Background and Context: Replication studies play an important role in Computing Education Research (CER) by supporting the development of consistent and reliable scientific knowledge. However, prior research indicates that the CER community tends to prioritise novel contributions over replication. A 2019 Systematic Literature Review (SLR) identified only 54 (2.38%) replication studies among 2,269 papers published between 2009 and 2018 across five major CER venues. In response, the Computer Science Education journal released a special issue dedicated to replication studies to encourage greater adoption of this research design. Objectives: This study aims to examine how the landscape of replication research in CER has evolved since 2019. Specifically, we investigate whether the prevalence of replication studies has increased and explore current perceptions and experiences of CER researchers regarding replication. Method: We replicated two prior studies. First, we conducted an updated SLR to identify replication studies published between 2019 and 2025 in the same five CER venues. Second, we replicated a survey of Computing Education researchers to better understand their perceptions, experiences, and challenges related to conducting and publishing replication studies. Findings: Our SLR identified 63 (2.50%) replication studies among 2,516 published papers. While the proportion of replication studies has increased slightly, overall growth remains limited. We observed a shift toward more published replication studies in journals and an increase in authors replicating their own prior work. Survey results indicate that although many researchers engage in replication within their teaching and research practice, they encounter significant challenges when attempting to publish replication studies. Implications: Despite increased discourse around open science and research rigour, the adoption of replication studies in CER has not substantially grown. Our findings offer opportunities for future research to promote replication in CER and to explore how the CER community can encourage researchers to publish replication studies.

Researchers use replication to confirm, strengthen, and advance Computing Education Research (CER). However, prior research shows that replication is infrequently used in CER, even though the community encourages its use. Previous research suggests that the CER community uses different terms to describe replication, which we aim to confirm in this Working Group (WG). We will conduct a Systematic Literature Review (SLR) across influential international CER venues to understand how researchers present and conduct replication studies, possibly identifying venues receptive to papers applying this research design. In addition, we will interview Computing Education researchers, conference leaders, and journal editors to understand their experiences and perceptions with replication. We expect to collect suggestions and recommendations on how CER can encourage more replication in future studies. Our work will highlight and confirm the terms the CER community uses to present replication studies, enabling researchers and educators to better identify these studies.

With the rise of generative AI performing tasks traditionally allocated to entry-level roles, and the current economic slowdown, recent graduates need to demonstrate non-technical skills and behaviours to stand out in the job market. Our study collects perspectives from academic and industry professionals on how graduates can demonstrate essential non-technical skills, also known as soft skills, and workplace behaviours. We used a mixed methods approach to analyse 168 survey responses and 34 interviews. Our findings indicate that communication and collaboration are crucial for entry-level professionals, whereas mentorship and a purpose-driven disposition are less important at this stage of their careers. Our findings suggest that asking questions and reflective thinking showcase non-technical skills and behaviours in recent graduates; these same approaches are also used by industry to assess such attributes, especially during interviews. We conclude with implications and practical advice for educators and researchers to integrate and advance authentic practice, helping students learn to apply and demonstrate non-technical skills and behaviours applicable in the workplace.

Collaboration is crucial in Software Engineering (SE), yet factors like gender bias can shape team dynamics and behaviours. This descriptive case study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub projects. Focusing on gender, we used a mixed-methods approach to analyse Slack communications, identifying gender differences in how students respond to initiated communications and comparing how students’ communications influenced other aspects of students’ performance, including learning gains. We found higher help-seeking and leadership behaviours in the all-woman team involved in this case study, while men responded more slowly. Although communication did not directly affect final grades, we identified statistical significance in the correlation between communication and students’ understanding of software development. With this case study showing that some students putting more effort into collaboration, future work can investigate diversity and inclusion training to balance these efforts. In addition, we observed a link between team engagement and a higher understanding of software development, highlighting the potential for teaching strategies that promote help-seeking. These findings could guide future research by integrating intersectionality to address the challenges that SE students face when using communication platforms, thereby fostering more equitable collaboration in SE Education. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board . • A case study evaluating student teams’ communication through the lens of gender. • Examined response patterns during an eight-week team activity contributing to GitHub. • Communication patterns correlated with individual learning gains. • Engaged communicators tended to understand software development concepts more deeply. • Women led in follow-up and monitoring; men lagged in response time.

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.

Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL educational theory in SE Education. The SMS identified 14 papers published between 2011 and 2022. We used qualitative analysis to classify the papers into four CL paradigms: Conditions, Effect, Interactions, and Computer-Supported Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift in student interaction research to computer-mediated technologies. We discussed the 14 papers in depth, describing their goals and further analysing the CSCL research. Almost half the papers did not achieve the appropriate level of supporting evidence; however, calibrating the instruments presented could strengthen findings and support multiple CL paradigms, especially opportunities to learn at the social and community levels, where research was lacking. Though our results demonstrate limited CL educational theory applied in SE Education, we discuss future work to layer the theory on existing study designs for more effective teaching strategies.

The open-source community uses the GitHub platform to exchange and share software applications and services of interest. This paper aims to identify the open-source community’s interest in gender-related projects on GitHub. Our findings create research opportunities and identify resources by the open-source community that promote diversity, equity, and inclusion. We use data mining to identify GitHub projects that focus on gender-related topics. We apply quantitative and qualitative methodologies to examine the projects’ attributes and to classify them within a gender social structure and a gender bias taxonomy. We aim to understand the open-source community’s efforts and interests in gender topics through active projects. In this paper, we report on a preponderance of projects focusing on specific gender topics and identify those with a narrow focus. We examine projects focusing on gender bias and how they address this non-inclusive behaviour. Results show a propensity of GitHub projects focusing on recognising and detecting an individual’s gender and a dearth of projects concentrating on the cultural expectations placed on women and men. In the gender bias domain, the projects mainly focus on occupational biases. These findings raise opportunities to address the limited focus of GitHub on gender-related topics through developing projects that mitigate exclusive behaviours.

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.

This poster focuses on workshops to support students' soft and selfreflection skills during collaborative learning. These workshops intend to help reduce anxiety during group work and to promote inclusive and equitable collaborative learning environments. Unfortunately, single-paced instructional approaches are typically applied in learning environments [3] and do not consider students' needs when learning nor provide soft-skills guidance that encourages equal participation. The workshops offer educator and student support for equitable group work through upskilling students' soft skills, such as leadership and communication, that promote better teamwork. By assisting students in developing and practising soft and self-reflection skills, they might have the opportunity to contribute to solving the activity, enabling them to participate equally. This poster includes an experimental study plan to integrate and evaluate the effects of these workshops.

Debugging manuals help programmers develop strategies to reduce program issues. These manuals assume the reader has prior programming knowledge to comprehend the presented strategies. While this assumed knowledge is above Introductory Programming (CS1) students’ programming abilities, it does illustrate a variety of strategies they can use during the debugging process to help them realise different approaches that are available to successfully debug a problem. In this research paper, we present a debugging manual, Read the Debugging Manual (RTDM), that is designed for CS1 students. The manual presents a variety of strategies that can be applied across the debugging process without assumed prior programming knowledge. A pilot study was conducted to evaluate the manual, where CS1 students were provided the manual as a debugging intervention for a programming activity. Students were invited to share their experiences and feedback on the manual by participating in a survey. The results show students wanting worked examples in the manual to practice strategies. Providing students with the manual seems to be supporting Self-Regulated Learning (SRL) strategies, helping students recall existing strategies and introducing them to new ones. The paper concludes with future research opportunities to improve the manual.

Students might have preconceptions about programming when enrolling in an Introductory Programming (CS1) course. These preconceptions might influence their expectations about programming assignments. Understanding these preconceptions could help give students a voice in their learning experience. This paper reports on a study for CS1 programming assignments. This study uses an assignment design activity as an instrument to collect student voice, asking students to design a programming assignment they expect to accomplish at the end of a CS1 course. A mixed-methods approach was used to analyse the subject matter and course learning outcomes of the students' assignment designs. The results show students applying prior knowledge, a process known as transfer of training, to design their assignments, predominately focused on math and gaming. The results also show that students with no prior programming experience had lower expectations from the programming assignments, which might have influenced their study effort in the course. We discuss integrating the results into CS1 assignments, helping students transition to new roles as programmers, and adjust their study expectations early to recognise when more effort is needed to successfully complete a course.

Collaboration is important during software development, but related work has found gender differences can influence the collaboration process, creating inequality in the team's dynamics. In this paper, we present a gender analysis study that involved 39 students, examining their teams' online collaborations while contributing to a large open-source software project. Eight teams of 4-6 Software Engineering (SE) students communicated over an online messaging platform, Slack, to complete an eight-week project. The goal of this study is to identify gender differences emerging from team collaboration. A mixed-methods approach was used to collect students' teamwork experiences and analyse their collaboration. Our research shows statistically significant results in female students' leadership, coordination, and project-monitoring behaviours used to complete the project. The results also showed a higher rate of help seeking within the all-female team, an infrequent behaviour observed in the all-male and mixed-gender teams. Our findings raise future research opportunities to further investigate the gender differences emerging from team collaboration.

A Computer Science programming assignment is a core assessment tool used by educators to measure students' understanding of programming concepts. A well-structured programming assignment can assist students in comprehending a problem and help them develop problem-solving skills. This research focuses on the presentation of well-structured programming assignments to help with students' understanding of the problem, supporting educators in the development of these assignments. To create well-structured programming assignments, we performed a literature review to identify design treatments that help students better understand introductory programming (CS1) assignments. An illustration study was then used to test the assignments from the identified design treatments. The illustration study collected students' perspectives on how the design treatments improved the understanding of the assignment design. From this work, we developed a framework from the identified design treatments. The results for the illustration study showed the design treatments helped students identify the problem's requirements and realise what is required to solve the problem. Though our work was examined within the CS1 context, our findings may be useful to similar assignment contexts, such as helping students identify requirements for solving problems.

Parsons problems are a type of assessment that helps introductory programming (CS1) students learn programming by arranging fragments of code to form working programs. Parsons problems enables students to focus on the learning concepts by minimising their focus on extrinsic operations while coding, such as syntax details. Because of the success in teaching programming with Parsons problems, we investigate the possibility of using it as a design-based intervention. We examine how Parsons problems can be used by procedural programming students during the design process, and investigate how the assessment supports the use of Self-Regulated Learning (SRL) strategies during the problem-solving process. We performed usability testing, comprising a mixed-methods approach of pre-test, think-alouds, and interviews, to collect students' behaviours and experiences. The results show a variety of approaches used by students when solving the Parsons problems, including engaging SRL strategies to better understand the problem and to verify their work. We offer future research opportunities to further explore Parsons problems as a design-based intervention.

This study explores how a well-formed programming assignment can assist students in understanding a problem. To determine whether the presentation of a programming assignment might help CS1 students better understand the problem, we perform a comparable replication study using Soloway's Rainfall Problem. This study integrates scaffolding treatments from prior studies using the Rainfall Problem into a theoretical assignment design pattern, to form a new presentation context. A survey was used to gain insight into how students perceived the presentation helped them understand the problem and potentially helped them solve the programming tasks. Results from the study show plan composition and natural language translation a challenge, but students appreciate the additional guidance from the assignment presentation in the form of hints and bullet points formatting to identify tasks. From the results, we are considering these scaffolding treatments and assignment presentation for future CS1 programming assignments.

Questioning is a learning activity that can promote the use of critical thinking skills, where thinking processes are required to answer a posed question. In this paper, we map instructional question types to Bloom's Taxonomy--a classification of critical thinking skills required for cognition--to form a framework for educators to construct learning activities through questioning. The preliminary instructional framework is applied to a question activity within a blended CS1 learning environment to support students in better understanding on how to solve a programming assignment. Our results show students meeting the desired Bloom's cognitive level when answering the question activity. Future research opportunities are presented to test the framework for upper-division CS courses and further explore the framework as an intervention for programming assignments.

Parsons Problems is an effective Introductory Programming teaching tool that allows students to arrange code to form a functional program, reducing their cognitive load so they can focus on programming concepts. This demonstration shows how Parsons Problems can be used as a pedagogical strategy in a Massive Open Online Course (MOOC) for introductory programming. A MOOC is a learning platform, where compilers and IDEs have already been embedded for CS students to immerse themselves in a single learning environment. The Parsons Problems package, js-parsons, available at https://github.com/js-parsons/js-parsons has been successfully integrated into the University of Adelaide's edX's MOOC platform as a component that can potentially be made available for other edX platforms. The demonstration presents the educators' interface to add new questions and receive students' results, along with students' perspective, including subgoals. The demonstration prototypes new feedback models when students encounter Parsons Problems errors, opening discussion up to the audience for opinions and input. This demonstration is intended for anyone wanting to know how to use Parsons Problems within MOOCs as a pedagogical approach; those seeking to incorporate MOOCs into their coursework with focused exercises; and those wanting to contribute to future Parsons Problems enhancements. Laptops are recommended, since participants will have the hands-on opportunity to evaluate the package during the demonstration.

Codification is a legal term defining the process of ordering rules corresponding to a plan. This term is applicable to Software Development to describe the ordering of program requirements into coding tasks. Codification is a meta-skill taught in advanced Software Engineering courses for large collaborative projects, though novices lack the experience to transfer this skill to small-scale assignments. As a result, novices form their own approach, potentially contributing to poor learning behaviors, poor self-efficacy, and course attrition. My research investigates an approach for introductory courses, enabling novices to plan before they implement a solution. The approach uses Socratic questions and Parsons Problems to relate the student's current knowledge base to the assignment. The hypothesis is that the pedagogy will reduce misconceptions about the assignment and enable students to create an ordered plan without extraneous cognitive load. The research explores this approach without adding objectives to the curriculum, or increasing the teacher's workload. Further investigation will be made to apply the pedagogy to other science, technology, engineering, and mathematics (STEM) disciplines.