This research-to-practice full paper discusses students' views on the role of generative artificial intelligence (GenAI) in their learning. The rapid integration of GenAI in educational settings has prompted significant interest in its implications for learning and academic integrity. This study investigates the adoption and impact of GenAI tools among computing students at a university, focusing on how they are utilized for educational purposes and their ethical implications. Semi-structured interviews with nine computing students were used to examine GenAI's specific use and timing. Additionally, it explores students' perceptions of the trustworthiness of GenAI outputs and identifies the students' ethical boundaries concerning its use in academic work. The findings reveal that while GenAI tools might enhance learning efficiency and provide substantial educational support, they raise significant ethical concerns, particularly regarding academic misconduct. The study highlights the need for educational strategies to navigate the challenges posed by GenAI technologies. Finally, three recommendations for computing education are outlined. This research contributes to the ongoing discourse on GenAI in education by describing the student's reflections on GenAI.

This Research-to-Practice WIP paper investigates how East Asian students studying in Sweden encounter various challenges. We discuss the discrepancy between our and previous findings and give recommendations. Background: Moving to and studying in a new country that is culturally different from their home country presents many challenges related to cultural differences to students. Intended Outcomes: This research aims to offer insights into how East Asian students adapt their learning strategies in Sweden and suggest how the university can support their lives and studies. Research Design: Six students were interviewed about their experiences studying in Sweden compared to studying at their home universities. Content analysis was performed to identify areas of interest. Findings: We report initial findings specifically related to academic and sociocultural issues.

This full research paper investigates upper-secondary students programming skills. In 2021, an initial survey assessed the programming skills of first-year upper-secondary school students, focusing on their ability to solve problems using block and text-based programming languages. The survey revealed significant differences between students with mandatory programming education (Group TECH) and those without (Group SCI), with Group TECH outperforming Group SCI across all categories. The follow-up survey in 2024 evaluated the same cohort to measure longitudinal changes in their programming knowledge and skills. The results showed marked improvements in Group TECH's performance, particularly in more complex programming tasks involving loops and embedded conditional statements, while Group SCI also showed progress, albeit to a lesser extent. The study found that students who engaged more frequently in programming activities outside of school demonstrated higher average scores. Overall, students in the study entered upper-secondary mostly prepared for programming topics mostly related to math like variables and conditionals in block programming languages. Students that left upper-secondary, after introductory programming, were better prepared to understand loops and complex problems in text.

In this full research-to-practice paper we study novice programming students learning process in the computer laboratory. Working with laboratory assignments is an important component when students learn to program. Here the assignments are intended to help students consolidate theoretical understanding and simultaneously train practice. However, it has been observed that the learning outcome of such laboratory sessions often is unsatisfactory. In this article we ask the question "How do novice students go about learning in the computer laboratory?" We analyse empirical data on novice students working in pairs in the laboratory, which is common in a first programming course. The data consists of video films of students where they discuss and solve programming problems, screen captures of what the students typed during the same laboratory session, and stimulated recall interviews with the students after the laboratory session. In the analysis we use an approach inspired by phenomenography and variation theory. We specifically focus on typical stages in the learning process when students learn in the programming laboratory. In doing so we have identified successful and less successful learning paths, where variation can play different roles. The stages identified in students’ learning process are I. Students first need to become aware of a lack of clarity. In the data we have identified different ways in which this necessary awareness was trigged; II. If, and in that case how, they resolve the lack of clarity. In all the stages we found successful and less successful ways in which students’ handle the situations. We illustrate the stages and discuss how and why variation may play different roles in the different stages of students’ learning, specifically focusing of the unsuccessful learning paths. Lastly, we discuss what these findings can tell us about how programming labs could be designed to promote learning in terms of helping students to avoid the unsuccessful paths identified.

This research work-in-progress paper presents findings related to upper secondary students’ understanding of programming concepts such as: (1) variable assignment, (2) if-statements, and (3) loops in Python and Scratch. In 2017, the Swedish education board added computer science curriculum to compulsory schooling. Students now entering upper secondary school are expected to have experience with programming. To find out how familiar first year upper secondary students are with programming, we assessed 172 students’ programming knowledge with a multiple choice questionnaire with block programming questions in Scratch and text programming questions in Python. Each question required students to read a program between 4-8 lines of code and correctly identify the output of the program. The questions included basic programming concepts such as: variable assignment, if-statements and loops. Additionally, we asked students to self-report their prior experiences with programming in terms of how many hours they had spent programming inside and outside of school, as well as if they had more experience with block or text programming. As expected, the students’ scores correlated positively with the amount of hours they self-reported to have spent programming inside and outside of school. In conclusion, we correlate students’ self-reported programming experience with their scores on the block and text sections and reason about these results based on teachers’ experience and research literature.

Student `copying' is often considered negatively as thoughts of plagiarism come to mind. Previously, we investigated the ways that instructors expect to use copying and imitation positively in their teaching. In this paper, we follow up that study by focusing on the student perspective and explore the ways in which students see copying and imitating as positive tools in learning to program (both at an introductory level and through more advanced learning of algorithms, etc.). In a qualitative research study, using semi-structured interviews, students were asked about how they use copying positively - their goals when they copy, how they go about it, how they view the experience of copying, and results beyond simply fulfilling their immediate goals. When comparing student results with the previous study, it was noted that there is some level of agreement between instructors and students about how copying can be useful for learning to program. There is also some degree of mismatch between instructor and student views. Students did not report imitating instructors' approaches to learning and sometimes were unsure about whether they were supposed to copy the materials that they were given. This leads to some teaching suggestions in terms of instructors being more explicit in their attitude to this type of `legitimate' copying and imitation.

The goal of most computing education research is to effect positive change in how computing is taught and learned. Statistical techniques are one important tool for achieving this goal. In this paper we report on an analysis of ICER papers that use inferential statistics. We present the most commonly used techniques; an overview of the techniques the ICER community has used over its first 14 years of papers, grouped according to the purpose of the technique; and a detailed analysis of three of the most commonly used techniques (t-test, chi-squared test, and Mann-Whitney-Wilcoxon). We identify common flaws in reporting and give examples of papers where statistics are reported well. In sum, the paper draws a picture of the use of inferential statistics by the ICER community. This picture is intended to help orient researchers who are new to the use of statistics in computing education research and to encourage reflection by the ICER community on how it uses statistics and how it can improve that use.

Students' "copying" is often considered negatively. In this paper, we explore the ways in which copying and imitation are used positively by computing instructors in their teaching. We found that instructors expect their students to use these strategies in many different contexts and at many different levels.

There is a continued need for basic research on how beginners learn programming across different contexts. In research aiming at measuring effects of learning, ways of measuring programming knowledge are needed. The aim of this paper is to describe a process of establishing validity of a context-specific assessment tool for novice programming. Our case is an ongoing research project that investigates the effects of and mechanisms behind learning computing programming from hands-on experiences. For that context, we have constructed and validated an assessment tool i.e., a programming knowledge test with a connected scoring rubric based on an introductory three-hour teaching session. We relied on three ways of arguing for how our assessment of knowledge is considered valid in this process: previous research, an empirical sample, and intersubjective knowledge judgments. The main result of our study is the test with the scoring rubric and our arguments for its validity. We also present some tentative findings from the research project on the role of hands-on in learning programming. Both the area of hands-on, as well as the context-specific process, seem to be fruitful topics of future research, particularly combined with a pragmatic action and communication theory.

Conferences such as ITiCSE have recently seen an increase in the number of papers presenting empirical research in computing education. While empirical research need not be quantitative, there has been a corresponding increase in the number of papers that present some level of statistical analysis to support their arguments. This poster introduces a project that is exploring how -- and how well -- statistics are used and reported in computing education research.

In a survey of the computing education community, many respondents suggested "active learning" as a teaching approach that would increase the likelihood of student success. In light of these responses, we analyze the way in which active learning is described in the computing-education literature. We find a strong consensus that active learning is good, but a lack of precision in how the term is used, often without definition, to describe instructional techniques, rather than student learning. In addition, active learning techniques are often discussed as if they were all equally effective. We suggest that making clear distinctions, both between teaching techniques and active learning and among the teaching techniques, would be fruitful for both instructors and researchers. Finally, we propose some dimensions along which distinctions among techniques could usefully be made.

We replicate a study and extend previous research that examined graduating students' achievement and understanding when asked to "produce a design". In this paper, we examine software designs produced by students at an earlier stage in their undergraduate studies -- the midpoint. We were looking for characteristics of the development of skill at software design as students progress through the curriculum. These students did about as well as graduating students from the same institution in terms of the quality of their software designs, although they failed to produce as many complete designs. In addition to attributes uncovered in previous research, a new design attribute was noticed -- meaningful links between static components. We raise the question of where GUI designs fit in the area of software design.

In a survey of the CS-education community, we find a range of beliefs about the "geek gene" theory. We suggest an alternative term, the "geekiness quotient (GQ)". The GQ, grounded in Gardner's work on multiple intelligences, is a hypothetical measure of the student's current CS ability. The GQ supports a moderate view of the geek gene: that students arrive in our classrooms with a range of CS abilities, whether acquired through background or innate talent, and can improve their abilities through effort.

In this article, we address the question of why computing students choose to learn computing topics on their own. A better understanding of why some students choose to learn on their own may help us to motivate other students to develop this important skill. In addition, it may help in curriculum design; if we need to leave some topics out of our expanding curriculum, a good choice might be those topics that students readily learn on their own. Based on a thematic analysis of 17 semistructured interviews, we found that computing students’ motivations for self-directed learning fall into four general themes: projects, social and peer interactions, joy of learning, and fear. Under these, we describe several more specific subthemes, illustrated in the words of the students. The project-related and social motivations are quite prominent. Although these motivations appear in the literature, they received greater emphasis from our interviewees. Perhaps most characteristic of computing is the motivation to learn to complete some project, both projects done for fun and projects required for school or work.

The rapid development in the IT area brings a series of shifts, in underlying theories, technology and work practices. In the normal course of events, most changes are evolutionary, with small, incremental improvements being made to theoretical understanding or practical application. Occasionally, however, changes occur of such magnitude that they do not just alter current operational practice but require a thorough reappraisal of the underlying assumptions on which that practice is based. In short, they require a review of the dominant way of thinking, or paradigm. Ability to adapt to evolving circumstances is critical, not only for industrial and commercial organisations, but also for individual employees, to survive and remain successful during paradigm shifts. How can we prepare our students so they can survive in a working life characterized by frequent paradigm shifts? It is important that CS and IT education prepares students for coping with shifts induced by technological development in their future careers, that students develop the competencies needed. To understand what competencies are needed, it is important to investigate characteristics among employees that have flourished in earlier paradigm shifts and not least to build knowledge on how to develop learning environments where such competencies and personal characteristics can be achieved This paper is about setting the stage for an action research project aimed at enhancing education with regard to being able to survive paradigm shifts in the IT industry.

We expand upon previous research that looked at the question: "Can graduating students design software systems?" Specifically we want to examine students' understanding of the phenomenon "produce a design." What does this instruction mean to them? In order to investigate student understandings, we examined their designs using a phenomenographic approach.

During the past two years, Massive Open Online Courses (MOOCs) have created wide interest in the academic world raising both enthusiasm for new opportunities for universities and many concerns for the future of university education. The discussion has mainly appeared in non-scientific forums, such as magazine articles, columns and blogs, making it difficult to judge wider opinions within academia. To collect more rigorous data we surveyed teachers, researchers, and academic managers on their opinions and experiences of MOOCs. In this paper, we present our analysis of responses from the computer science academic community (n=137). Their feelings about MOOCs are highly mixed. Content analysis of open-ended questions revealed that the most often mentioned positive aspects included affordances of MOOCs, freedom of time and location for studying, and the possibility to experience teaching from top-level international teachers/experts. The most common negative aspects included concerns about pedagogical designs of MOOCs, assessment practices, and lack of interaction with the teacher. About half the respondents claimed they had not changed their teaching as a result of MOOCs, a small number used MOOCs as learning resources and very few were engaging with MOOCs in any significant way.

The McCracken et al. working group paper is often cited for the proposition that students can't program. In that study, students from four different institutions were each assigned to implement one of three versions of a calculator. More than half of the students failed to produce a program that compiled and executed in the time assigned.

Threshold concepts can be used to both organize disciplinary knowledge and explain why students have difficulties at certain points in the curriculum. Threshold concepts transform a student's view of the discipline; before being learned, they can block a student's progress.

Research has shown that most learning in the workplace takes place outside of formal training and, given the swiftly changing nature of the field, computer science graduates more than most workers, need to be able to learn computing topics outside of organized classes.

Phenomenographic research studies have identified different understandings of the concepts class and object by novice programmers. Aspects of understanding include a focus on artefacts of text, syntax and structure (text), as active agents in a program (action) and as models of an external reality (model). We explore the hypothesis that these aspects of conceptual understanding form a hierarchy in which mastery of the text aspect is a necessary precondition for understanding objects as active agents and the action aspect is a precondition for model understandings. We use empirical data from the final examination of an introductory programming course to test the relationship between the text and action aspects. Our findings do not support the hypothesis of a hierarchy but rather suggest that text and action understandings develop in parallel.

Transformational biographies are used to shed light on situations when students have experienced 'surprise.' These biographies are examined in light of a philosophical position that surprise is useful in engendering learning, and, that as educators, we should try and set up situations in which our students experience it more often. The paper reports on a grounded-theory inspired examination of 108 students' transformational biographies for evidence of surprise. Students clearly perceived an 'anomaly or contradiction to a previous belief or understanding' in just under half of them. The paper then goes on to further identify different kinds of surprise and the triggers that caused them and concludes with suggestions for enhancing learning by capitalizing on students' surprise.

We examine the transformations experienced by students during their study of computing. These transformations led to changes in the students' perceptions of computer science, in their sense of identity as computer scientists, their behavior and their confidence. The changes are caused by learning or using particular concepts, and often associated with writing computer programs, learning new programming languages, or interacting with peers.

This paper examines transformational learning experiences of computing students as a way to better understand threshold concepts in computing. From empirical evidence we found that students often describe transformative experiences as learning situations in which they were led to use various kinds of abstraction, for example modularity, data abstraction, inheritance, polymorphism, reuse, design patterns, and complexity. Some students describe an abstract concept as coming first, and then needing to be made concrete though application; others describe transformations in which they learn the advantages of these abstract concepts from their experience of not using them.

In this paper, we present the results of an experiment in which we sought to elicit students' understanding of object-oriented (OO) concepts using concept maps. Our analysis confirmed earlier research indicating that students do not have a firm grasp on the distinction between "class" and "instance." Unlike earlier research, we found that our students generally connect classes with both data and behavior. Students rarely included any mention of the hardware/software context of programs, their users, or their real-world domains. Students do mention inheritance, but not encapsulation or abstraction. And the picture they draw of OO is a static one: we found nothing that could be construed as referring to interaction among objects in a program. We then discuss the implications for teaching introductory OO programming.

This paper is part of an ongoing series of projects in which we are investigating "threshold concepts": concepts that, among other things, transforms the way a student looks as the discipline and are often troublesome to learn. The word "threshold" might imply that students cross the threshold in a single "aha" moment, but often they seem to take longer. Meyer and Land introduce the term "liminal space" for the transitional period between beginning to learn a concept and fully mastering it.

Students often get stuck when trying to learn new computing concepts and skills. In this paper, we present and categorize strategies that successful students found helpful in getting unstuck. We found that the students reported using a broad range of strategies, and that these strategies fall into a number of recognizably different categories.

Yes, and Yes.We are currently undertaking an pirical investigation of "Threshold Concepts" in Computer Science, with input from both instructors and students. We have found good pirical evidence that at least two concepts---Object-oriented programming and pointers--are Threshold Concepts, and that there are potentially many more others.In this paper, we present results gathered using various experimental techniques, and discuss how Threshold Concepts can affect the learning process.

This paper describes Threshold Concepts, a theory of learning that distinguishes core concepts whose characteristics can make them troublesome in learning. With an eye to applying this theory in computer science, we consider this notion in the context of related topics in computer science education.

This paper examines software designs produced by students nearing completion of their Computer Science degrees. The results of this multi-national, multi institutional experiment present some interesting implications for educators.

What is 'programming thinking'? In a study, first year students were interviewed on their understanding of what learning to program means. Many students talked about learning to program in terms of learning a special way to think, different from other subjects studied. Many of these students had problems in describing what this special way to think included. The analysis of the interviews revealed some features of this thinking, as expressed by the students. In this paper we discuss and analyse 'programming thinking' using phenomenography as our research approach [7]. Our results are coherent with Hazzan's research on the learning theory 'process-object duality' [4], but points to problems in learning of object-oriented programming not indicated in 'process-object duality'. In comparing the results form our own study with this learning theory, we discuss what this might mean in learning object-oriented programming.

Problems with understanding concepts, so called misconceptions, have been investigated and reported in a number of studies regarding object-oriented programming [4], [3]. In a first programming course using an object-oriented language, it is of great importance that students get a good understanding of central concepts like object and class at an early stage of their education. We have, with a phenomenographic research approach, performed a study with first year university students, investigating what an understanding of the concepts object and class includes from a student perspective. By applying variation theory [8] to our results we are able to pin-point what the students need to be able to discern in order to gain a rich understanding of these concepts.