Opportunities for and inclinations towards academic dishonesty among students have emerged as prominent topics of contemporary discourse. Some reasons for this are new assessment methods, some of which were introduced during the COVID-19 pandemic, and the potential for assistance from generative AI tools. To gain a comprehensive understanding of computing students' perceptions of academic dishonesty, we conducted a survey study based on the seminal work done by Sheard and Dick at the turn of the century and ten years later, which focused on the perceptions of computing students at two Australian universities [19-22]. Computer science and engineering students at our institution were asked about their definition of academic dishonesty, its prevalence, the motivations and deterrents for engaging in such behaviour, and strategies to mitigate it. The survey was administered to students across all five year levels of the programme in 2021 (before the advent of GitHub Copilot and ChatGPT) and again in 2023. The results of these two surveys are compared with each other and against the results of the Sheard and Dick study of 2010. Additionally, we conducted a comparative analysis on the responses of students in different academic years to determine if fifth-year students exhibit more mature perceptions of academic dishonesty than first-year students. To further explore student attitudes towards cheating within the context of generative AI, a specialised survey was conducted in 2024, including an open-ended query. A thematic analysis of the responses to this question revealed seven distinct themes.

Fluency is a term commonly used to express proficiency within a specific area, often languages. However, it is sometimes also used in programming. In this poster, we present a phenomenographic study to investigate how CS students understand the term.

When trying to understand student success in computer science, much of the attention has been focused on CS1, leaving follow-up courses such as CS2 less researched. Prior studies of CS2 have often taken a deductive approach by focusing on predetermined variables such as CS1 grades, the impact of different paths from CS1 to CS2, gender and race. Although this has resulted in a better insight into these variables, we wonder if there might be another way of viewing which variables affect the students' success in the course. We have therefore chosen an inductive approach to better understand what these variables might be and how they interplay. This was done by analysing 16 semi-structured interviews with students enrolled in CS2 who have another speciality than computer science. The interviews focused mainly on the students' methods for succeeding in the course, experiences of the course and programming background. Through a thematic analysis of the interviews, we found the following five main success variables for CS2: programming competence, computer literacy, opportunity to receive help, ability to help oneself and teaching. These variables can in several cases be related to the ones previously addressed, however, they can also offer a different perspective on student success in the course.When trying to understand student success in computer science, much of the attention has been focused on CS1, leaving follow-up courses such as CS2 less researched. Prior studies of CS2 have often taken a deductive approach by focusing on predetermined variables such as CS1 grades, the impact of different paths from CS1 to CS2, gender and race. Although this has resulted in a better insight into these variables, we wonder if there might be another way of viewing which variables affect the students' success in the course. We have therefore chosen an inductive approach to better understand what these variables might be and how they interplay. This was done by analysing 16 semi-structured interviews with students enrolled in CS2 who have another speciality than computer science. The interviews focused mainly on the students' methods for succeeding in the course, experiences of the course and programming background. Through a thematic analysis of the interviews, we found the following five main success variables for CS2: programming competence, computer literacy, opportunity to receive help, ability to help oneself and teaching. These variables can in several cases be related to the ones previously addressed, however, they can also offer a different perspective on student success in the course.

Teaching assistants (TAs) are often used in computer science (CS) courses to conduct tutorials in smaller groups and to be able to provide students with one-to-one help sessions. TAs can also assist with the assessment, which can be time-consuming in large classes. Previous research does, however, indicate that TAs can be poorly prepared for their work tasks. In this experience report, we present a TA training course that addresses this issue and prepares new TAs for their responsibilities. This includes conducting tutorials, tutoring and providing students with useful feedback in lab settings, and conducting assessments. In addition, we also aimed at creating a safe space for our TAs to discuss challenges and fears that they foresee or have already experienced as TAs. To do this, we developed an introductory course consisting of five modules, that are based on previous research on TAs in CS and in the Technological Pedagogical Content Knowledge (TPACK) framework. The modules are: HR questions, classroom teaching, helping and supervising in lab sessions, assessment, and reflection and discussion. The course is given in a blended learning format, where the first three modules are student-paced online modules, while the last two are synchronous meetings. To evaluate the training, we present course evaluation results from 53 TAs who enrolled in this 6-hours course during the 2020/2021 academic year. We conclude that this TA training course has been well appreciated by almost all the participants, discuss lessons learned and future plans.

The COVID-19 pandemic caused many universities to switch from on-campus education to distance education, in just a couple of days. In this study, we investigate how computer science (CS) majors experienced the emergency remote education during the first months of the pandemic. We sent out a survey to bachelor's and master's students, which yielded 794 responses. In the survey, we asked the students to compare the online education to their on-campus education on a number of aspects, such as motivation, experienced stress and the possibility to collaborate with other students. We also asked the students to compare the quality of learning activities, such as lectures and tutorials. The results show that students preferred the on-campus setting in all aspects, but on open-ended questions about advantages, the students stated the new education format made their schedule more flexible, increased accessibility of learning material, and reduced commuting time. We also identified some differences between the students' year of study, implying that different courses did not adapt to the new conditions equally well.

In this full paper on innovative practice, we describe and discuss findings from dual degree study programmes that combine a master's degree in engineering with a master's degree in education. This innovative study programme design has emerged in Sweden due to an alarming demand for more Upper Secondary School teachers in STEM subjects. Studies on alumni from these programmes indicate that the graduates are highly appreciated not only as teachers in schools, but also in business and industry, e.g. in roles as IT consultants and computer science engineers. Data indicate that the breadth of the combined education, and especially leadership and pedagogical skills, are important factors for these graduates' success as engineers.

This full paper presentation starts with the understanding that in the present-day audit society, in which most academics also find themselves, assessment and probing are usually done from positions of higher authority, and not always with quality improvement as the main driving force. In the context of teaching and learning in higher education, this can result in pedagogical evaluations that seem to take uncalled-for interest in areas of little concern to teachers, potentially omitting areas of greater interest to them. In contrast, this paper describes the process and outcomes of a grass-roots initiative to assess the experiences and attitudes toward teaching and learning at the university among peers. In 2014, a university-financed project was launched at KTH Royal Institute of Technology to engage more teachers in the university's educational development work. The university initially selected 24 self-nominated teachers who were given relatively free reins to work on educational development projects. This project was one of them. The assessment took the form of a survey targeting all teaching - or potentially teaching - faculty at the university, carried out first in 2016 and then repeated in 2019. The first survey was sent out to 2,204 respondents, and the second to 2,534 respondents, with response rates of 20% and 17% respectively. This paper will describe the process of designing these surveys, looking at what was asked about and what was not asked about, as well as the process of getting internal approval for sending them out to a survey-fatigued faculty. The paper also reports on the results in terms of both some key findings and in terms of the effect of the survey work itself on different stakeholders and potential agents of change at the university. Most importantly, however, this paper will discuss the conditions and prerequisites for grass-roots initiatives such as this to succeed, based on the literature and on the experiences gained through the survey work. Grounded in this discussion, we will suggest strategies to help teachers at other universities successfully repeat this or similar endeavours.

This Research, Full Paper presents a study on the experiences of teaching assistants (TAs) in Computer Science (CS) courses. TAs are widely used in CS education, to reduce the workload of faculty. A way to allow smaller groups of students in tutorials and individual guidance in lab sessions, even though the numbers of students in the classes are increasing. The aim of this study is to describe the TAs, own experiences of their role, which work tasks they have and how they are carried out. Fifteen semi-structured interviews with TAs, who were themselves studying towards a bachelor's or master's degree, were carried out and analyzed using thematic analysis. The results indicate that the TAs typically work alone with preparing the material for the tutorials and that they are given a high amount of freedom both regarding which examples to explain and which pedagogical approaches to use. During the lab sessions the students conduct lots of different tasks, related to assessment and tutoring. In the paper, we present a framework with the identified work tasks, with descriptions on how they were carried out. The thematic analysis resulted in six role descriptions; as a teacher, friend, tutor, grader, mentor and apprentice. Combining the roles as tutors and graders, can be experienced as tricky, especially if the instructions and grading criteria are lacking details. The TAs can also view themselves as both a teacher and a friend to their students, which could cause conflicts of interest to arise. The TAs could experience to be an apprentice to the course coordinator and senior TAs, but also to be a mentor for the students and more novice TA colleagues. The implications of this study could be of use for faculty who employ TAs in their courses or are conducting or planning TA training sessions. Based on the result, we have made some recommendations for TA training.

Programme Integrating Courses (PICs) aim to tie students, teachers and courses in education programmes closer together. In this study, we investigate three PICs, as part of engineering programmes in computer science and media technology. The purpose of this study was to gain a deeper understanding of how students and mentors experience the PICs with a focus on the assessment and the relationship between students, and students and mentors. We used a mixed method approach, interviewed 22 students and 6 mentors, and sent out questionnaires to all 25 mentors and all students from two of the three courses (630+470 students). The results showed that the students and mentors appreciated the social aspects of the courses, getting to know each other and share experiences. However, some were uncomfortable reflecting upon the given non-technical topics. On a general level, the students stated that their mentors assessed their reflections correctly but they were sceptical towards being graded on a scale other than pass/fail.

This working group contributes to formulating a framework for modeling competencies in the current and future disciplines that comprise computing education. We draw upon the innovative approach taken in the curricular document for information technology (IT2017), curricular competency frameworks, other related documents such as the software engineering competency model (SWECOM), the Skills Framework for the Information Age (SFIA), current research in competency models, and elicitation workshop results from other computing conferences. The outcomes contribute to the Computing Curricula 2020 (CC2020) project, and include the formulation and review of sets of disciplinary-relevant competencies for use in computing education. This work directly informs the CC2020 project sponsored by the Association for Computing Machinery (ACM) and the IEEE Computer Society.

The aim of this work is to investigate a new method of involving all students in the continued development of an educational program. Using this method, we have obtained a list of well-scrutinized suggestions for improvement that have support among the students, and that we can start to implement. We have also saved a large pool of suggestions that could be used in the future.

When compared to earlier programming and data structure experiences that our students might have, the perspective changes on computers and programming when introducing theoretical computer science into the picture. Underlying computational models need to be addressed, and mathematical tools employed, to understand the quality criteria of theoretical computer science. Focus shifts from doing to proving. Over several years, we have tried to make this perspective transition smoother for the students of a third-year mandatory algorithms, data structures, and computational complexity course. The concepts receiving extra attention in this work are NP-completeness, one of the most central concepts in computer science, and dynamic programming, an algorithm construction method that is powerful but somewhat unintuitive for some students. The major difficulties that we attribute to NP-completeness are that the tasks look similar but have a different purpose than in algorithm construction exercises. Students do not immediately see the usefulness of the concept, and hence motivation could be one issue. One line of attacking NP-completeness has been to emphasize its algorithmic aspects using typical tools for teaching algorithms. Some potential difficulties associated with dynamic programming are that the method is based on a known difficult concept—recursion—and that there are many ingredients in a dynamic programming solution to a problem. For both dynamic programming and NP-completeness, we have invented several new activities and structured the teaching differently, forcing students to think and adopt a standpoint, and practice the concepts in programming assignments. Student surveys show that these activities are appreciated by the students, and our evaluations indicate that they have positive effects on learning. We believe that these activities could be useful in any similar course. The approach to improving the course is action research, and the evaluation has been done using course surveys, self-efficacy surveys, rubrics-like grading protocols, and grades. We have also interviewed teaching assistants about their experiences.

Study programs are a central component of the higher education system, in particular for professional education. However, for many education programs, especially in engineering, a large part of the beginner students fail to reach graduation. A reason for this could be lack of program coherence. In order to strengthen the program coherence of the computer science and engineering program at KTH, a program integrating course was introduced in 2010. The course runs at low speed through the first three years of the program. It consists of reflection seminars in small cross-grade groups of students, led by a professor as a mentor. In the seminars the students exercise self-regulated learning abilities, such as metacognitive skills, when reflecting, discussing and acting upon different topics, such as study techniques, procrastination, studying abroad, plagiarism and responsibility, and the professional life after the studies.

A program integrating course runs over several years, shows the main thread of the program, and its purpose is to enable students to become more professional in handling their studies, at the same time as the course has a positive effect on the mentors, other courses and the program itself. This is achieved through regular reflection seminars where students meet in small cross-grade groups with a professor as a mentor.

NP-completeness is one of the most central concepts in computer science, and has been extensively applied in many diverse application areas. Despite this, students have problems grasping the concept and, more specifically, applying it to new problems. Independently, we have identified these problems at our universities in different countries and cultures. In an action research approach we have modified our courses and studied the effects. We here present some promising results. Our approach is mainly based on the idea of making more evident the fact that proving a new NP-completeness result is not at all different from designing a new algorithm. Based on this idea, we used tools typically used to teach algorithms (such as automatic program assessment and algorithm visualization systems), accompanied by other activities mainly devoted to augmenting the motivation to study computational complexity and forcing students to think and adopt a standpoint.

Automated assessment systems have been employed in computer science (CS) courses at a number of different universities. Such systems are especially applicable in teaching algorithmic problem solving since they can automatically test if an algorithm has been correctly implemented, i.e., that it performs its specified function on a set of inputs. Being able to implement algorithms that work correctly is a crucial skill for CS students in their professional role, but it can be difficult to convey the importance of this in a classroom situation. Programming and problem solving education supported by automated grading has been used since 2002 at our department. We study, using action research methodology, different strategies for deploying automated assessment systems in CS courses. Towards this end, we have developed an automated assessment system and both introduced it into existing courses and constructed new courses structured around it. Our primary data sources for evaluation consists of course evaluations, statistics on students' submitted solutions, and experience teaching the courses. Authors of this paper have been participating in teaching all of the courses mentioned here.

This paper describes an attempt to introduce computer lab exercises on NP-completeness proofs in a class already containing computer lab exercises on algorithms and data structures. In the article we are interested in the answer of the following question: Can the students feel that their understanding of theoretical computer science is improved by performing a computer lab exercise on the subject? The class is mandatory for students in a computer science program, and is taken by about 130 students each year. Theory of NP-completeness proofs with reductions has previous years been examined on an individual assignment with written solutions handed in and later explained orally by the student to a teacher. The new assignment is performed as a computer lab exercise where students are working in small groups of two. This exercise is placed before the individual assignment, and is examined first by running automated test cases and later by an oral presentation in lab to a teacher. An improvement can be observed of the students ’ average results since the new assignment was introduced. This is not enough to prove the benefit of using the new assignment. However, the responses to questionnaires at course evaluations show that almost all students think that the assignment really gave them better understanding of polynomial reductions in NP completeness proofs. The students’ result on the new assignment corresponds closely to their results on the following individual assignment. Seemingly, the new assignment predicts accurately who is going to pass the following assignment.

The study of the approximability properties of NP-hard optimization problems has recently made great advances mainly due to the results obtained in the field of proof checking. The last important breakthrough proves the APX-completeness of several important optimization problems and thus reconciles "two distinct views of approximation classes: syntactic and computational" [S. Khanna et al., in Proc. 35th IEEE Symp. on Foundations of Computer Science, IEEE Computer Society Press, Los Alamitos, CA, 1994, pp. 819--830]. In this paper we obtain new results on the structure of several computationally-defined approximation classes. In particular, after defining a new approximation preserving reducibility to be used for asmany approximation classes as possible, we give the first examples of natural NPO-complete problems and the first examples of natural APX-intermediate problems. Moreover, we state new connections between the approximability properties and the query complexity of NPO problems.

An efficient implementation of a part-of-speech tagger for Swedish is described. The stochastic tagger uses a well-established Markov model of the language. The tagger tags 92 per cent of unknown words correctly and up to 97 per cent of all words. Several implementation and optimization considerations are discussed. The main contribution of this paper is the thorough description of the tagging algorithm and the addition of a number of improvements. The paper contains enough detail for the reader to construct a tagger for his own language. Copyright © 1999 John Wiley & Sons, Ltd.

We investigate the computational complexity of two closely related classes of combinatorial optimization problems for linear systems which arise in various fields such as machine learning, operations research and pattern recognition. In the first class (Min ULR) one wishes, given a possibly infeasible system of linear relations, to find a solution that violates as few relations as possible while satisfying all the others. In the second class (Min RVLS) the linear system is supposed to be feasible and one looks for a solution with as few nonzero variables as possible. For both Min ULR and Min RVLS the four basic types of relational operators =, ⩾, > and ≠ are considered. While Min RVLS with equations was mentioned to be NP-hard in (Garey and Johnson, 1979), we established in (Amaldi; 1992; Amaldi and Kann, 1995) that min ULR with equalities and inequalities are NP-hard even when restricted to homogeneous systems with bipolar coefficients. The latter problems have been shown hard to approximate in (Arora et al., 1993). In this paper we determine strong bounds on the approximability of various variants of Min RVLS and min ULR, including constrained ones where the variables are restricted to take binary values or where some relations are mandatory while others are optional. The various NP-hard versions turn out to have different approximability properties depending on the type of relations and the additional constraints, but none of them can be approximated within any constant factor, unless P = NP. Particular attention is devoted to two interesting special cases that occur in discriminant analysis and machine learning. In particular, we disprove a conjecture of van Horn and Martinez (1992) regarding the existence of a polynomial-time algorithm to design linear classifiers (or perceptrons) that involve a close-to-minimum number of features.

The study of the approximability properties of NP-hard optimization problems has recently made great advances mainly due to the results obtained in the field of proof checking. The last important breakthrough proves the APX-completeness of several important optimization problems and thus reconciles "two distinct views of approximation classes: syntactic and computational" [S. Khanna et al., in Proc. 35th IEEE Symp. on Foundations of Computer Science, IEEE Computer Society Press, Los Alamitos, CA, 1994, pp. 819--830]. In this paper we obtain new results on the structure of several computationally-defined approximation classes. In particular, after defining a new approximation preserving reducibility to be used for asmany approximation classes as possible, we give the first examples of natural NPO-complete problems and the first examples of natural APX-intermediate problems. Moreover, we state new connections between the approximability properties and the query complexity of NPO problems.