cover the data science lifecycle, including data acquisition, preparation, model development and deployment, visualization, and storytelling. A freely-available web-based Data Science Learning Platform (DSLP) will be presented to show how to perform hands-on data science exercises with little or no coding background. The presenters will also share their experiences in using the DSLP tool in an entry-level data science course to non-computing majors at RIT. Both the tool and course materials will be shared with workshop participants. The typical workshop participant is a high school teacher or a college instructor interested in teaching data science at the introductory level. No prior programming or data science experience is needed, thus making the workshop materials usable by a wide audience. Participants need to have a laptop with access to the Internet to attend the hands-on exercises workshop. The laptop should have a current web browser (e.g., Safari or Chrome) installed to access the web-based learning platform. This work was supported by the National Science Foundation under Award 2021287.

Since the early 21st century, ABET's accreditation criteria have focused on learning outcomes (what students learn) rather than what professors teach. Such accreditation criteria bring to bear the need for programs to establish clear learning objectives and assessment processes that ensure that program graduates have the requisite technical and professional preparation. To this end, ABET defines student outcomes as "what students are expected to know and be able to do by the time of graduation," further noting that these outcomes "relate to the knowledge, skills, and behaviors that students acquire as they progress through the program." With the recent release of Computing Curricula 2020 (CC2020), the competencies of computing program graduates have received additional attention. CC2020 describes competency as "comprising knowledge, skills, and dispositions that are observable in accomplishing a task within a work context."

Competency-based learning has been a successful pedagogical approach for centuries, but only recently has it gained traction within computing education. Building on recent developments in the field, this working group will explore competency-based learning from practical considerations and show how it benefits computing. In particular, the group will identify existing computing competencies and provide a pathway to generate competencies usable in the field. The working group will also investigate appropriate assessment approaches, provide guidelines for evaluating student attainment, and show how accrediting agencies can use these techniques to assess the level of competence reflected in their standards and criteria. Recommendations from the working group report are intended to help practical computing education writ large.

Prompted by the skyrocketing demand for data scientists, progress made by the ACM Data Science Task Force on defining data science competencies, and inquiries about data science accreditation, ABET is in the process of developing accreditation criteria for undergraduate data science programs. The effort is led by members of a joint data science criteria subcommittee appointed by ABET's Computing Accreditation Commission (CAC) and CSAB (the lead society for computing accreditation). Establishing data science accreditation criteria is a notable milestone in the maturing data science discipline, indicating the presence of an accepted body of knowledge, standards of practice, and ethical codes for practitioners. This position paper motivates the effort and discusses prior work towards defining data science education requirements. It describes the ongoing process for creating and obtaining approval of the accreditation criteria, and how feedback was and will be solicited from the computing and statistical communities. The current draft data science criteria, which was approved in July 2020 by the relevant ABET bodies for a year of public review and comment, is presented. These criteria emphasize the three pillars of data science: computing foundations, mathematical/statistical foundations, and experience in at least one data application domain. This report thus serves both to inform and to stimulate the academic discussion needed to finalize appropriate data science accreditation by ABET.

In recent years, all computing disciplinary communities and curricular guidelines have increased their expectations of and requirements for incorporating cybersecurity into their discipline. For computer science, this has been a daunting task for a number of reasons, including the fast-paced evolution and expansion of the discipline, the perceived challenge of finding space in the curriculum, and the difficulty of selecting the best content. This paper takes the position that infusing security concepts pervasively into an undergraduate Computer Science program is a crucial and attainable best practice. A five-step methodology is presented to incorporate cybersecurity into a traditional computer science curriculum in a way that maintains disciplinary integrity without adding significant new curricular content. This methodology is consistent with the philosophy and recommendations of the latest computer science and cybersecurity curricular guidelines. The paper also illustrates the application of these techniques to a typical Computer Science program.

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project.

Over the past two decades, data science or data analytics degree programs have begun to emerge, reflecting the world's demand for data specialists to make sense of the vast amounts of collected data in the sciences, engineering, business, and other domains. As degree creation has occurred mainly due to demand, ACM and other professional bodies have recently stepped in to provide curricular guidance. However, no \em shared global framework for data science as an academic discipline exists, making growth unfocused and driven by employer demands. More recently, the growth of artificial intelligence has also impacted data science programs. This working group builds on prior efforts and participant experiences to develop a global taxonomy of approaches to data science education and expectations for graduates of data science programs to \em think like data scientists.

This session is designed to attract participants with an interest in post-secondary academic cybersecurity programs. Despite the extraordinary interest in cybersecurity due to the job market, relatively little consensus exists on how to design academic programs. Divergence on issues such as degree title, program objectives and outcomes, program scope, whether only one distinct discipline of cybersecurity exists or several, whether a separate academic unit is needed to offer such degrees, and what role other (non-computing) disciplines should play in cybersecurity programs. Prior efforts in defining cybersecurity at the collegiate level include the US Department of Homeland Security and the US National Security Agency's Centers for Academic Excellence (CAE) programs, NIST's National Initiative for Cybersecurity Education (NICE), the Cyber Education Project, the Joint Task Force's CSEC 2017 draft curricular guidelines, and ABET accreditation criteria for cybersecurity programs. The audience is expected to consist of college faculty and administrators who are either in the process of starting cybersecurity programs or are seeking guidance for developing security content for other computing programs that must now teach security. This session will allow participants to share ideas regarding how to develop and teach cybersecurity in four-year, post-secondary academic programs.

The Curricular Guidelines for Post-Secondary Degree Programs in Cybersecurity (CSEC 2017) released by the Joint Task Force of the ACM, IEEE Computer Society, AIS and IFIP have begun to define the academic field of cybersecurity. Increasing numbers of institutions are now offering full-fledged undergraduate cybersecurity degree programs. However, as the CSEC 2017 guidelines do not define what constitutes a standalone undergraduate cybersecurity degree, the computing community needs to agree on parameters of what such a degree ought to be. To this end, ABET has created flexible accreditation criteria for cybersecurity programs guided extensively by CSEC 2017 and community input. These criteria were used to conduct pilot accreditation visits of four distinct cybersecurity programs in 2017-18. This panel discusses benefits, challenges, and costs of cybersecurity program accreditation. The panelists, who represent the first four ABET-accredited cybersecurity programs, will discuss major curricular features of their programs, their motivation for participating in the pilot accreditation, the effort in preparing for accreditation, and the benefits anticipated both for their programs and the larger computing community due to program accreditation

Global cybersecurity crises have compelled universities to address the demand for educated cybersecurity professionals. As no shared framework for cybersecurity as an academic discipline exists, growthhas been unfocused and driven by training materials, which make it harder to create a common body of knowledge. An international perspective is still harder, as different nations use different criteria to define local needs. As a result, new programs entering this space are on their own to conceptualize, design, package and market their programs, as there is no globally accepted reference model for cybersecurity to allow employers or students to understand the extent of a given cybersecurity program.

The Computing Accreditation Commission of ABET accredits over 300 Computer Science programs worldwide under the Computer Science program criteria. In the past few years, the Commission has engaged the computing community to modify and update these criteria, and has now signed off on an updated set of program criteria that take into account factors such as the CS2013 curricular guidelines, impact on currently accredited programs, structural changes to clarify the criteria, and the reduction of the assessment burden. These recent changes to the Computer Science program criteria primarily impact student outcomes (what program graduates are expected to know and be able to do by graduation) and curriculum. Those changes will impact computer science programs in a variety of ways and degrees---some programs will be impacted significantly while others will be affected in a relatively minor way. This paper examines the changes that will take effect starting in the 2019-20 accreditation cycle, covering the rationale for those changes and exploring the likely impact on computer science programs that are currently accredited or seeking accreditation in the near future.

Due to the ongoing demand for cybersecurity professionals, universities have begun to step up to the challenge of providing degrees in cybersecurity or related disciplines. However, growth in academic programs has been unfocused, in part because there is no standard definition of what a cybersecurity undergraduate program should contain and no cohesive community of practice with which those programs are aligned. This session is intended to establish dialog intended to lead to a community of practice for undergraduate cybersecurity education. The presenters will briefly review prior efforts in defining cybersecurity at the collegiate level, including the Centers for Academic Excellence (CAE), National Initiative for Cybersecurity Education (NICE), Cyber Education Project, CSEC2017 curricular guidelines and ABET cybersecurity accreditation. Session participants will examine current efforts, explore the development of a cybersecurity community of practice, and get clarity about where their own efforts fit into overall computing education.

The demand for cybersecurity professionals is projected to grow substantially, with the US Bureau of Labor Statistics reporting that employment in cybersecurity within the US will grow by 18% from 2014 to 2024, much faster than the average for all occupations. As creating a cyberspace workforce has become a matter of national security for every country, cybersecurity needs to be taught at all levels, to all students, in the educational system. The good news is that cybersecurity is also a topic that students from a wide variety of backgrounds find interesting, and as a result, it motivates them to study computing too. Over the past two decades, there has been an increased effort worldwide to incorporate computer science and computational thinking into the middle and high school curriculum. The CS10K initiative in the US has led to projects to introduce computer science at the K-12 educational level. One of these initiatives, the new Advanced Placement (AP) course in Computer Science Principles (CSP), was designed to introduce computer science in an engaging way, show students how computing is relevant in their lives, and to attract a diverse group of students to computing. The CSP Curriculum Framework allows for multiple implementations of the CSP course, permitting course designers to develop courses to engage and attract specific groups of students and that focus on specific themes in computing. This paper describes an approach to develop a new CSP course, CyberCSP, which integrates cybersecurity first principles throughout the course. The approach builds on an CSP course that was created from a previous collaboration between the Computer Science Department at Rochester Institute of Technology, Rochester, New York, and the Webster Central School District in Webster, New York. The paper discusses the background, details of the earlier CSP course, how relevant cybersecurity content was identified, and then integrated into the CSP course to create the CyberCSP variant of the Computer Science Principles course.

As the worldwide demand for cybersecurity-trained professionals continues to grow, the need to understand and define what cybersecurity education really means at the college or university level. Given the relative infancy of these efforts to define undergraduate cybersecurity programs, the panelists will present different perspectives on how such programs can be structured. They will then engage with the audience to explore additional viewpoints on cybersecurity, and work toward a shared understanding of undergraduate cybersecurity programs.

The United States Department of Homeland Security has identified 16 critical infrastructure sectors that employ computing, technology and engineering students. However, most undergraduate curricula in these disciplines do not incorporate the fundamentals of critical infrastructure protection (CIP) into their curricula in a meaningful way. This paper describes the design, development, and usage of a modular curricular framework for integrating CIP into undergraduate programs via self-contained interdisciplinary course modules; a course module is a distinct curricular unit such as a lab or teaching component for use by an instructor in existing courses without requiring any course or program modifications. The framework is designed for use in multiple disciplines, and the modules are designed for presentation at different levels of the undergraduate experience, with subsequent modules built on those presented earlier. In addition, the paper discusses assessment results obtained from the validation of the framework and modules over the past three years that covered 345 students at the community college and university levels.

No abstract available.

Robots are typically used at the college level either as a pedagogic platform for introductory programming or for more advanced courses in robotics. With robots becoming cheaper and more plentiful, personal interactions with them will become more commonplace. This project therefore takes the position that undergraduate computing students need the opportunity to explore core computing concepts in a robotics context. Specifically, we will give students the ability to work alongside teams of highly capable and easily programmable corobots, a term used to identify robots that work side by side with humans, rather than being completely autonomous and isolated. A modular approach is used to incorporate corobotics into various computer science (CS) courses such as first-year computing, networking, and data management, thus permitting the students to see these corobots in multiple contexts. This work-in-progress paper describes the corobotics infrastructure that has been developed, and outlines how this infrastructure can be used to support diverse courses in the CS curriculum.

Service-Oriented Programming (SOP) is a relatively new programming paradigm that supports the development of new software applications using existing services as building blocks. SOP has gained significant popularity in industry as it increases software reuse and productivity. As the SOP paradigm can improve modern software development, the presenters have created a course-module based approach for incorporating SOP into Computer Science (CS) and Software Engineering (SE) curricula; a course module is a distinct curricular unit such as a lab or teaching component that an instructor may incorporate into an existing course typically without requiring formal curricular approval. SOP course modules have been developed for inclusion in standard courses in many CS and SE programs; for example, an introductory SOP course module in a CS2 course while advanced modules for courses such as Programming Language Concepts, Software Engineering, or Web Services. This workshop will present basic concepts and techniques of SOP and describe how the course-module approach toward SOP can be adapted for the participants' own teaching. The typical participant would be a faculty member with some background in programming, and is interested in learning more about SOP but does need not to have prior web service programming experience.

Service-Oriented Programming (SOP) has emerged as a new programming paradigm that allows the wrapping of existing software as web services, thus permitting the development of new software applications by using existing web services as building blocks. SOP has attracted great attention from industry as it dramatically increases software reuse. Despite the growing demand for an SOP-trained workforce, SOP has not been adequately covered in coursework for undergraduate students in Computer Science (CS) and Software Engineering (SE). This project addresses this curricular shortcoming via the design and creation of SOP materials for undergraduate CS and SE. The concept of course modules-self-contained units of instruction that can be incorporated into several existing courses-is used to make these materials accessible at multiple educational institutions. This paper describes an exemplification and visualization framework that supports the teaching of SOP, along with three course modules that can be folded into typical courses currently offered to CS or SE undergraduates.

The 16 critical infrastructure sectors identified by the US Department of Homeland Security employ many engineering, technology and computing graduates who increasingly face critical infrastructure protection (CIP) issues. However, most undergraduate curricula in these disciplines do not incorporate CIP in any meaningful way. This paper proposes a flexible curricular framework for integrating CIP into undergraduate education via self-contained inter-disciplinary CIP course modules; a course module is a distinct curricular unit such as a lab or teaching component for use by an instructor in existing courses without requiring any course or program modifications. The proposed course modules cover physical, human, and cyber aspects of CIP. The framework is designed for use in multiple disciplines, and the modules are designed for presentation at different levels of the undergraduate experience, with subsequent modules building on those presented earlier. This approach is intended to prepare students for careers solving problems in design, implementation, and maintenance of robust, sustainable infrastructure assets.

Many diverse domains-in the sciences, engineering, healthcare, and homeland security-have been grappling with the analysis of “Big Data,” which has become shorthand to represent extremely large amounts of diverse types of data. A recent Gartner report predicts that around 4.4 million IT jobs globally will be created by 2015 to support Big Data, with 1.9 million of those jobs in the United States. Therefore, understanding approaches and techniques for handling and analyzing Big Data from diverse domains has become crucial for not only in computing but also engineering students. The mini-workshop will make use of active and collaborative learning exercises to introduce faculty in computer science, software engineering, and other disciplines to concepts and techniques involved in managing and analyzing Big Data. Approaches for incorporating Big Data into the engineering and computing curricula will also be presented.

This paper presents strategies for providing successful Research Experiences for Undergraduates (REU). The authors have advised several undergraduates on research for the past few years, and have jointly supervised around twenty-five students, over two summers, on a project funded by an NSF-funded REU program in areas relating to the visualization of astrophysical data using high performance file systems. Several of these student projects have led to research publications. The paper briefly motivates the need for research in modern computing and engineering education. It then presents specific details about the development of summer REU programs including how to: secure funding and institutional support; plan a summer program including the design of scalable research projects; develop strategies to advertise and recruit students, especially from underrepresented groups; create a dynamic research and social environment through one-on-one mentoring; develop appropriate assessment and evaluation processes; and track student participants after they graduate from the program.

In the modern world of computing, diverse mobile devices connect to the internet and typically share data stored within the cloud. To live and succeed in this world, it is imperative that all majors, especially engineering majors, have an in-depth, not introductory, understanding of topics in mobile web computing. This paper explores the design and development of an immersion concentration of courses in these topics as part of general education at our institution. The concentration consists of courses that introduce hands-on programming of mobile devices; the issues in and the impact of architectural and distributed computing; and finally secure data management, privacy, and legal issues in mobile web computing. All of these courses focus on developing critical thinking, modern communication, contemporary issues, and life-long learning issues in the context of hands-on mobile computing. This work-in-progress paper motivates this effort, discusses the main features of the proposed courses, outlines some of the challenges being faced, and summarizes the current status of this ongoing effort.

Distance learning has been one vehicle of course delivery at the Rochester Institute of Technology since the early 1990s. Building on the authors' experience in teaching completely online and blended (partly on-campus and partly online) courses to both undergraduate and graduate students, in diverse areas of computing, engineering, and technology, this paper explores issues in delivering online classes in these disciplines. One particular focus is on conveying the traditional in-class laboratory experience to a similar one in the online world while another focus is to leverage the modalities available in online to create newer and richer experiences for students and instructor. The paper presents the challenges and pitfalls in totally asynchronous learning, from assessment to assignments, from lectures to labs, and from participation to plagiarism. For each challenge, the authors provide examples of practices that succeeded, building on experience with those that did not. To place this work in perspective, the paper also examines related work in online computing and engineering education.

Everyone is building a distributed systems these days, but the "how" has become a matter for religious debate and the "why" is often neglected completely.This panel brings together a variety of practitioners to explain the joys and woes of building a distributed system including: .why a distributed solution was chosen .what middleware was selected and why .what issues and gotchas reared their ugly heads and what was done about it

Article Free Access Share on Developing an undergraduate software engineering program in a liberal arts college Authors: Paul T. Tymann State University of New York, Oswego, New York State University of New York, Oswego, New YorkView Profile , Douglas Lea State University of New York, Oswego, New York State University of New York, Oswego, New YorkView Profile , Rajendra K. Raj State University of New York, Oswego, New York State University of New York, Oswego, New YorkView Profile Authors Info & Claims SIGCSE '94: Proceedings of the twenty-fifth SIGCSE symposium on Computer science educationMarch 1994Pages 276–280https://doi.org/10.1145/191029.191143Published:12 March 1994Publication History 4citation146DownloadsMetricsTotal Citations4Total Downloads146Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Abstract Emerald is a general‐purpose language with aspects of traditional object‐oriented languages, such as Smalltalk, and abstract data type languages, such as Modula‐2 and Ada. It is strongly typed with a non‐traditional object model and type system that emphasize abstract types, allow separation of typing and implementation, and provide the flexibility of polymorphism and subtyping with compile‐time checking. This paper describes the Emerald language and its programming methodology. We give examples that demonstrate Emerald's features, and compare and contrast the Emerald approach to programming with the approaches used in other similar languages.

Emerald is a strongly-typed object-oriented language designed for programming distributed applications. Among other things, it provides abstract typing, type conformity, and complete separation of typing from implementation. While Emerald supports type inheritance, it does not support behaviour sharing among objects for simplifying distribution. To increase Emerald's utility in general-purpose programming, some support for software re-use is needed. Our research reveals that inheritance-based techniques commonly used in other object-oriented systems for obtaining re-use are inappropriate for Emerald. As an alternative to traditional inheritance, a compositional model, in which objects are composed from simpler entities, is proposed, outlined and analysed in this paper.