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.

Professional dispositions are important in numerous fields, including computing. However, strategies to promote dispositions are not yet well understood. In this experience report, we share what students suggest to promote five dispositions: being collaborative, meticulous, persistent, responsive, and self-directed. To get a broad perspective of students' voices, we gathered feedback over two terms in four courses at three institutions.

Dispositions are valued by employers and promoted by recent computing curricular recommendations. Yet, fostering and assessing dispositions are not well understood. In a multi-institutional study, students were asked to assess their dispositions in terms of behaviors that were identified in prior literature for those dispositions, both at the start and the end of a term. During the term, instruments were used to have students reflect on their dispositions. The research questions of the study are: 1) Do students associate behaviors with the dispositions for which they were identified in prior work? 2) Does reflecting on dispositions change how students assess themselves in terms of the behaviors? and 3) Is there a difference between introductory and upper-level students in how they assess themselves in terms of the behaviors? The findings of the study are that 1) at least 60% of the students associated the behavior statements with the dispositions for which they were identified; 2) students lowered their self-assessment of some behaviors after reflecting on dispositions; and 3) upper-level students assessed themselves more positively on some behaviors than introductory students. These results support a model of development of dispositions in which self-assessment of behaviors associated with dispositions improves with academic level, but at each level, gets revised lower after reflection.

Lifelong learning is essential in computing, given the dynamic nature of the field. Employers and curricular reviewers recognize the value of being self-directed in support of becoming a lifelong learner. The ACM/IEEE-CS Computing Curricula 2020 report identifies self-directed as having elements of self-motivation, determination, and independence. Little is known, however, about how to cultivate this disposition in computing courses. The motivation of this study is to better understand what behaviors computing students believe are self-directed. This study's research questions are: 1) What do students describe as their self-directed practices in computing? and 2) What do students report are factors that prevent them from being self-directed? Assignments in five undergraduate computing courses from four institutions included prompts to elicit student's reflections on how they were self-directed (or not). Thematic content analysis using the constant comparative method produced eight categories of self-directed behaviors (utilizing external resources, learning necessary material, working independently, assessing oneself, planning ahead, applying useful techniques, completing the assigned work, and reviewing against expectations). Thematic analysis also resulted in five categories of factors that impeded the self-directed behavior (assignment structure, unsuccessful effort, self-sufficiency, insufficient motivation, and insufficient time). Understanding how students describe self-directedness can help educators design pedagogical and assessment approaches that facilitate self-directed student behaviors in the classroom.

This full research paper contributes to current work on fostering professional dispositions in computing and engineering education by identifying the categories of behaviors that students associate with dispositions while doing course work. Professional dispositions, demonstrated through desirable behaviors in the workplace, such as being persistent or self-directed, are explicitly sought by employers. Fostering dispositions among students has been identified in various curricular recommendations as an important goal. In prior work, the authors used reflection exercises, in which students were presented with the definition of a disposition and asked to answer an open-ended reflection prompt on how they applied the disposition in their own work. Thematic analysis of student responses to reflection exercises resulted in categories of behaviors that students associated with dispositions. In the work discussed in this paper, the authors used vignette exercises to collect and analyze similar data and gain further insight into behavioral categories and students' perceptions of dispositions. Vignettes include short scenarios that demonstrate the application of dispositions in real life. A vignette exercise involves students reading a vignette scenario, identifying the disposition demonstrated by the scenario, and answering the same open-ended reflection prompt as in the reflection exercises from the earlier studies. The research question for this study is: Which behavioral categories obtained from analyzing student responses to reflection exercises were confirmed using vignette exercises (and which were not confirmed), and which behavioral categories were refined? To answer this question, researchers from four different institutions of higher education collected data in multiple courses over two semesters. The student open-ended responses to vignettes were thematically analyzed to identify behavioral categories for four dispositions: collaborative, meticulous, persistent and self-directed. The ultimate goal of this work is to create classroom inter-ventions and learning activities that foster dispositions among students based on behavioral categories. This study supports this goal in two ways. It provides another iteration of behavioral category analysis and introduces vignettes to encourage students to reflect candidly and communicate clearly how they apply dispositions in terms of behaviors. The study results and their implications for fostering dispositions in a classroom setting are presented and discussed.

The ITiCSE '23 final keynote raised teaching soft skills, or professional dispositions, to help students face challenges in modern programming. This project addresses helping computing students develop professional dispositions through collaborative learning (CL) since some in the industry observe entry-level engineers struggling due to their fragile professional dispositions. We are motivated to understand professional expectations from entry-level engineers and present the academia-industry gap to support practitioners and researchers in advancing CL in Computing Education, encouraging positive curricula and policy changes that promote DEIA. We will present CL practices alongside their supported professional dispositions to assist practitioners in adoption. We will present the academia-industry gap in CL for future research opportunities, helping researchers advance CL practices to integrate professional dispositions the industry expects from entry-level engineers.

Dispositions, skills, and knowledge form the three components of competency-based education. Moreover, dispositions are considered crucial for students to succeed in the workplace. Few studies investigate how dispositions manifest in the form of observable behaviors, which causes challenges for both students and educators. Computing students, for example, may not understand what is expected of them, and how to achieve dispositions. This paper presents the results of a qualitative, multi-institutional study on students' understanding of the dispositions adaptable, persistent, self-directed, meticulous, and professional. Perceptions were gathered by asking for exemplary situations of students applying each of the five dispositions in the context of assignments within computing courses. Students who indicated they did not apply the disposition were asked to describe the hindering circumstances. The data was evaluated by using Mayring's content analysis technique, resulting in the development of deductive-inductive categories of observable behaviors reflecting the student's perspective. For meticulous and professional, new categories representing observable behaviors were developed. For adaptable, persistent, and self-directed, the authors confirmed and extended prior work. Moreover, factors hindering students in applying the investigated dispositions are identified. The resulting categories with observable student behaviors are an important step toward the operationalization of competency-based learning outcomes including dispositions. A common understanding of dispositions will also help with the design of new forms of instruction and measures to foster the application of dispositions in the context of computing education.

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.

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.

Abstract There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.

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.

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.

Version control systems are widely used in industry and form a staple of modern software development, and this panel addresses the benefits and difficulties of introducing version control in education. Version control systems such as Git, and software development platforms such as GitHub, Bitbucket and GitLab are industry standards for which there is an increasing appeal within education. In general, students and teachers are motivated to use these tools in the classroom to increase graduates' competitiveness in the job market. Although highly desirable, the adoption of version control is hindered by a steep learning curve for both teachers and students, a lack of resources tailored to specific instructional and learning needs, as well as inadequate integration and alignment with the learning outcomes in the program curricula. The panel will analyze these challenges, solicit feedback from the audience's experience with version control tools, and facilitate discussions around promising strategies that could benefit student learning and graduates' professional competencies expected in the workplace.

Students often struggle with advanced computing courses, and comparatively few studies have looked into the reasons for this. It seems that learners do not master the most basic concepts, or forget them between courses. If so, remedial practice could improve learning, but instructors rightly will not use scarce time for this without strong evidence. Based on personal observation, program tracing seems to be an important pre-requisite skill, but there is yet little research that provides evidence for this observation. To investigate this, our group will create theory-based assessments on how tracing knowledge affects learning of advanced topics, such as data structures, algorithms, and concurrency. This working group will identify relevant concepts in advanced courses, then conceptually analyze their pre-requisites and where an imagined student with some tracing difficulties would encounter barriers. The group will use this theory to create instructor-usable assessments for advanced topics that also identify issues caused by poor pre-requisite knowledge. These assessments may then be used at the start and end of advanced courses to evaluate to what extent students' difficulties with the advanced course originate from poor pre-requisite knowledge.

About twice a year, typically at the start of a new semester, the SIGCSE community listserv comes alive with a discussion of tools for grading. This panel formed from a challenge to submit novel perspectives on grading. Of the dozen replies posted within 48 hours, the assembled panelists provide differing perspectives on the same point: let's return to building tools and methodologies for teaching rather than focusing on grading

Plagiarism is of great concern to faculty in all fields, including computer science as it leads to one certain outcome -- a compromise not just in student learning but also in the entire academic process. Faculty have tried to find ways to deal with this epidemic such as writing new course materials each semester, putting a larger or entire grade focus on exams, or even asking individual students to explain their assignments. However, plagiarism remains a source of frustration for both faculty and administrators. This BOF will bring interested faculty together to discuss the various and surprising ways in which students plagiarize, the methods of countering plagiarism, and the currently available tools for detecting plagiarism. Questions we will be discussing include: Do students understand plagiarism in the context of writing software? How can we develop an atmosphere that discourages plagiarism? Does such a thing as a "plagiarism-proof" assignment exist? If programmers go to online repositories, modify the code, and use it in professional programs then is it fair to expect the students to do just the opposite?

Many computer science programs have capstone experiences or project courses that allow students to integrate knowledge from the full breadth of their major. Such capstone projects may be student-designed, instructor-designed, designed in conjunction with outside companies, or integrated with ongoing free and open source (FOSS) projects. The literature shows that the FOSS approach has attracted a great deal of interest, in particular when implemented with projects that have humanitarian goals (HFOSS). In this article, we describe five unique models from five distinct types of institutions for incorporating sustained FOSS or HFOSS (alternatively H/FOSS) project work into capstone experiences or courses. The goal is to provide instructors wishing to integrate open source experiences into their curriculum with additional perspectives and resources to help in adapting this approach to the specific needs and goals of their institution and students. All of the models presented are based on sustained engagement with H/FOSS projects that last at least one semester and often more. Each model is described in terms of its characteristics and how it fits the needs of the institution using the model. Assessment of each model is also presented. We then discuss the themes that are common across the models, such as project selection, team formation, mentoring, and student assessment. We examine the choices made by each model, as well as the challenges faced. We end with a discussion how the models have leveraged institutional initiatives and collaborations with outside organizations to address some of the challenges associated with these projects.

Code quality is a key issue in software development. The ability to develop software of high quality is therefore a key learning goal of computing programs. However, there are no universally accepted measures to assess the quality of code and current standards are consideredweak. Furthermore, there are many facets to code quality. Defining and explaining the concept of code quality is therefore a challenge faced by many educators. In this working group, we investigate the perceptions of code quality of students, teachers, and professional programmers. In particular, we are interested in the differences in views of code quality by students, educators, and professional programmers and which quality aspects they consider as more or less important. Furthermore, we are interested in which sources of information on code quality and its assessment are used by these groups. Eventually, this will help us to develop resources that can be used to broaden students' views on software quality.

Plagiarism is of great concern to faculty in all fields, including computer science as it leads to one certain outcome -- a compromise not just in student learning but also in the entire academic process. Faculty attempt to deal with this epidemic in varied ways such as by writing new course materials each semester, putting a larger or entire grade focus on exams, or even asking individual students to explain their assignments. However, plagiarism remains a source of frustration for both faculty and administrators. This BoF will bring interested faculty together to discuss the various and surprising ways in which students plagiarize, the methods of countering plagiarism, and the currently available tools for detecting plagiarism. Questions we will be discussing include: Do students understand plagiarism in the context of writing software? How can we create an atmosphere that discourages plagiarism, yet fosters collaboration and encourages learning from multiple sources? To what extent can one make an assignment "plagiarism-proof"?

Patients with serious diseases, such as cancer, often must consider and choose between multiple clinical trials. Patients are often concerned that participation in one clinical trial may preclude their participation in another trial. We are developing a system to help patients in this decision making process, focusing on pediatric cancer where clinical trial participation is very high. In this paper, we present a detailed description of the approach used to process and analyze the eligibility constraints, using a pattern-based approach from the literature. We show how the semantic categorizations produced by these patterns can be used to determine which eligibility constraints can produce conflicts, as well as to guide an analysis algorithm. This work contributes to research on using eligibility criteria and other clinical trial information to better support patient decision making and to improve accrual of patients into clinical trials.

Many, if not most, computer science majors plan on careers in some aspect of software development in industry. Software development in industry is characterized by extensive use of tools and specialized software to support large scale projects, such as Git, Maven, Gradle, Jenkins, Hibernate, and Spring. We want our students to have exposure to industry strength tools and systems, but want to avoid having our courses overwhelmed by tools with steep learning curves. In this session, we plan to discuss questions and issues that are raised when choosing, installing, teaching, and using industrial strength tools and frameworks in software project courses. Bring your suggestions for favorite tools and frameworks, ideas on learning activities, and support materials. Some of the questions we might tackle: How do we identify tools that support our course learning objectives? How do we find the time and resources to learn the tools? How do we work with our IT staff in installing and configuring the tools? How many tools and systems should we use in a project course, and when do we merely start overwhelming the students' And finally, what works?

Many university software engineering/capstone courses attempt to teach students the importance of communication by having them work collaboratively on a large scale project. This can be a sink-or-swim approach. There is a real risk of complete project failure, and while students may learn that coordination is important, it isn't clear they learn how to do it effectively. The CoordinationTracker project uses social network analysis both to study coordination in student project teams, and to develop an automated tool to support students learning this skill. In this talk, I will briefly present some results using social network analysis and socio-technical congruence measures to study communication in a software engineering course, and then will describe the design of the proposed CoordinationTracker tool, which will use socio-technical congruence measures to give feedback to students. For example, as a student works on a file, an automated reminder capability will suggest who the student should be communicating with in order to prevent unwanted effects on components dependent on the file. The system also will have a reporting component which will allow each student, as well as the instructor, to view his or her coordination performance via graphs. Once CoordinationTracker has been implemented, it will be tested on several iterations of the software engineering course in order to determine whether students improve their coordination while using the tool. I am interested in audience feedback on the design of this tool as well as possible involvment with the project.

Patients with serious long-term disease often search for clinical trial information on their own, using sites such as ClinicalTrials.gov. We are developing a patient-focused search tool for clinical trials. Since patients are often concerned that participation in one clinical trial may preclude their participation in other trials, one of the planned features of this system will be the ability to analyze whether treatments in one trial will prevent a patient from participating in another. This paper describes an approach to this task. Eligibility criteria are classified according to linguistic pattern, UMLS concepts, content, and time. The criteria are then compared with treatments and side effects in other trials relevant to a patient, in order to identify potential conflicts.

No abstract available.

Coordination among developers has long been recognized as critical in software projects. One of the reasons for running large project software engineering courses is to teach students how to coordinate while working in a group; yet we have little understanding of how the students might fail or ways to assist them in learning this skill. Socio-technical congruence is a way to measure coordination based on the fit between communications among developers and the dependencies of the project. In this work-in-progress paper, we use a measure of socio-technical congruence to analyze the coordination in a student software engineering team. We found that congruence did not improve over time as has been shown for professional software teams. We then describe a proposed tool that uses socio-technical congruence measures to support and give advice to students who are learning how to effectively coordinate activities on a group project.

There has been an explosion of interest in bioinformatics, medical informatics, and healthcare informatics in the past decade. As a result, many computer science departments are developing courses or degree programs in bioinformatics and/or health informatics. This session is aimed at faculty who are teaching, or developing courses that tie together computer science and biology, medicine, or healthcare. The discussion leaders all have experience teaching courses in healthcare informatics and/or bioinformatics within computer science departments. We will share our expertise and experience on such issues as effectively team teaching interdisciplinary courses, developing case studies and projects, and developing links with biologists and clinicians. Some of the questions we might tackle: What types of courses should be included in degree programs? What role do curricular standards, especially in healthcare, play? What topics belong in interdisciplinary bioinformatics courses? How do we cope with students who may have differing backgrounds and prerequisites? The hope is that we can create an informal network for sharing ideas which will persist after the session. To this end, we will also discuss ways for maintaining a community, perhaps as a mailing list, blog, or website.

Effective communication among members of a software development team is considered to be a critical factor in the success of software projects. Social network analysis is a promising way to analyze communication patterns that has been used in a number of studies of professional software development teams. In this paper, we present preliminary data on communication events collected in a software engineering course and analyze it on a number of measures including basic social network measures. Successful groups are compared to an unsuccessful group on these measures. This is a work in progress report of a project to use social network analysis as a means of studying communication patterns among software engineering students, in order to improve teaching strategies.

The use of community-based projects has been recognized as having pedagogical and experiential value for computing majors (e.g. [3], [4], [5], [6]). Community-based projects can be valuable learning experiences for computing majors as well as for faculty and community partners. However, these types of projects do present challenges for faculty and should be aligned with desired course outcomes. This panel will discuss the use of community-based projects from multiple perspectives. The expectation is that the panel will serve as a forum for the participants to share the opportunities, challenges, pedagogical motivations, and best practices obtained from prior experience. Exemplar projects will be highlighted. The panel also hopes the session motivates the audience to share their own experiences and sparks the interest of those faculty members who may have no prior experience with community-based projects.

A description is given of WharfRat, a knowledge base of data type implementations which employs case-based reasoning as its primary retrieval mechanism. Given a description of an abstract data type, it retrieves the most similar data type implementation in the knowledge base. The focus of the study is the process by which two case descriptions are compared. Similarity between data types is modeled by a fuzzy relation. A set of similarity matching rules has been developed and implemented. The system employs a general, graph-based data model in which object types are organized in a specialization network. Abstract data representations are built using the constructs of the general data model. This system is the first step toward developing a complete programming-by-similarity system.>

A discussion is presented of the integration of analogical reasoning into a knowledge base system. Such a system would retrieve stored examples on the basis of similarity to a current problem. A data model that permits a case-oriented representation is developed. The cases are organized in memory using an indexing scheme that permits directed search. The search strategy is based on the goal of the problem. Incorporating this type of reasoning into a database will provide a powerful search strategy which does not require users to be familiar with the organization of the database.>