LLM-based tools such as ChatGPT, GitHub Copilot, and the like have already arrived in software development practice, and continue to change the way of how software is created. Students use Generative AI tools for any purpose, whether we agree with the use or not. In this paper, we argue that in order to appropriately prepare our students for professional life, we educators need to incorporate the use of these tools explicitly into the teaching and learning process, so that students will learn a systematic and professional usage of these tools. Teaching basic software development concepts to students with only little or no prior knowledge has always been a challenge. Incorporating GenAI-based tools into software development education requires some careful rethinking of teaching concepts, especially with respect to constructive alignment, i. e. aligning learning objectives, teaching and learning methods, and exams to address GenAI-based support. Based upon first observations on the usage of GenAI tools in class, in this paper we suggest and compare concepts for teaching and learning basic software development that integrate GenAI tools at different levels of intensity and at different points during a semester. From these observations, we derive recommendations both for teaching and learning settings and for corresponding assessments, for teaching software development in a constructively aligned way in the age of GenAI tools.

As large language models (LLMs) become increasingly integrated into learning environments, their potential to enhance or hinder the acquisition of computational skills remains debated. This study investigates the role of generative AI (GenAI) tools, particularly Harvard's CS50 Duck, in supporting programming education. Through a mixed-methods approach, we examine students' perceptions, engagement, and practical application of the CS50 Duck within our Computational Thinking course. Our results indicate that while proficient students use GenAI tools to reinforce problem-solving skills, struggeling students may over-rely on them, potentially bypassing critical learning processes. Survey and assignment data suggest that students value the non-judgmental feedback provided by the CS50 Duck, yet express nuanced views on GenAI's role in formal assessments and programming education. We show that analysing chat histories can serve as a qualitative framework for examining the interactions between students and artificial tutors, while simultaneously offering critical insights into students' learning processes and the challenges they encounter. This study also underscores the need for instructional strategies that guide responsible GenAI use and highlights the importance of educator involvement in integrating these tools effectively.

To assess in a constructively aligned way those programming competences that are relevant for the professional practice of future software developers, an assessment format would be suitable where students actively program, within the integrated development environment (IDE) that they are individually used to, and with unrestricted access for researching in the web. A format that accommodates these needs well is “Bring Your Own Device, Open Book, Open Web”, where students work on their own devices against given git repositories, with full access on knowledge bases and the internet. In this work, we share experiences and suggest well established practices for executing this exam type. As well, we discuss how the ready availability of large language models impacts this assessment type.

In our teaching, we observe that students rarely explicitly ask questions, no matter what channels we offer, and even though there obviously are open issues that need to be clarified, both organizational and content-related. However, to support our students properly, we require their cooperation in order to identify what they really need. To achieve this, we analyzed the situation in more detail, identifying possibly relevant facets such as the ability to identify open issues at all, formulating a specific question, selecting an appropriate channel for posing a question, as well as emotional issues such as shyness, shame or fear of humiliation. In order to validate the relevance of these different facets, we conducted both qualitative and quantitative investigations with our cohorts over the last two years. The results indicate that students struggle with each of these facets to a certain degree, especially with the emotional aspects. To improve the situation, we suggest a set of good teaching practices and interventions that foster our students' abilities to identify open issues, formulate specific question, select an appropriate channel for posing their question, and to overcome possible shyness or even fear.

In the field of education ChatGPT has sparked both admiration and controversy. This study explores students' perspectives, specifically focusing on those in computer science-related fields. We investigated their motivations, trust, perceptions of utility, and reliability of ChatGPT by conducting two surveys-one at the beginning and another at the end of the semester. During the semester, students were encouraged to engage with ChatGPT. Our findings highlight the tool's multifaceted use in an academic settings, establishing it as a valuable resource for a variety of learning tasks. Most students have incorporated ChatGPT into their regular academic activities and view it as a beneficial aid. They perceive it as a multi-task solver and anticipate significant advancements in its writing assistance features in the near future. Many, not only attribute high accuracy to it but think that it adheres to appropriate content and structural norms. Our results suggest that active confrontation with ChatGPT enhances understanding of its capabilities, limitations and autoregressive nature. Consequently, we recommend an approach of informed engagement that includes the distinction between language processing and genuine language understanding and a carefully crafted terminology.

AI -systems that are based on large language models, such as ChatGPT, have quickly increased their prowess over the last year, and at the same time became readily available. As of now, many disciplines gain experience in using tools such as ChatGPT in a professional setting - and software engineering is no exception. Just as with any new kind of tooling, it is to be expected that in the era of ChatGPT, some traditional skills of the discipline will become rather obsolete, while at the same time new skill sets emerge that will be required from future professionals. Therefore, as educators we must reconsider the skill set we aim at fostering in our software engineering students, and adapt our intended learning outcomes accordingly. Furthermore, we need to adapt both assessment strategies and the teaching and learning methods we employ, in order to provide our students with a study experience that adheres to the principle of constructive alignment.

The great challenges of our time and age are vastly complex and thus cannot be solved by a single discipline alone. Instead, a combination of a variety of fields is necessary. As digitalization becomes increasingly pervasive in both our personal and professional lives, IT and potentially AI skills are needed for finding appropriate solutions. Consequently, job profiles emerge that require solid skills both in a traditionally non-IT discipline as well as in IT, AI, and digitalization in general. However, university education traditionally focuses on just one discipline in its study programs. Therefore, it is high time to systematically create study programs in which different disciplines join forces, to create a truly transdisciplinary education and to foster transdisciplinary profiles. Typically, developing and operating these transdisciplinary study programs requires close cooperation of different disciplines. Although this seems to be quite obvious at first sight and easy to put into practice, experience shows that in these efforts, there lurk a variety of challenges that, if ignored, endanger the success of any transdisciplinary attempts. In this work, we try to sensitize to typical challenges involved in creating transdisciplinary study programs. We provide advice and good practices that may help to overcome typical difficulties.

People are the backbone of any organization, and thus the important factor that determines whether an organization will be successful – or fail. This makes leadership a highly relevant skill for future professionals. Therefore, in our institution we aim at providing students not only with factual knowledge and technical competences, but with leadership skills as well, thus enabling them to take on responsibility and inspire and guide others. In this work, we present a course design for teaching leadership skills, which not only addresses the topic in a theoretical manner, but in addition creates a setting where students can actively apply selected aspects of leadership skills.

We continuously face high dropout rates in our faculty's Bachelor degree programs in Computer Science and in Information Systems. This is a quite common problem across engineering and computer science departments. Economically, for the university dropout means a waste of resources. On the other hand, to the young people it means a waste of lifetime, even though they might gain some experience. Furthermore, the labor market demands for many more well-educated engineers and IT specialists than are currently output. When we want to take special care of students that are in danger of dropping out, we must ensure that we identify those that really need this support. This group is sometimes called the “murky middle”. To identify them, we not only have to differentiate them from those students that will eventually earn their degree even without counseling, but also have to distinguish them from those students that do not seriously study at all - which is quite a significant number. Data analysis techniques on students' demographic and progress data have shown potential for an early prediction of dropout. We use logistic regression to identify factors for study success or failure, and we apply decision tree analysis to support the process of deciding which students we should invite to special counseling programs. Our models reach a similar high accuracy as previously reported studies with respect to predicting whether a student will drop out after the first semester. We also demonstrate that we are able to predict reasonably well whom we should - or should not - invite for counseling after the first semester.

At least in the western hemisphere, women in tech still are a minority instead of the matter of course that they should be. This holds true for the domain of Computer Science as well. Comparatively few women enter into CS-related courses of study, and even fewer of them finish these programs successfully. Thus, both the work force and society lose a large amount of the women's potential, which would be desperately needed to ensure that the technical innovation we need to cope with the pressing problems of our age are designed appropriately to address the needs of a wide range of people.To ensure that both women and men have comparable chances of successfully graduating in CS-related courses of study, we need to gain a closer insight into the hurdles that especially women are facing during their study process. For this means, we apply data analysis to the student data of our student administrative system, in order to identify major differences between the study progress of women and men, and to better understand where our female students struggle the most. Only if we understand this properly, we are able to improve our teaching purposefully in a gender sensitive way, and to introduce any structural changes that might help to increase and strengthen the women in our student body.

Over the last years, electronic exams have received increasing attention, as they offer far more potential than simply replacing paper-based questions by electronically supported multiple choice questions, or replacing blank paper by text fields in a web-browser or PDF form. Especially in the area of software development and software engineering, both students and teachers expect to benefit from an exam environment where students can utilize the same set of tools that they are used to from working on their homework exercises. Furthermore, teachers expect that electronic exams provide support with respect to automatic assessment. In this paper, we investigate applying electronic exams to introductory programming courses. In these exams, students have to solve programming problems using an integrated development environment, and hand in source code as their solution. This, however, requires a shift in formulating exam tasks, in order to align these tasks with learning objectives. It also requires careful investigation into the students' ability to cope with the environment and the rigorous supervision by a compiler and other checkers under time restrictions and, possibly, the nerves of an exam situation. To achieve this, we discuss how programming tasks can be designed in a manner that allows addressing all levels of Bloom's taxonomy of learning objectives, in order to implement constructive alignment. Furthermore we analyze our students' behavior during exams and their perception of this kind of exam. As well, we analyze the resulting solutions and identify typical error patterns that occur.

Dropout rates in STEM subjects are consistently high almost all over the world. To remedy the situation, many universities are taking measures that aim at reducing dropout rates. For these measures to be most effective, tools are required to predictively identify those students that are at risk of dropping out, to identify the individual causes of their struggles, to then select and apply appropriate interventions and finally to assess the effects that these interventions achieve.In this study, we focus on the first of these steps, i.e., identifying early indicators for potential risk of student dropout in our CS related degree programs. To achieve this, we analyse the data from our student administration system, focusing on our students’ performance in their first semester of studies. More precisely, we relate dropout to success rates and investigate into different behavioural patterns that lead to student dropout. Furthermore, we identify several factors that increase the risk of dropping out, and document some first early indicators for student dropout.

In many Computer-Science study programs, first-year students struggle with the transition from secondary school to academic education. To improve the situation at our institution, we designed an innovative, competency-oriented prep course for our first-year students. Instead of just repeating basic mathematics, this preparatory course took a more holistic approach by addressing both technological and non-technological skills that are considered to be essential for a successful start in CS-related study programs. These competences comprise self-regulation and learning techniques, problem solving, abstract and logical thinking as well as the reactivation of fundamental mathematics from school. To analyze the effectiveness of our approach, we evaluated the pilot run.

Self-reflection is an important competence for self-directing one’s learning process in an effective way. However, not all students are sufficiently well equipped with this ability. To help students acquire this ability, we conceptualize our notion of self-reflection by extending existing models. On this basis, we develop an assessment tool for self-reflection. Based on about 300 data sets, we evaluate the quality of our assessment tool by analyzing to what extent it reliably measures the different aspects of self-reflection. By performing a Principal Component Analysis, we identify four factors that we relate to the theoretical conceptualization of our notion of self-reflection.

It is commonly agreed that the competence of abstract thinking is an important skill for software developers. The analysis of students’ answers of our in-house developed Abstract Thinking Assessment (ATA) provides evidence that the competence of abstract thinking is an essential prerequisite for the acquisition of professional software development skills. Moreover, a deficit in abstract thinking competence of first-year students is indicated by the data. Thus, teaching professional software development skills should address the competence of abstract thinking explicitly.The question we try to answer is: How can we promote abstract thinking competence in a course on software development? This work proposes a concept to address the students’ competence of abstract thinking integrated into a class on software development. This paper presents the revision of the teaching concept of the module Software Development I with regard to the competence of abstract thinking. Concepts and approaches from literature are discussed. Moreover, we present the preparation of the teaching units, the material used and how they have been put into practice. This process considers findings from the competence model, the analysis of the ATA and the literature review. Furthermore, the teaching style has been changed from a traditional approach to pair-teaching. In the end, the teaching concept is evaluated.

Every student has to write a thesis statement at the end of their study programme. However, despite of this universality, both the process of supervision and the criteria used in the process of grading theses statements vary widely. In literature, there is a gap regarding the definition of demands and expectations on theses statements from a competence-oriented point of view. A thorough investigation of cognitive, non-technical competences that are required to successfully accomplish a thesis project and write the thesis itself has not yet been performed. In this paper, we propose a set of competences that are required to perform a thesis project and to create a thesis statement. Our goal is to apply the principles of constructive alignment to the supervision of thesis statements, as it is commonly done for lecture-based or lab-based teaching. Based on a review of literature and module specifications, we specify requirements for thesis statements and use them as a basis for deriving appropriate learning objectives for thesis projects. Then, we identify cognitive competences that are required to fulfill these learning objectives. On this basis, recommendations are developed for teaching practices in the process of supervision, as well as for grading of thesis statements.

Especially at the beginning of university studies, students form a group that is heterogeneous in many aspects. Compared with other disciplines, there is a significant dropout in computer science. We ascribe this to the fact that first-semester students often have significant deficits in cognitive competences - such as systematic and logical thinking - which are crucial for studying computer science successfully. To capture the students' learning capabilities, we developed a diagnostic test containing tasks that aim at evaluating the incoming students' initial level of highly relevant cognitive competences. In this paper, we evaluate the quality of this diagnostic test from a statistical point of view. Particular emphasis is placed on the analysis of the methodical competence. We assume that this component of the students' cognitive competences is of particular importance, as it is relevant not only for a successful start into the study phase, but is also essential for acquiring new skills and technical competences in computer science.

In virtually any learning process, questions occur on the students' side. However, in university education, more often than not students do not voice these questions aloud, especially not within a large auditorium. As a consequence, knowledge and skill gaps are not brought to the lecturer's attention as soon as the student becomes aware of them, but are kept more or less secret instead. Thus, the underlying knowledge gaps tend to exist for a rather long time, which inhibits the students' learning and development process. Remedying this situation requires to create - and maintain - a learning environment that encourages students to ask questions. We introduce a systematic approach for fostering questions in class that addresses both emotional and cognitive aspects. It combines some theoretical background with best practices from practical teaching.

Several weeks after new first-year students in Computer Science arrived, many lecturers complain that some of their students are not well-equipped with all competences necessary to study successfully. Hence, they struggle. This situation appears at different universities in various countries. Otherwise, there are students in every place, which meet the study requirements rather easily. This paper describes the development of a test, assessing first-year students' cognitive competences as well as basic knowledge in maths and computer usage. The test allows insights into the first-year students' initial level of competences. Thus, lecturers can base their lectures on the students current level. We conducted this test in the last four years: a pilot study as well as three regular runs. Until now we collected test results of over 750 students. First insights into the results facilitate the assumption that important competences are lacking and already influence our teaching in introductory courses. The next step is to clarify with help of the data, which competences influence the study success respectively the study failure.

How do lecturers know that their teaching is effective? If they want to formally assess this, they first have to define learning objectives that specify the intended outcome of their teaching, in terms of competencies to be developed in their students. On this basis, they can then design appropriate assessment tools, such as formal exams, that attempt to objectively measure to what extent the aspired competencies have been developed. As an example, we focus an introductory, two-term class on software development. We provide a subset of a generic definition of learning objectives that covers all six levels of Bloom's taxonomy. Based on that, we design an exam concept that matches these objectives, specifying the share that each competence level holds in the overall exam. Finally, we analyze exam results focusing on competence levels that our students reached.

The student entry phase is the period of a study process in which fundamental technical and non-technical skills should be acquired. The interrelation of those skills is not only crucial for the study process itself. Rather, the systematic, well-reflected methodological usage of specific technical knowledge is a key element for the future professional career after graduation. Unfortunately, during our last years of teaching we noticed that most of the novices in software engineering are neither well equipped with the necessary soft skills, nor can they handle the key technical skills in an appropriate way at the end of their first semester. Therefore, we designed a project that addresses technical and non-technical competencies in an integrated way. In the project, we induce students to actively increase their awareness of their own proficiency in technical and non-technical skills by guided reflection and feedback processes, both individually as well as in a group.

Many programming errors that we observe in novice programmers coincide with those that are described in literature. Due to their vast number, it is practically impossible to list all errors explicitly and describe interventions for each specific error. Therefore, to reduce the number of single instances that have to be considered, we developed an error classification scheme that enables us to classify errors into seven classes. These classes are based on the revised Bloom-taxonomy and focus on the underlying problem that causes the observed errors. Based on these classes, we investigate and develop interventions that focus on the underlying problems, thus addressing the problem causes rather than their symptoms. In this paper, we describe both the interventions themselves, and the research process we applied for identifying these interventions.

In this paper we present accumulated results from two years of experience with a teaching unit on debugging Java programs. With this special teaching unit, we strive to foster the debugging skills of our students. Students were asked to find different defects in given code, to analyze these and finally to fix them. As well, students were requested to document their approach in writing. The achieved results ranged from “all bugs found and fixed” to “completely lost in the code”. When analyzing these results, we discovered that the debugging skills of our students seem to correlate with some non-technical skills that are essential base competencies in software engineering, such as the ability to work in a systematic way. This implies that for improving our students' debugging skills, it is helpful to address not only the technical aspects of debugging, but to foster the required base competencies as well.

Just-in-Time Teaching (JiTT) is an activating teaching method that is highly popular in today's university education systems. In this paper, we use the experience we made using JiTT during two semesters to analyze the effectiveness of this teaching method.

For us a lecturers, it is important to assess early on in a class whether we effectively reach our students and create the desired teaching outcome. Usually, the major assessment is some kind of exam which takes place at the end of term - and thus at a time when it is generally too late to change anything. Instead, lecturers need a toolset of different feedback and evaluation techniques that provide feedback as required, at different intervals throughout term, adequate for different teaching settings and group sizes, and with a reasonable amount of time and effort required from all parties involved. To help choose a suitable feedback technique for a specific teaching context, we introduce a classification scheme for feedback techniques. On this basis, we rate a variety of well-established feedback techniques, thus making it possible to select those feedback techniques that best suit one's individual needs.

Many freshmen students of computer science are unaware of the requirements and job profiles of their future professional domain. To provide them with a deeper insight into the requirements of their future jobs at an early point of their studies, we designed a software engineering simulation project that focuses on the main tasks a software engineer has to deal with in professional life. More precisely, this project focuses on the major software-technical, methodological and practical competencies every computer scientist-to-be must acquire. Thus, this introductory software engineering project serves as a kind of “kick off meeting” for freshmen students in computer science. It creates an initial insight into and understanding of the overall interrelation of the different tasks and skills that will be a crucial part both of their study process as well as of their future professional career.

Since unit testing is a skill required of professional software developers, lecturers have to develop this skill in their software engineering students. Therefore, we introduce the approach of “objects first, tests second”, which incorporates unit testing into introductory programming classes. We discuss requirements that teaching materials must meet to effectively support this approach, and present a concept for assessing the quality of student written tests. An analysis of students' results illustrates the effectiveness of this teaching approach.

First-year students are often not well equipped with the base competencies that are a necessary precondition for effectively acquiring complex new knowledge. Among others, these base competencies comprise self-organization, analytical thinking, or communication skills. Shortcomings in these competencies often lead to problems in the study process. To solve these problems, we need to investigate the students' initial competencies, in order to become aware of possible deficits, find ways to deal with them, and thus to enable students to reach their goals. Therefore, we developed a self-assessment on base competencies, an accompanying knowledge test and a questionnaire on personal information. The data collected by these tools was analyzed, searching for competence characteristics which influence whether our students persevere and participate in the final exam, or drop out early. The results of our data analysis support the assumption that both non-technical and technical competencies are crucial to study successfully. Correlation analysis has identified several characteristics which distinguish the students that participate in the exam from those who do not. Based on these results, we suggest ways to optimize university courses, teaching methods, and the support of first-year students to meet their needs.

Over the recent years, we experienced that a significant percentage of first-year students shows difficulties in acquiring even introductory software development knowledge, as well as in coping with the study process itself. In most cases, the core problem is not a lack of general intellectual capacity, but rather significant deficiencies in certain base competencies (i.e. self-, practical and cognitive as well as social competencies). We imply that these base competencies are crucial for successfully studying computer science or related topics. In order to identify these base competencies, we collected a superset of competencies from literature, structured this set, and performed filtering and clustering steps, resulting in almost 100 remaining base competencies that are relevant in our teaching context. As it is impossible for any lecturer to develop all these competencies in a single effort, we finally boiled this set down to a selection of those competencies that we deem to be most relevant for successfully studying software related topics at university level. Towards this end, for each competence, we defined what we the lecturers expect from our incoming freshmen students. In addition, we specified the skill level expected by the job market. Furthermore, we assessed the skill level of those 70% of our students that have obvious difficulties in coping with study requirements. Finally, we selected those competencies with a large difference between what is expected from our graduates and what we find in our incoming cohort. This analysis resulted in a selection of 27 competencies that we deem to be highly essential prerequisites for software engineering education, and which are not sufficiently well developed in the vast majority of freshmen students. For each of these base competencies we provide definitions and denote reasons for their selection.

Not all incoming students are sufficiently well endowed with those base competencies (such as self organization, analytical thinking or communication skills) that are prerequisite for acquiring complex new knowledge as well as coping with the study process itself. As lecturers, we have to be aware of our incoming students' base competency profile, in order to pick them up where they are and help them develop whatever they need to study successfully. To investigate the students' initial skills regarding their base competencies, we developed a self-assessment focusing on selected self competencies, practical and cognitive skills as well as social competencies that are crucial to study computer science or related topics. In this paper, we present our assessment approach and its design. An initial evaluation in which 320 students were involved indicates that deficits in base competencies can be made tangible for students. Based on the deficits we identified, we are going to optimize our courses to meet freshmen students' needs in a better way.

In our everyday teaching we experience that many first-year students are ill prepared for the demands of an academic study process in technical domains. Usually, the students' problems are not rooted in a lack of intellectual capacity or previously acquired founding expertise in the chosen course of study. Rather, it is the deficiencies in more basic competencies such as practical and cognitive, self and social areas that place a major hurdle in the students' learning process. In an attempt to elucidate and improve this situation, as a first step we develop a questionnaire that captures the lecturers' expectations on student competencies in these areas, as opposed to their view on competencies that first-year students possess in reality. This questionnaire is also applied to capture the university-employer skill gap.