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Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community. 

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. 

This working group aims to identify available datasets within the context of computing education research. One particular area of interest is programming education, and the data in question may include students' steps, progress, or submissions in the form of program code. To achieve this goal, the working group will review well-known data resources and repositories (e.g., DataShop, GitHub, NSF Public Access Repository, and IEEE DataPort) and recent papers published within the SIGCSE community. As a result of the review process, the working group will create an overview of available datasets and characterize them while reflecting on current data practices, challenges, and the consequences of limited access to research data. Additionally, the group intends to propose a path for the community to become more open and move toward open data practices. This proposal highlights the importance of sharing research data within the computing education research community to make it stronger and more productive. 

The Computing Curricula 2020 (CC2020) report, issued by the ACM and IEEE Computer Society, identified knowledge, skills, and dispositions as the three main components of competency for undergraduate programs in computer engineering, computer science, cybersecurity, information systems, information technology, and software engineering, as well as data science. As earlier generations of curricular guidelines in computing have described knowledge and skills to some extent, the notion of dispositions is relatively new to computing. Dispositions are cultivable behaviors, such as adaptability, meticulousness, and self-directedness, that are desirable in the workplace. Multiple employer surveys and interviews confirm that dispositions are as crucial for success in the workplace as the knowledge and skills students develop in their academic programs of study. As such, the CC2020 report describes eleven dispositions that are expected of competent computing graduates. These are distinct and separate from the technical knowledge and disciplinary skills of computing and engineering. Dispositions are also distinct from baseline or cross-disciplinary skills, such as critical thinking, problem-solving, teamwork, and communication. In contrast, dispositions are inherently human characteristics that describe individual qualities and behavioral patterns that lead to professional success. Dispositions are learnable, not necessarily teachable. This work-in-progress paper motivates dispositions within computing disciplines and presents the background of this approach. It also discusses the use of reflection exercises and vignettes in understanding, promoting, and fostering behavioral patterns that undergraduate computing students identify as related to dispositions they experience in the course. Preliminary data and results from the study are also presented. 

The Computing Curricula 2020 (CC2020) report, issued by the ACM and IEEE Computer Society, identified knowledge, skills, and dispositions as the three main components of competency for undergraduate programs in computer engineering, computer science, cybersecurity, information systems, information technology, and software engineering, as well as data science. As earlier generations of curricular guidelines in computing have described knowledge and skills to some extent, the notion of dispositions is relatively new to computing. Dispositions are cultivable behaviors, such as adaptability, meticulousness, and self-directedness, that are desirable in the workplace. Multiple employer surveys and interviews confirm that dispositions are as crucial for success in the workplace as the knowledge and skills students develop in their academic programs of study. As such, the CC2020 report describes eleven dispositions that are expected of competent computing graduates. These are distinct and separate from the technical knowledge and disciplinary skills of computing and engineering. Dispositions are also distinct from baseline or cross-disciplinary skills, such as critical thinking, problem-solving, teamwork, and communication. In contrast, dispositions are inherently human characteristics that describe individual qualities and behavioral patterns that lead to professional success. Dispositions are learnable, not necessarily teachable. This work-in-progress paper motivates dispositions within computing disciplines and presents the background of this approach. It also discusses the use of reflection exercises and vignettes in understanding, promoting, and fostering behavioral patterns that undergraduate computing students identify as related to dispositions they experience in the course. Preliminary data and results from the study are also presented. 

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. 

This is a panel presentation on the role of dispositions in computing education. 

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. The leaders of this special session collaborate on a multi-institutional project funded by the National Science Foundation. Using their experiences at four higher education institutions, they will demonstrate how to foster dispositions among computing students through two hands-on activities. The audience will get first-hand experience using reflection exercises and vignettes, and will participate in debating their design, merits, and limitations. The resulting interaction will provide the audience ample time to discuss the benefits and challenges of incorporating and fostering dispositions in computing programs. It is hoped that participants will leave with concrete ideas on how to extend the current work to their own courses, programs, and institutions.