LLMs find increasing use as an educational tool, both from the instructors' perspective as well as from the students'. This paper presents a preliminary study investigating the effects of prior knowledge concerning a given topic on the effectiveness of using an LLM (Microsoft Copilot) to study the topic. Choosing Büchi automata as an example, twelve computer science students were tasked with first giving a self-report on prior knowledge, then studying Büchi automata for 15 minutes using only an LLM as a study tool, and afterwards filling out a short topical questionnaire. Two trends could be observed: Prior knowledge of LLMs seems to increase the learning effect while prior knowledge of a topic that is related to the studied topic seems to diminish the learning effect.

Learning outcomes play an important role in education. For laboratory education in STEM, a multitude of different collections for programme level laboratory learning outcomes exist. This work has two major contributions: 1. a comparison of the different programme level learning outcome collections applying qualitative content analysis; and 2. a synthesis of a catalogue of 16 programme level learning outcomes for educational STEM laboratories based on the comparison. By including some more general frameworks - namely the Stifterverband Future Skills framework and the World Economic Forum Education 4.0 framework - we aim for a wider point of view that respects not only Academia but also economic perspectives while at the same time consolidating different perspectives and nomenclatures. We hope that the catalogue can both be useful for practitioners as well as for research in the area of laboratory pedagogy.

This work presents a large scale literature review on the question of what a laboratory in computer science is. This question arises since computer science has different traditions and is thus harder to grasp compared to more traditional fields of study. A total of 83 papers from the IEEE and ACM digital libraries were inductively categorised. All reviewed papers were published between the years 2017 and 2021. The results show that most laboratories are described in the context of teaching (course development and broader education / laboratory pedagogy research). One big problem in current laboratories seems to be that most are described without any didactical concepts, and the didactical concepts described by the included papers cover a wide range of principles. The disciplines of the reviewed laboratories are highly diverse and span across a wide spectrum with most papers either focussing on programming / software development or do not have a specific laboratory description.

There are increasing demands for IT security education which could be partly met by easier access to IT security laboratories. This paper proposes the use of ‘Infrastructure as Code’ (IaC) as a central building block for introducing dynami-cally adaptable teaching scenarios to laboratories in the context of IT security. The decision was made based on our didactical concept (which is built on Bloom's Taxonomy). The concept we propose is intended for use in a virtual laboratory, where the whole laboratory set-up is distributed over and contained within virtual machines. This way, we are able to build realistic, complex teaching scenarios. After comparing multiple IaC solutions, we decided to build our implementation on Terraform. The most important building blocks written in Terraform are presented. In addition, a user interface was created to meet demands of students and teachers. We will describe example teaching scenarios including one where students are tasked with gaining access to vulnerable data via a 2-step attack.

We present a dataset containing source code solutions to algorithmic programming exercises solved by hundreds of Bachelor-level students at the University of Hamburg. These solutions were collected during the winter semesters 2019/2020, 2020/2021 and 2021/2022. The dataset contains a set of solutions to a total of 21 tasks written in Java as well as Python and a total of over 1500 individual solutions. All solutions were submitted through Moodle and the Coderunner plugin and passed a number of test cases (including randomized tests), such that they can be considered as working correctly. All students whose solutions are included in the dataset gave their consent into publishing their solutions. The solutions are pseudonymized with a random solution ID. Included in this paper is a short analysis of the dataset containing statistical data and highlighting a few anomalies (e.g. the number of solutions per task decreases for the last few tasks due to grading rules). We plan to extend the dataset with tasks and solutions from upcoming courses.

Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such personalised interaction capabilities of a human companion robot on its social acceptance, perceived intelligence and likeability in a human-robot interaction scenario. In order to measure this impact, the study makes use of an object learning scenario where the user teaches different objects to the robot using natural language. An interaction module is built on top of the learning scenario which engages the user in a personalised conversation before teaching the robot to recognise different objects. The two systems, i.e. with and without the interaction module, are compared with respect to how different users rate the robot on its intelligence and sociability. Although the system equipped with personalised interaction capabilities is rated lower on social acceptance, it is perceived as more intelligent and likeable by the users.

This paper describes the techniques used in the submitted video presenting an interaction scenario, realised using the Neuro-Inspired Companion (NICO) robot. NICO engages the users in a personalised conversation where the robot always tracks the users' face, remembers them and interacts with them using natural language. NICO can also learn to perform tasks such as remembering and recalling objects and thus can assist users in their daily chores. The interaction system helps the users to interact as naturally as possible with the robot, enriching their experience with the robot, making it more interesting and engaging.

The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of "unmapped" geographic data is space usage rules (SURs), which are defined as geographically-bound activity restrictions (e.g. "no dogs", "no smoking", "no fishing", "no skateboarding"). Researchers in the area of human-computer interaction have recently begun to develop techniques for the automated mapping of SURs with the aim of supporting activity planning systems (e.g. one-touch "Can I Smoke Here?" apps, SUR-aware vacation planning tools). In this paper, we present a novel SUR mapping technique -- SPtP -- that outperforms state-of-the-art approaches by 30% for one of the most important components of the SUR mapping pipeline: associating a point observation of a SUR (e.g. a 'no smoking' sign) with the corresponding polygon in which the SUR applies (e.g. the nearby park or the entire campus on which the sign is located). This paper also contributes a series of new SUR benchmark datasets to help further research in this area.