Abstract Contributor turnover is a major challenge in open-source software (OSS) projects, affecting project sustainability, quality, and many other dimensions of project performance. Although turnover has been widely studied in the OSS context, the importance of sustained contribution periods to long-term contributor survival remains poorly understood. In this context, contributor survival refers to the probability of sustained activity over time. We investigate the effect of contribution streaks, defined as consecutive months of contribution, on long-term survival, and analyze what kind of engagement, at different levels of ownership, predicts short-term contribution streaks. Using five years of data from 14 open-source projects, we applied landmark analysis to understand long-term survival based on peak streak lengths and logistic regression to identify the activities supporting short-term streaks. In both cases, we identified a three-month contribution streak to be an important threshold for sustained contributions. We found that development and communication related activities across ownership levels are important predictors of whether contributors continue their streaks, even though the relative importance shifts over time. In addition, we found that prior experience plays a stronger role early on, but its effect diminishes during later streak months. These findings suggest that contribution streaks are important predictors of long-term survival and provide practical insights for open-source practitioners on how to improve the retention of new and existing contributors through supporting short-term contribution streaks.

Abstract The increasing adoption of hybrid and remote work has reshaped how software teams communicate, collaborate, and make design decisions. Our prior study showed that hybrid setups often result in missed discussions, fragmented understanding, and limited documentation of design rationale. At the same time, Generative AI (GenAI) tools–such as GitHub Copilot and ChatGPT–are becoming embedded in development workflows, providing support in code generation. While GenAI’s role in software development is gaining attention, its impact on early-stage design ideation activities and collaboration in hybrid teams remains underexplored. This paper builds on earlier findings to propose a forward-looking vision of GenAI as an instant, readily available co-developer in hybrid software design. We revisit documented challenges from our earlier study on hybrid collaboration and outline how GenAI could address these issues by facilitating asynchronous participation, surfacing undocumented rationale, and preserving design continuity. We present a conceptual framing of GenAI-supported collaboration and propose a research agenda to guide future studies on integrating GenAI into hybrid settings.

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.

Coding camps bring together individuals from diverse backgrounds to tackle given challenges within a limited timeframe. Such camps create a rich learning environment for various skills, some of which are directly associated with the camp, and some of which are a result of working as a team during the camp. Unfortunately, coding camps often remain isolated from the broader educational curriculum or other bigger context, which downplays the opportunities they can offer to students. In this paper, we present the vision of the European initiative OSCAR, which aims at connecting coding camps to the educational and professional context faced by the learners. In addition, we sketch a supporting platform and its features for connected coding camps.

Abstract Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This position paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team’s work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone courses are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of project-based software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. In a systematic literature review for 2007–2022, we identified 127 articles describing real-world capstone courses. These articles were analysed based on their presented course characteristics and the reported course outcomes. The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4–5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses generally include various forms of student assessment both during and at the end of the course. This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We also further synthesise insights on the reported course outcomes. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.

Teachers often preach for their students to start working on assignments early. There is even a fair amount of scientific evidence that starting early is beneficial for learning. In this work, we investigate students’ time management behavior in a second-year project-based software engineering course. In the course, students work on a software project in small groups of four to six students. We study time management from multiple angles. Firstly, we conduct an exploratory factor analysis and study how different time management related behavioral metrics are related to one another, for example, whether individual students’ time management practices in the second-year group project-based course are similar to their earlier time management practices in first-year courses where students work on assignments individually. Understanding how students’ previous time management behavior is manifested in later project-based courses would be beneficial when designing project-based education. Secondly, we study whether students’ time management practices affect the peer-review scores they get from their group members. Lastly, we explore how time management affects course performance. Our findings suggest that time management behavior, even from courses taken in the past, can be used to predict how students perform in future courses.

Full research paper-Team configuration, work practices, and communication have a considerable impact on the outcomes of student software projects. This study observes 150 college students who first individually solve exercises and then carry out a class project in teams of three. All projects had the same requirements. We analyzed how students' behavior on individual pre-project exercises predict team project outcomes, investigated how students' time management practices affected other team members, and analyzed how students divided their work among peers. Our results indicate that teams consisting of only low-performing students were the most dysfunctional in terms of workload balance, whereas teams with both low-and high-performing students performed almost as well as teams consisting of only high-performing students. This suggests that teams should combine students of varying skill levels rather than allowing teams with only low performers or letting students to form teams without constraints. We also observed that individual students' poor time management practices impair their teammates' time management. This underlines the importance of encouraging good time management practices. Most teams reported that they divided tasks in a way that is beneficial for the acquisition of technical skills rather than collaboration and communication skills. Only a few teams assigned tasks so that students would have worked only on tasks they already knew and thus felt most comfortable to work with.

Full research paper—Online education can be delivered in many ways. For example, some MOOCs let students to proceed with their own pace, while others rely on strict schedules. Although the variety of how MOOCs can be organized is generally well understood, less is known about how the different ways of organizing MOOCs affect retention. In this work, we compare self-paced and fixed-schedule MOOCs in terms of retention and work-load. Using data from over 8.000 students participating in two versions of a massive open online course in programming, we observe that drop-out rates at the beginning of the courses are greater than towards the end of the courses, with self-paced MOOC being more extreme in this respect. Mostly because of different starts, the fixed-schedule course has a better overall retention rate (45%) than its self-paced counterpart (13%). We hypothesize that students initial investment of time and effort contributes to their persistence in their course, meaning that they do not want to let their initial investment go to waste. At the same time, in both self-paced and fixed-schedule MOOCs, there are students who receive almost full points from one week but fail to continue to the next week. This suggests that the issue of dropouts in MOOCs may also be related to participants struggling to take up new tasks or schedule their work over a longer time period. Our results support scheduling student activities in open online courses and opens up new research directions in engaging students in self-paced courses.

Previous research suggests that structured learning diaries can increase students' self-regulation skills. However, learning diaries also imply great effort for students, and more research is needed to understand the effect of diaries on students' motivation and engagement. In the current study, we investigate whether our approach of using curricular concept maps as structured learning diaries has an effect on students' self-regulation or study engagement. 104 first-year engineering master's students were randomly assigned to experimental and control groups. The structured learning diary using a digital tool was a compulsory weekly assignment for the experimental group. Both groups completed pre- and post-test questionnaires on self-regulation and study engagement. Using Repeated Measures ANOVA, we did not find statistically significant differences between the experimental and the control groups in self-regulation or study engagement. However, with a more fine-grained grouping based on diary usage, we found a statistically significant decrease in passive diary users' dedication (part of study engagement). Our results indicate that making students actively use reflection tools such as structured learning diaries remains a challenge. Moreover, such an intensive intervention may even have negative effects on study engagement for students who do not actively use the tool.

It has been suggested that performance goals focused on appearing talented (appearance goals) and those focused on outperforming others (normative goals) have different consequences, for example, regarding performance. Accordingly, applying this distinction into appearance and normative goals alongside mastery goals, this study explores what kinds of achievement goal orientation profiles are identified among over 2000 students participating in an introductory programming MOOC. Using Two-Step cluster analysis, five distinct motivational profiles are identified. Course performance and demographics of students with different goal orientation profiles are mostly similar. Students with Combined Mastery and Performance Goals perform slightly better than students with Low Goals. The observations are largely in line with previous studies conducted in different contexts. The differentiation of appearance and normative performance goals seemed to yield meaningful motivational profiles, but further studies are needed to establish their relevance and investigate whether this information can be used to improve teaching.

Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for improving old applications, particularly in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex—data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce the Human Data Model—a new tool and a programming model for programmers and end users with scripting skills that help combine data from various sources, perform computations, and develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run on almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the work is inspired by several interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model.

Since 2012, part of computer science student body at the University of Helsinki has been selected by using a massively open online version of the same introductory programming course that our freshmen take. In this multi-year study, we compare study success between students accepted through the online course (MOOC intake) and students accepted through the traditional entrance exam and high school matriculation exam based intake (normal intake). Our findings indicate that the MOOC intake perform better in computer science studies when looking at completed credits and grade point average, but there is no difference when considering other courses. Retention among the MOOC intake is better than among the normal intake. Additionally, students in the MOOC intake are more likely to complete their capstone project and Bachelor's thesis in the studied time-frame. However, the MOOC intake makes the already skewed gender balance more pronounced.

Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area.

Research on the indicators of student performance in introductory programming courses has traditionally focused on individual metrics and specific behaviors. These metrics include the amount of time and the quantity of steps such as code compilations, the number of completed assignments, and metrics that one cannot acquire from a programming environment. However, the differences in the predictive powers of different metrics and the cross-metric correlations are unclear, and thus there is no generally preferred metric of choice for examining time on task or effort in programming. In this work, we contribute to the stream of research on student time on task indicators through the analysis of a multi-source dataset that contains information about students' use of a programming environment, their use of the learning material as well as self-reported data on the amount of time that the students invested in the course and per-assignment perceptions on workload, educational value and difficulty. We compare and contrast metrics from the dataset with course performance. Our results indicate that traditionally used metrics from the same data source tend to form clusters that are highly correlated with each other, but correlate poorly with metrics from other data sources. Thus, researchers should utilize multiple data sources to gain a more accurate picture of students' learning.

Due to the increased enrollments in Computer Science education programs, institutions have sought ways to automate and streamline parts of course assessment in order to be able to invest more time in guiding students' work.

Large-scale courses such as Massive Online Open Courses (MOOCs) can be a great data source for researchers. Ideally, the data gathered on such courses should be openly available to all researchers. Studies could be easily replicated and novel studies on existing data could be conducted. However, very fine-grained data such as source code snapshots can contain hidden identifiers. For example, distinct typing patterns that identify individuals can be extracted from such data. Hence, simply removing explicit identifiers such as names and student numbers is not sufficient to protect the privacy of the users who have supplied the data. At the same time, removing all keystroke information would decrease the value of the shared data significantly.

Patching a program during its execution without restarting is called dynamic software updating (DSU). DSU is well acknowledged in research, but rarely applied in practice as witnessed by constant need for reboots and restarts of both applications as well as operating systems. This raises the question of how well DSU related techniques are supported in education. In this paper, we review how the major software engineering and education guides acknowledge dynamic software updating techniques. Our analysis indicates that although DSU is not explicitly mentioned in the guides, the need is already well motivated and many DSU concepts are implicitly supported. Based on this, we argue that DSU could be introduced as an optional topic in software engineering studies.

We have observed students teams on a software engineering project course to understand what software engineering practices they end up using and how do they experience the usefulness of the selected practices and tools. In our context, the most often applied practices and tools were planning meeting, commitment to using tasks, self-selected communication tools, a revision control, and project management systems. We found out that student valued various face-to-face activities the most - even more than they were able to practice them. Finally, the wide variety of communication tools students take even from their leisure time and apply in the course setting surprised us.

A small number of recent studies have suggested that learning is enhanced when the illustrations in instructional materials are designed to appeal to the learners' emotions through the use of color and the personification of key elements. We sought to replicate this emotional design effect in the context of introductory object-oriented programming (OOP). In this preliminary study, a group of freshmen studied a text on objects which was illustrated using anthropomorphic graphics while a control group had access to abstract graphics. We found no significant difference in the groups' scores on a comprehension post-test, but the experimental group spent substantially less time on the task than the control group. Among those participants who had no prior programming experience, the materials inspired by emotional design were perceived as less intelligible and appealing and led to lower self-reported concentration levels. Although this result does not match the pattern of results from earlier studies, it shows that the choice of illustrations in learning materials matters and calls for more research that addresses the limitations of this preliminary study.

The methodology of eye tracking has been gradually making its way into various fields of science, assisted by the diminishing cost of the associated technology. In an international collaboration to open up the prospect of eye movement research for programming educators, we present a case study on program comprehension and preliminary analyses together with some useful tools.

Achievement badges are increasingly used to enhance educational systems and they have been shown to affect student behavior in different ways. However, details on best practices and effective concepts to implement badges from a non-technical point of view are scarce. We implemented badges to our learning management system, used them on a large course and collected feedback from students. Based on our experiences, we present recommendations to other educators that plan on using badges.

We present a design and open source implementation for a service oriented e-learning system, which utilizes external services for supporting a wide range of learning content and also offers a REST API for external clients to fetch information stored in the system. The design will separate different concerns, such as user authentication and exercise assessment, into separate services, which together form a complete e-learning environment. A key component of the design is identifying a general set of characteristics among existing exercise assessment systems, by which the assessment methods are grouped into three types: synchronous, asynchronous and static exercises.

The process of solving a programming assignment is generally invisible to the teacher. We only see the end result and maybe a few snapshots along the way. In order to investigate this process with regard to Parsons problems, we used an online environment for Parsons problems in Python to record a detailed trace of all the interaction during the solving session. In these assignments, learners are to correctly order and indent a given set of code fragments in order to build a functioning program that meets the set requirements. We collected data from students of two programming courses and among other analyses present a visualization of the solution path as an interactive graph that can be used to explore such patterns and anomalies as backtracking and loops in the solution. The results provide insights into students' solving process for these types of problems and ideas on how to improve the assignment environment and its use in programming education.

We introduce js-parsons - MIT licensed JavaScript widget to embed Parson's puzzles to any HTML. The novelty of js-parsons is the 2-dimensional drag-and-drop of the code lines. Firstly, the code lines in the solution need to be in correct order as in the existing solutions. Secondly, since our exercises present Python, code blocks are created by indenting the code lines. This is done by drag-and-dropping the lines in horizontal direction. In addition, js-parsons can record how puzzles are solved and send the logs to a server. We hope to use the logging feature to understand how students solve puzzles and how puzzles should be designed to be more effective.

Because of the web, JavaScript (JS) is one of the most popular programming languages today. Despite the importance, JS is rarely in the core of programming courses. Although JS might not be in the core, it has still a role in many courses. In this paper we introduce an open source tool to create small, automatically assessed JavaScript programming exercises. Sources of the tool are available online. Automatic assessment is based on unit tests, JSLint and various software metrics. The fact that the assessment happens inside the student's own browser is the novelty of our work. Installation and sandboxing of a server are not required. This makes it easy to add exercises into any web page. The downside is that exercises are for self study purposes since grades submitted from a browser could be tampered with.

The idea of using visualization technology to enhance the understanding of abstract concepts, like data structures and algorithms, has become widely accepted. Several attempts have been made to introduce a system that levels out the burden of creating new visualizations. However, one of the main obstacles to fully taking advantage of algorithm visualization seems to be the time and effort required to design, integrate and maintain the visualizations.Effortlessness in the context of algorithm visualization is a highly subjective matter including many factors. Thus, we first introduce a taxonomy to characterize effortlessness in algorithm visualization systems. We have identified three main categories based on a survey conducted among CS educators: i) scope, i.e. how wide is the context one can apply the system to ii) integrability, i.e., how easy it is to take in use by a third party, and iii) interaction techniques, i.e., how well does the system support different use cases regularly applied by educators. We will conclude that generic and effortless visualization systems are needed. Such a system, however, needs to combine a range of characteristics implemented in many current AV systems.