Generative artificial intelligence (GenAI) offers new possibilities for generating personalized programming exercises, addressing the need for individual practice. However, the task quality along with the student perspective on such generated tasks remains largely unexplored. Therefore, this paper introduces and evaluates a new feature of the so-called Tutor Kai for generating comprehensive programming tasks, including problem descriptions, code skeletons, unit tests, and model solutions. The presented system allows students to freely choose programming concepts and contextual themes for their tasks. To evaluate the system, we conducted a two-phase mixed-methods study comprising (1) an expert rating of 200 automatically generated programming tasks w.r.t. task quality, and (2) a study with 26 computer science students who solved and rated the personalized programming tasks. Results show that experts classified 89.5% of the generated tasks as functional and 92.5% as solvable. However, the system's rate for implementing all requested programming concepts decreased from 94% for single-concept tasks to 40% for tasks addressing three concepts. The student evaluation further revealed high satisfaction with the personalization. Students also reported perceived benefits for learning. The results imply that the new feature has the potential to offer students individual tasks aligned with their context and need for exercise. Tool developers, educators, and, above all, students can benefit from these insights and the system itself.

Collaboration is a crucial part of computing education. The increase in AI capabilities over the last couple of years is bound to profoundly affect all aspects of systems and software engineering, including collaboration. In this position paper, we consider a scenario where AI agents would be able to take on any role in collaborative processes in computing education. We outline these roles, the activities and group dynamics that software development currently include, and discuss if and in what way AI could facilitate these roles and activities. The goal of our work is to envision and critically examine potential futures. We present scenarios suggesting how AI can be integrated into existing collaborations. These are contrasted by design fictions that help demonstrate the new possibilities and challenges for computing education in the AI era.

In this poster, we present a new educational tool based on GenAI: a Supportive Chatbot for Resolving Introductory Programming Tasks (SCRIPT). Its goal is to support novice learners of programming with guardrails and predefined prompts, so they can focus on the problem-solving process, and receive quality feedback.

To benefit from most of the current digital educational technologies, learners are required to sit down and look closely at a computer monitor or smart device screen for hours, which can have side effects on learners’ health and lifestyle. As an attempt to address this, we developed MathKinetics, an application designed to support the practice of cognitive skills such as arithmetic while engaging in physical activity by integrating the principles of Multimodal Learning, Life Kinetik, and Gamification. MathKinetics is a variant of an endless running game where users control an avatar through their body posture and dodge obstacles. At the same time, they pick up arithmetic problems whose answers need to be verbalized. In this paper, we present an exploratory evaluation of MathKinetics and its user experience. We conducted user tests with 20 participants. Results from our tests indicate that MathKinetics is a fun way to practice arithmetic skills and train executive cognitive functions such as task switching.

This research paper contributes to the computing education research community's understanding of Generative AI (GenAI) in the context of introductory programming, and specifically, how students utilize related tools, such as ChatGPT. An increased understanding of students' use is mandatory for educators and higher education institutions, as GenAI is here to stay, and its performance is likely to improve rapidly in the near future. Learning about students' use patterns is not only crucial to support their learning, but to develop adequate forms of instruction and assessment. With the rapid advancement of AI, its broad availability, and ubiquitous presence in educational environments, elaborating how AI can enhance learning experiences, especially in courses such as introductory programming is important. To date, most studies have focused on the educator's perspective on GenAI, its performance, characteristics, and limitations. However, the student perspective, and how they actually use GenAI tools in course contexts, has not been subject to a great number of studies. Therefore, this study is guided by the following research questions: (1) What do students report on their use pattern of ChatGPT in the context of introductory programming exercises? and (2) How do students perceive ChatGPT in the context of introductory programming exercises? To address these questions, computing students at a large German university were asked to solve programming tasks with the assistance of ChatGPT as part of their introductory programming course. Students (n=298) provided information regarding the use of ChatGPT, and their evaluation of the tool via an online survey. This research provides a comprehensive evaluation of ChatGPT-3.5's application by novice programmers in a higher education context. The findings reveal that while students widely adopt GenAI, their use varies significantly, ranging from acceptance of generated solutions to dynamic, and critical engagement. Therefore, this work has implications for educators designing guardrails or forms of instructions on the use of GenAI tools in the classroom.

Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs such as Codex, GPT-3.5, and GPT 4 have shown promising results in the context of large programming courses, where students can benefit from feedback and hints if provided timely and at scale. This paper explores the quality of GPT-4 Turbo's generated output for prompts containing both the programming task specification and a student's submission as input. Two assignments from an introductory programming course were selected, and GPT-4 was asked to generate feedback for 55 randomly chosen, authentic student programming submissions. The output was qualitatively analyzed regarding correctness, personalization, fault localization, and other features identified in the material. Compared to prior work and analyses of GPT-3.5, GPT-4 Turbo shows notable improvements. For example, the output is more structured and consistent. GPT-4 Turbo can also accurately identify invalid casing in student programs' output. In some cases, the feedback also includes the output of the student program. At the same time, inconsistent feedback was noted such as stating that the submission is correct but an error needs to be fixed. The present work increases our understanding of LLMs' potential, limitations, and how to integrate them into e-assessment systems, pedagogical scenarios, and instructing students who are using applications based on GPT-4.

The advent of Large Language Models (LLMs) started a serious discussion among educators on how LLMs would affect, e.g., curricula, assessments, and students' competencies. Generative AI and LLMs also raised ethical questions and concerns for computing educators and professionals.

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.

Most computing students enter the industry once they graduate. As future software engineers, they will be in powerful positions, making decisions that impact their personal lives, others, and society. Thus, preparing graduates for their careers is crucial by addressing ethical considerations, decision problems, and other concepts related to morals, values, and legal aspects (e.g., data protection, privacy, security, etc.) as part of computing curricula. In this paper, we propose the integration of data ethics into computing programs and provide a framework for an ethics module, including relevant competency-based learning objectives. The proposed module is based on a curricular analysis of all 71 German data science degree programs focusing on ethics courses. The course contents and competency goals were analyzed and classified based on their cognitive complexity. As the results proved the lack of competency-based learning outcomes, we designed observable competency goals, meaning knowledge, skills, and dispositions taken in the context of a task. In addition, we provide suggestions for contents, pedagogical instructions, and assessments in such a course. The proposed module serves as a first draft and resource to support other educators aiming to design such a course and who are willing to integrate it into computing curricula.

A recent ITiCSE working group investigated when and how experts give feedback and hints at steps novice programmers take when solving programming problems. Based on the feedback literature and an analysis of expert feedback on steps, the working group designed guidelines for when and how to give feedback. The feedback provided by educators using these guidelines on a number of sequences of student steps varied a lot. In this paper, we try to answer the question of why educators give feedback at particular steps to novice learners of programming. We prepared six authentic sequences of student steps when solving an introductory programming task. The preprocessed sequences were used in a survey to gather information about when and why an expert would give feedback. Respondents annotated each step from one sequence with if and why they would give feedback at that step. Our survey received 47 responses. We qualitatively analyzed the responses, resulting in a coding scheme consisting of 19 different reasons for why experts intervene (or not) when novice learners work on introductory programming tasks. We found a considerable variety of reasons experts give for when and how to help students with feedback and hints. Also, sometimes one expert uses a reason at a step to explain why they do intervene, and another expert uses the same reason at the step to not intervene. The categories of experts' feedback indicators will pave the way for several future studies and applications, including learning systems trying to resemble expert feedback strategies.

Generative AI (GenAI) has seen great advancements in the past two years and the conversation around adoption is increasing. Widely available GenAI tools are disrupting classroom practices as they can write and explain code with minimal student prompting. While most acknowledge that there is no way to stop students from using such tools, a consensus has yet to form on how students should use them if they choose to do so. At the same time, researchers have begun to introduce new pedagogical tools that integrate GenAI into computing curricula. These new tools offer students personalized help or attempt to teach prompting skills without undercutting code comprehension. This working group aims to detail the current landscape of education-focused GenAI tools and teaching approaches, present gaps where new tools or approaches could appear, identify good practice-examples, and provide a guide for instructors to utilize GenAI as they continue to adapt to this new era.

In a previous Birds of a Feather discussion, we delved into the nascent applications of generative AI, contemplating its potential and speculating on future trajectories. Since then, the landscape has continued to evolve revealing the capabilities and limitations of these models. Despite this progress, the computing education research community still faces uncertainty around pivotal aspects such as (1) academic integrity and assessments, (2) curricular adaptations, (3) pedagogical strategies, and (4) the competencies students require to instill responsible use of these tools. The goal of this Birds of a Feather discussion is to unravel these pressing and persistent issues with computing educators and researchers, fostering a collaborative exploration of strategies to navigate the educational implications of advancing generative AI technologies. Aligned with this goal of building an inclusive learning community, our BoF is led by globally distributed leaders to facilitate multiple coordinated discussions that can lead to a broader conversation about the role of LLMs in CS education.

This work represents a work-in-progress study on how dispositions are essential to the workplace. Recent computing curricular reports have heralded the importance of moving from knowledge-based to competency-based learning. However, dispositions, as with skills and knowledge, are a crucial component of competency. Hence, this work reports on a study conducted by the authors to ascertain the relationship between job advertisements and the dispositions expected in the workplace. The results show a remarkable degree of correlation between the two. At the same time, some dispositions are more explicitly expected than others. Hence, computing and engineering educators should increase their efforts to foster dispositions in their curricula to develop competent graduates ready to succeed in the workplace. At the same time, more research is required on the industry's perspective and understanding dispositions.

Ever since the emergence of large language models (LLMs) and related applications, such as ChatGPT, its performance and error analysis for programming tasks have been subject to research. In this work-in-progress paper, we explore the potential of such LLMs for computing educators and learners, as we analyze the feedback it generates to a given input containing program code. In particular, we aim at (1) exploring how an LLM like ChatGPT responds to students seeking help with their introductory programming tasks, and (2) identifying feedback types in its responses. To achieve these goals, we used students' programming sequences from a dataset gathered within a CS1 course as input for ChatGPT along with questions required to elicit feedback and correct solutions. The results show that ChatGPT performs reasonably well for some of the introductory programming tasks and student errors, which means that students can potentially benefit. However, educators should provide guidance on how to use the provided feedback, as it can contain misleading information for novices.

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.

Innovation and technology in computer science education is driven by research and practice. Both of these activities involve the gathering and analysis of data in order to develop new tools, methods including software, or strategies to solve recent challenges in the field. However, data as basis for any new solution is hardly shared, reused and recognized. This is due to the fact that the publication of research data encompasses a number of challenges for researchers, while benefits of publishing data remain low. As a result, further analyses of data as part of secondary research are uncommon in the computer science education community. Therefore, the authors of this position paper critically reflect on current practices related to the publication of research data in this community. Moreover, a path forward is outlined for future conferences, such as ITiCSE, to become increasingly FAIR, and open with regard to research data.

Software and IT infrastructure keeps changing the way we live and work, but not necessarily for the better for all of us. Considering the implications of software on our society, and the industry's expectations towards computing graduates, it appears natural to address social and ethical competencies within computing curricula. However, this is not necessarily the case in German computing education. Due to this desideratum, this paper addresses the role of ethical guidelines and social responsibility in programming education in contrast to industry expectations. Expected competencies in programming education and ethics modules of CS study programs were identified by a secondary analysis of available data. The present work also gathered and qualitatively analyzed job advertisements with regard to expected competencies. The results (1) illustrate the lack of correspondence with what is expected in educational settings and the profession, and (2) outline implications for a socially responsible programming education. These findings will support educators in developing competency-based pedagogical approaches to address socially responsible learning objectives in future programming courses and CS study programs.

The recent advent of highly accurate and scalable large language models (LLMs) has taken the world by storm. From art to essays to computer code, LLMs are producing novel content that until recently was thought only humans could produce. Recent work in computing education has sought to understand the capabilities of LLMs for solving tasks such as writing code, explaining code, creating novel coding assignments, interpreting programming error messages, and more. However, these technologies continue to evolve at an astonishing rate leaving educators little time to adapt. This working group seeks to document the state-of-the-art for code generation LLMs, detail current opportunities and challenges related to their use, and present actionable approaches to integrating them into computing curricula.

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.

In cognitive psychology, creativity is an established and well-researched construct. Creativity is linked to openness to experience, and creating innovative solutions. It is this synthesis of prior knowledge and experience that reflects the core ideas of constructivist learning theory. Recently, the investigation and measurement of students’ creative capacities has gained traction in computing. Although being creative is important in the solution of programming problems, creativity is neither embedded in computing curricula as a learning objective, nor established in competency-based educational practice. Assuming that creativity is a malleable component of competency, the present paper aims at investigating constructivist theories to help foster creativity as part of programming competency in computing. Accordingly, learning theories from Piaget, Vygotskiy, Bruner and Dewey were reviewed with regard to their perspective on human learning and the creation of new knowledge through connections. Their review and alignment with the context of programming education results in recommendations for pedagogical interventions, such as collaborative project work, social interaction, scaffolding, active learning, multi-sensory experiences, gamification, and authentic tasks. As a next step, these pedagogical approaches will be investigated with regard to their effects on the creativity of novice learners of programming.

No abstract available.

Every year, millions of students learn how to write programs. Learning activities for beginners almost always include programming tasks that require a student to write a program to solve a particular problem. When learning how to solve such a task, many students need feedback on their previous actions, and hints on how to proceed. In the case of programming, the feedback should take the steps a student has taken towards implementing a solution into account, and the hints should help a student to complete or improve a possibly partial solution. Only a limited number of learning environments for programming give feedback and hints on intermediate steps students take towards a solution, and little is known about the quality of the feedback provided. To determine the quality of feedback of such tools and to help further developing them, we create and curate data sets that show what kinds of steps students take when solving programming exercises for beginners, and what kind of feedback and hints should be provided. This working group aims to 1) select or create several data sets with steps students take to solve programming tasks, 2) introduce a method to annotate students' steps in these data sets, 3) attach feedback and hints to these steps, 4) set up a method to utilize these data sets in various learning environments for programming, and 5) analyse the quality of hints and feedback in these learning environments.

Technical progress and social transformation processes require lifelong learning in different spaces and formats. This leads to new developments and challenges in education with regard to competency-based learning and recognition practices, especially as educational biographies become increasingly diverse and extensive repetitions of qualifications should be avoided. Until recently, a common assessment strategy for previously developed competencies has not been established in higher education. In this study, competencies expected in German vocational and higher educational programming training were identified, compared and evaluated with regard to equivalence. The methodological approach started with an extensive review of general frameworks and curricula, before competency descriptions and analyses of reference documents of German vocational and university programming education were gathered and systematically compared. As part of the iterative design process, a comparison matrix was developed, and common features along with gaps and overlaps of competencies were identified. Even though both educational tracks pursue different goals, they share a great number of expected programming competencies. In the context of lifelong learning, the proposed process can be utilized for improving the permeability and recognition practises in both vocational training and higher education.

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.

This work addresses the demand of an empirically developed competence model for programming as challenging core tier of computer science curricula. The presented paper investigates the application of Bloom's revised taxonomy for learning, teaching and assessing by Anderson and Krathwohl for the specification of currently used learning objectives in programming education. Accordingly, 129 module descriptions of beginner level programming courses from 35 German universities constitute the sample. Learning goals are evaluated using Mayring's qualitative content analysis. In addition, seven guided interviews with computer science professors as experts are categorized according to Mayring's qualitative analysis method. As a result, a model comprised of deductively-inductively built cognitive categories is proposed, proving the adequacy of Bloom's revised taxonomy for computer science and programming in particular. The categories depict current operationalized learning objectives and cognitive competencies of novice programmers, as well as additional non-cognitive competencies. Thus, the results can help classify competency levels and support the didactic design of introductory programming classes and assessment. This research also constitutes a basis for the development of a measuring instrument of programming competence in the future.