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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 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.

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.

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.

Knowledge of computer programming is increasingly important in society. Many countries have introduced programming into the school curriculum. Programming students’ learning has been studied from many perspectives, one being the importance of students working hands-on with programming problems. The explanations and underlying factors for this are however less researched.

In a controlled study with upper secondary school students (n=53) learning basic Java programming for three hours, we studied how factors like learning outcome, engagement, motivation, stress, and long-term memory are affected by hands-on and hands-off learning respectively. Students worked in pairs to solve programming problems. In each pair, one student was randomly selected to write the code hands-on, while the other student contributed hands-off. The roles did not switch during the session.

We used tests and questionnaires to assess the learning outcome and some other aspects of relevance for learning. Statistical analysis of the results showed that working hands-on reduced stress. There was no difference in knowledge gain immediately after the teaching, but the hands-on group did slightly better on a follow-up test one week later. The results are discussed in relation to research about, e.g., stress, long-term learning, and novices learning to program.

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.