Abstract Remote participation in school is possible today with the help of telepresence robots. Such technologies can offer great opportunities for children with long-term illnesses to continue attending school. Consequently, telepresence robots are already used in some schools when children are absent for long periods. However, despite their positive impact, such robots also create challenges for the privacy of people involved in such a setting. Therefore, in this paper, we discuss the user needs of such robots in this specific and privacy-sensitive application field. We held three workshops with different user groups with and without experience with the robots. Among them were formerly and currently ill children, parents, teachers, head teachers, media educators, and supporting personnel. We discussed their experiences (if any), ideas, expectations, and concerns with a focus on privacy aspects to find out about the user needs of different user groups. Our results reveal various interrelationships and conflicts between the individual actors. They serve as a basis to discuss the implications for the design of future telepresence robots for schoolchildren.

Abstract Telepresence robots offer great opportunities for children with long-term illnesses to continue attending school. Consequently, they are already used if children are absent for long periods. When designing such systems, the privacy of various stakeholders must be considered. However, conflicts often arise because the privacy requirements of different user groups cannot be fulfilled simultaneously. In this paper, we analyze the corresponding trade-offs that have to be made when designing telepresence robots under conflicting privacy requirements. We analyzed previous literature and held three workshops with different user groups (children, parents, teachers, head teachers, media educators, and supporting personnel) with and without experience with telepresence robots in schools. Based on the literature and the workshop results, we present four major privacy trade-offs we identified and discuss design approaches for them. With this work, we contribute to the design research on telepresence robots in schools by revealing the major privacy-related conflicts and potential design approaches to overcome the conflicts.

Abstract Robots are gradually being freed from their safety fences due to the advances in safety features integrated with most new robots. These robots enable a new form of human-robot interaction in which contact is possible. There are two robot-related factors that play a decisive role in the interaction dynamics and human perception in such a case - the motion speed and distance the robot keeps away from the human. Literature indicates that these factors influence whether humans perceive trust, safety, and comfort, which are essential components in the acceptance of robots by their end users. However, although speed and distance were intensively investigated in isolation, little is known about their combined effect. To this end, we conducted an experiment investigating the impact of the industrial robot arm’s approaching speed and stopping distance on the users’ trust, safety, and comfort while they were performing a collaborative task. Our results provide interesting insights into the implications of speed and trust. While we were able to replicate former studies in terms of implications of speed and distance, our data does not show any interaction effect between the two variables. However, our participants’ observations indicated that distance impacts the dependent variables more severely than speed.

Abstract Escape rooms have gained popularity across the globe, be it for entertainment, team building, or learning purposes. Solving puzzles has intrigued humanity since ancient times and playing games together facilitated intercultural encounters, encouraging mutual learning. This situated action builds on the idea of a mobile escape room within a suitcase, which when traveling shares previous encounters and collects new puzzles, thereby ever evolving. A suitcase will arrive in Sibu, Malaysia, with augmented reality stickers that entail puzzles created in Namibia. Visitors need to solve the puzzles to open the suitcase, which contains treats from Namibia, as well as materials to create new puzzles. Visitors participate in creating their own cultural puzzles, upload them to the augmented reality application, and thereby contribute to a new multicultural escape suitcase. The puzzles can also be played back at home, and the suitcase will continue evolving as it travels.

Abstract In this paper we present the co-design and implementation of an extended reality escape room with 26 primary school students. The aim of our study was to explore the co-design process with students and to co-create a playable escape room, providing an asymmetric immersive experience in which players collaborate. We realised the complexity of designing such an escape room with primary students. We share our experiences and learnings in regard to required capacities and skills of co-designers, and adjustment of complexity and timing to players. We also maintain that the integration of extended reality technologies into escape rooms requires further research to realise asymmetric co-located collaboration.

Abstract This paper presents an integrated approach combining in-person teaching with digital assessments in a mechanical engineering course, leveraging a blended learning environment to enhance both physical and remote instructions. The primary focus is on addressing challenges associated with digital exams, such as hardware and software limitations, connectivity issues, and the risk of cheating. To mitigate these barriers for digital exams, the paper proposes creating unique exam tasks based on student IDs and employing semi-automatic grading of computer drawings using Python scripts, which streamline the assessment process while ensuring accuracy and fairness. Traditional assessment methods typically involve written exams, computer-based tests, practical labs, oral exams, and project-based assessments. These methods require students to attend exams in person, with activities strictly monitored to prevent cheating. However, this approach highlights the need to transition from traditional methods to digital assessments, focusing on knowledge application rather than simply recalling the information. In order to show the possibility of such a transition, a Computer-Aided Design (CAD) course is employed as a case study, where theoretical knowledge is assessed through digital quizzes and practical skills via design challenges and final exams. By creating unique tasks based on student IDs, the course ensures exam integrity and fairness and still allows students to work on the assigned problem on their own computer device and on their own time schedule. Additionally, a semi-automatic system compares the volumetric properties of student-generated 3D models with reference solutions using Python scripts. This approach significantly reduces manual grading workload while maintaining high assessment standards. The course structure aligns learning activities with desired outcomes through the Constructive Alignment of Biggs et. al. Weekly quizzes handled via Moodle automatically grade the theoretical knowledge of the students, while biweekly tutorials and practical sessions support the transition from theory to practical application. Design challenges, graded and contributing to the final exam score, motivate students and provide continuous feedback and assessment. This dynamic learning environment not only engages students but also enhances the retention of theoretical knowledge and its practical application through digital tools. In conclusion, this paper showcases the successful integration of digital assessment methodologies in mechanical engineering education. By addressing and overcoming challenges early, and aligning learning activities with outcomes, the blended learning approach enhances the educational experience. The strategic use of unique exam tasks and semi-automatic grading systems not only ensures fair and accurate assessments but also prepares students for the demands of the digital age in their professional careers.

Abstract For years, researchers have been analyzing mobile Android apps to investigate diverse properties such as software engineering practices, business models, security, privacy, or usability, as well as differences between marketplaces. While similar studies on iOS have been limited, recent work has started to analyze and compare Android apps with those for iOS. To obtain the most representative analysis results across platforms, the ideal approach is to compare their characteristics and behavior for the same set of apps, e. g., to study a set of apps for iOS and their respective counterparts for Android. Previous work has only attempted to identify and evaluate such cross-platform apps to a limited degree, mostly comparing sets of apps independently drawn from app stores, manually matching small sets of apps, or relying on brittle matches based on app and developer names. This results in (1) comparing apps whose behavior and properties significantly differ, (2) limited scalability, and (3) the risk of matching only a small fraction of apps. In this work, we propose a novel approach to create an extensive dataset of cross-platform apps for the iOS and Android ecosystems. We describe an analysis pipeline for discovering, retrieving, and matching apps from the Apple App Store and Google Play Store that we used to create a set of 3,322 cross-platform apps out of 10,000 popular apps for iOS and Android, respectively. We evaluate existing and new approaches for cross-platform app matching against a set of reference pairs that we obtained from Google's data migration service. We identify a combination of seven features from app store metadata and the apps themselves to match iOS and Android apps with high confidence (95.82 %). Compared to previous attempts that identified 14 % of apps as cross-platform, we are able to match 34 % of apps in our dataset. To foster future research in the cross-platform analysis of mobile apps, we make our pipeline available to the community.

Abstract Filter lists are used by various users, tools, and researchers to identify tracking technologies on the Web. These lists are created and maintained by dedicated communities. Aside from popular blocking lists (e.g., EasyList), the communities create region-specific blocklists that account for trackers and ads that are only common in these regions. The lists aim to keep the size of a general blocklist minimal while protecting users against region-specific trackers. In this paper, we perform a large-scale Web measurement study to understand how different region-specific filter lists (e.g., a blocklist specifically designed for French users) protect users when visiting websites. We define three privacy scenarios to understand when and how users benefit from these regional lists and what effect they have in practice. The results show that although the lists differ significantly, the number of rules they contain is unrelated to the number of blocked requests. We find that the lists’ overall efficacy varies notably. Filter lists also do not meet the expectation that they increase user protection in the regions for which they were designed. Finally, we show that the majority of the rules on the lists were not used in our experiment and that only a fraction of the rules would provide comparable protection for users.

The precision of yield calculation of modern design and simulation software for photovoltaic systems strongly rely, beside the accuracy of the specified module and inverter data, on the quality of the weather data. Since data from weather stations is not available for most locations world-wide this data is calculated by using modern interpolation methods. Beside this, simulation software typically uses historical weather data. In this work the mismatch of yield simulation results based on proprietary data, meaning interpolated or also called synthetical data, and data coming from a weather station in proximity to the installation is evaluated. The simulated data sets are compared to measurement data as obtained by the inverter output and hence give a profound understanding how interpolated data may influence the simulation results. The outcome shows that the quality of the yield simulation, if compared to the measurement data, is increased by a factor of up to four if on-site weather data is used as input for the simulation. The largest source of deviation is irradiation, which varies up to 10% if synthetical and measured irradiation on-site is compared. The second largest sources for simulation mismatches are power calculation and module temperature correction.

The Artificial Intelligence community has long pursued personalized education. Over the past decades, efforts have ranged from automated advisors to Intelligent Tutoring Systems, all aimed at tailoring learning experiences to students' individual needs and interests. Unfortunately, many of these endeavors remained largely theoretical or proposed solutions challenging to implement in real-world scenarios. However, we are now in the era of Large Language Models (LLMs) like ChatGPT, Mistral, or Claude, which exhibit promising capabilities with significant potential to impact personalized education. For instance, ChatGPT 4 can assist students in using the Socratic method in their learning process. Despite the immense possibilities these technologies offer, limited significant results are showcasing the impact of LLMs in educational settings. Therefore, this paper aims to present tools and strategies based on LLMs to address personalized education within online collaborative learning settings. To do so, we propose RAGs (Retrieval-Augmented Generation) agents that could be added to online collaborative learning platforms: a) the Oracle agent, capable of answering questions related to topics and materials uploaded to the platform.; b) the Summary agent, which can summarize and present content based on students' profiles.; c) the Socratic agent, guiding students in learning topics through close interaction.; d) the Forum agent, analyzing students' forum posts to identify challenging topics and suggest ways to overcome difficulties or foster peer collaboration.; e) the Assessment agent, presenting personalized challenges based on students' needs. f) the Proactive agent, analyzing student activity and suggesting learning paths as needed. Importantly, each RAG agent can leverage historical student data to personalize the learning experience effectively. To assess the effectiveness of this personalized approach, we plan to evaluate the use of RAGs in online collaborative learning platforms compared to previous online learning courses conducted in previous years.