Our working group aims to better understand teamwork in undergraduate computing education through a systematic literature review and focus groups with instructors. Based on the strategies suggested and needs identified, we will also co-design instructional resources with the community to cultivate the skills, values, and virtues pertinent to teamwork. Through planned research and collaboration, we seek to inform and establish practical materials that can enhance teaming pedagogy and prepare graduates for the evolution of technology and the workforce.

In Peer Instruction (PI) an instructor displays a challenging multiple-choice question during lecture that students answer individually, discuss verbally with nearby peers, answer individually again, and finally, the instructor leads a discussion of the question. Peer Instruction typically increases student learning and motivation over traditional lecture. We added a text-chat mode to improve PI for remote synchronous learning. This feature assigns students to discussion groups to maximize the number of groups that have members with different answers. The tool was pilot tested in Winter 2022 and revised. In Fall 2022 and Winter 2023, it was tested at one institution. In Fall 2024, it was tested at four institutions. We conducted a log file analysis of student data from 1394 students and analyzed surveys with 848 student responses. We found that questions answered using the text-chat had a significantly higher improvement than those using traditional verbal discussion, although the two modes were tested with different questions. Interestingly, most of the students preferred to discuss the question verbally, although some preferred the text-chat discussion. These results inform efforts to improve the effectiveness of Peer Instruction and increase its adoption.

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.

Many novice programmers struggle to write code from scratch and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. Parsons problems can also include distractor blocks that are not needed in a correct solution, but which may help students learn to recognize and fix errors. Evidence indicates that students find Parsons problems engaging, easier than writing code from scratch, useful for learning patterns, and typically faster to solve than writing code from scratch with equivalent learning gains. This working group leverages the work of the 2022 ITiCSE working group which published an extensive literature review of Parsons problems and designed and piloted several studies based on the gaps identified by the literature review. The 2023 working group is revising, conducting, and creating new studies. We will analyze the data from these multi-institutional and multi-national studies and publish the results as well as recommendations for future working groups.

The Computer Science Curricula 2023 (CS2023) effort includes a synergistic endeavor to provide supplemental material to aid in the adoption of the guidelines. This supplemental material takes the form of a series of peer-reviewed articles by experts on the design and delivery of Computer Science programs. The Computing for Social Good (CSG) committee is responsible for providing an international perspective on Computing for Social Good in Education (CSG-Ed), description of models for incorporating CSG into curricula, and a discussion of best practices including examples. There is a wide range of approaches to CSG-Ed both in the classroom and beyond. This BoF is intended to gain input from current instructors about these approaches including a discussion of which approaches work well and which are more problematic. We also intend for the BoF to bolster the community of educators who are interested in CSG-Ed in order to disseminate approaches to a wider audience.

Programming is a complex task that requires the development of many skills including knowledge of syntax, problem decomposition, algorithm development, and debugging. Code-writing activities are commonly used to help students develop these skills, but the difficulty of writing code from a blank page can overwhelm many novices. Parsons problems offer a simpler alternative to writing code by providing scrambled code blocks that must be placed in the correct order to solve a problem. The extensive literature on Parsons problems documents numerous benefits to using them as both formative and summative assessments. These include more efficient learning, the possibility to dynamically adapt to learner needs, and more reliable grading. Despite these positive findings, further research is needed in order to draw broader inferences. Most work has been conducted at single institutions under unique conditions that are not easily replicated, and some prior studies have been inconclusive or had limitations that affected data validity. To address this, we propose a multi-institutional and multi-national study of the effectiveness of Parsons problems for novice programmers. We will focus on introductory programming courses (CS0/1/2) that use Java, Python, and C/C++ as these are the most common teaching languages. The working group will collaborate to refine the scope, methodology and research questions, and contribute to data collection and analysis.

A notional machine is a pedagogic device to assist the understanding of some aspect of programs or programming. It is typically used to support explaining a programming construct, or the user-understandable semantics of a program. For example, a variable is like a box with a label, and assignment copies or moves a value into that box. This working group will capture examples of notional machines from actual pedagogical practice, as expressed in textbooks (or other teaching materials) or used in the classroom. We will interview at least 30 teachers about their experience with, and perceptions of, the use of notional machines in teaching. Using the interviews, we will work on devising and refining a form to characterise essential features of notional machines. We will also attempt to relate them to each other to describe potential learning sequences or progressions. The working group report will contain descriptions of notional machines used at different levels in education, in different countries, by many teachers. Capturing and Characterising Notional Machines Sally Fincher, Johan Jeuring, Craig S Miller Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). ITiCSE 2020,,Trondheim, Norway © 2020 Copyright held by the owner/author(s). 978-1-4503-0000-0/18/06...$15.00 https://doi.org/10.1145/1234567890 The resulting catalogue of notional machines will allow a teacher to select a machine for a particular use, permit comparison between them, and provide a starting point for further categorization and analysis of notional machines. Additionally, we will make more theoretical explorations. We will explore a variety of presentational formats, examining what is necessary and what superfluous; we will look for dimensions of comparison and will examine how notional machines are instantiated across the discipline. We argue that the creation and use of notional machines is potentially a signature pedagogy for computing [1] and that creating and using notional machines represents a certain level of pedagogic sophistication that might be an indicator of pedagogic content knowledge (PCK).

Diagnostic messages generated by compilers and interpreters such as syntax error messages have been researched for decades. Unfortunately these messages which include error, warning, and runtime messages, present substantial difficulty and could be more effective, particularly for novices. Recent years have seen increased number of papers in the area including studies on the effectiveness of these messages, improving or enhancing them, and their usefulness as a part of programming process data that can be used to predict student performance. Despite this increased interest, the long history of literature is quite scattered and has not been brought together in any digestible form. We argue that in order to help the community proceed with more work on diagnostic messages, the literature needs to be presented in a state-of-the-art report. In addition we will synthesize and present the existing evidence for these messages including the difficulties they present and their effectiveness. We will also formulate a set of guidelines based on this evidence that can be used when designing or enhancing diagnostic messages. This work can serve as a starting point for those who wish to conduct research on such messages, those who wish to design better messages or those that aim to measure their effectiveness, more effectively.

International students are an important and desirable constituent in most computer science programs. These students help to enrich the programs, bring new perspectives into the classroom, diversify the student population, globalize the curriculum, broaden the perspective of domestic students, and generate revenue for the host institution. Each of these characteristics is desirable and increasingly important in today's highly connected world and job market. Most institutions invest resources in attracting international students and provide orientation sessions for them on arrival to help acclimate them to the new environment and to introduce them to other students. There are often clubs to provide support groups and social functions to help them meet and make friends with domestic students. However, challenges for international students, and for the faculty teaching them, persist at many institutions despite these efforts to help international students deal with culture shock, differing academic expectations and teaching methods, and different attitudes toward issues such as plagiarism.

POGIL (Process Oriented Guided Inquiry Learning) is a form of active learning where students work collaboratively through activities designed to guide them to discover concepts on their own. POGIL is based on the principle that students will learn and retain more when they construct their own understanding of the computer science (CS) content. Working in groups of three to four, students develop process skills, such as problem solving, communication, and teamwork. This special session will first provide SIGCSE attendees the opportunity to experience a CS POGIL activity for themselves. We will then discuss different ways that instructors might adopt POGIL for use in their classroom, and how the adoption of POGIL by multiple instructors in a department can change the culture of the department.

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.

Across the US, plummeting CS enrollments and disappointing student retention have caused serious concern in recent years. Yet, in the Spring of 2007, the Berea College faculty unanimously adopted a CIS major in response to the rise in enrollment in CS0 and retention into CS1. This paper details how using a funnel approach to attract students into the discipline via multiple CS0-level courses resulted in the exception to the trend.

For controlling robots in an urban search and rescue (USAR) application, a wearable joystick is presented with improved sensing capability as well as a giant magneto-resistance (GMR) sensor model for use with rare-earth magnets. Scientists have been studying a variety of existing human/robot interface devices to control USAR robots in a disaster. Due to the stresses involved in USAR environments, the selection of an appropriate interface device out of the numerous interactive devices available has to be carefully considered. Furthermore, the total burden to the user of human/robot interface devices in USAR tasks includes not only the periods of interaction, but also the burden of transporting and remotely setting up the devices. The wearable joystick presented is developed with the design goal of minimizing total encumbrances. The features of this wearable joystick include easy and wire-free installation into regular gloves. An improved hardware structure for the sensor pad and the alignment of magnets is described that completely wraps the wrist. This band-type mechanism provides more robust data acquisition than previous prototypes. To evaluate performance, time-to-complete tests are performed, with a comparison to a metric for path tortuosity. The fractal dimension of the resulting path is analyzed to represent the degree of control the user has over the interface device. Experimental results are provided from both computer screen tess and real USAR robot driving tests.

This article assesses the performance of a virtual pheromone-based sectored dispersion algorithm which is implemented on a low-cost miniature mobile robotic platform using sonar sensing. The approach is a distributed one and the robotic platform and sensor array are sufficiently inexpensive that they can be employed in undergraduate research and/or for coursework in a small college environment

To safely and efficiently guide search and rescue operations in disaster areas, gathering of relevant information such as the locations of victims, must occur swiftly. Using the concept of repellent virtual pheromones inspired by insect colony coordination behaviors, miniature robots can be quickly dispersed to survey a disaster site. Assisted by visual servoing, dispersion of the miniature robots can quickly cover an area. An external observer such as another robot or an overhead camera is brought into the control loop to provide each miniature robot estimations of the positions of all the other nearby robots in the robotic team. Each robot can then move away from the other nearby robots, resulting in the robot collective swiftly dispersing through the local area. The technique has been implemented using the miniature scout robots, developed by the Center for Distributed Robotics at the University of Minnesota, which are well-suited to surveillance and reconnaissance missions.

We consider the complexity of determining whether differing probability distributions for the same Bayes net, result in different, policies, significantly different, policy outcomes or optimal value functions.