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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
Future social robots will act autonomously in the world. Autonomous behavior is usually realized by using AI models built with real-world data, which often reflect existing inequalities and prejudices in society. Even if designers do not intend it, there are risks that robots will be developed that discriminate against certain users, e. g. based on gender. In this work, we investigate the implications of a gender-biased robot that disadvantages women, which unfortunately is a bias in AI that is often reported. Our experiment shows that both men and women perceive the gender-biased robot to be unfair. However, our work indicates that women are more aware that a gender bias causes this unfairness. We also show that gender bias results in the robot being perceived differently. While the gender bias resulted in lower likability and intelligence ratings by women, men seem to lose trust in the robot if it behaves unfairly.
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
Video tutorials are an effective method of knowledge transfer and learning. However, they are often time-consuming to create and difficult to access during work. This paper introduces an approach that simplifies the creation of video tutorials in the workplace and promotes their use during work. By utilizing smart glasses, practitioners can record video tutorials during their daily work processes and utilize them for knowledge transfer to other employees without much additional effort. This offers the advantage of directly and easily sharing expertise in the workplace without being constrained by time or location. Given the intended simplicity of this approach, the question arises whether it creates video tutorials that help people in their learning. The paper presents a study that compares the effectiveness of knowledge transfer using these video tutorials against traditional personal training methods in the workplace. With 18 participants from the nursing and production sectors, we observed the training and learning outcomes of using video tutorials on smart glasses over multiple sessions, comparing them with personal training, which is considered the standard for practical onboarding. The study results indicate that learning with video tutorials does not significantly differ in terms of learning outcomes from traditional personal training methods. Overall, this study highlights the potential of video tutorials with smart glasses for knowledge transfer in workplaces, while also identifying challenges and opportunities for optimizing onboarding processes for employees.
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.
In der öffentlichen Debatte wird die Generation Z häufig als zentrale Herausforderung für die zukünftige Arbeitswelt dargestellt. Es wird behauptet, dass diese Generation, geprägt durch digitale und gesellschaftliche Umbrüche, andere Erwartungen an Arbeitgeber hat als ihre Vorgänger. Unsere Studie untersucht, ob diese Annahme wissenschaftlich haltbar ist und ob sich die Relevanz von Arbeitgeberattraktivitätsfaktoren zwischen den Generationen Baby Boomer, Generation X, Y und Z signifikant unterscheidet.
Basierend auf einem neuen empirischen Ansatz analysieren wir mit einer umfangreichen deutschen Stichprobe (N=1133) mittels multipler Regressionen die Vorhersagekraft von 19 Arbeitgeberattraktivitätsfaktoren für die allgemeine Arbeitgeberattraktivität. Dabei prüfen wir, ob und inwieweit die Generationszugehörigkeit moderierende Effekte auf die Bedeutung dieser Faktoren hat.
Die Ergebnisse zeigen, dass wesentliche Attraktivitätsfaktoren wie Identifikation mit dem Unternehmen, Management und Führung sowie die Arbeitsaufgabe generationsübergreifend relevant sind. Unterschiede zeigen sich lediglich in einzelnen Dimensionen wie Arbeitsplatzsicherheit, Corporate Social Responsibility, Work-Life-Balance und Unternehmensimage, wobei die Varianzaufklärung durch Generationsunterschiede äußerst gering ist (<2 %) und die signifikanten Regressionskoeffizienten im Durchschnitt nur bei .23 liegen.
Die Studie liefert eine empirische Basis für die Diskussion über Generationsunterschiede bei arbeitsrelevanten Einstellungen. Unsere Ergebnisse stellen die gängige Praxis von generationsspezifischem Employer Branding und eine Überschätzung von Generationsunterschieden, die auf stereotypen Annahmen beruhen, infrage. Selbst Alters- und Lebensphaseneffekte lassen sich in unseren Ergebnissen kaum nachweisen. Zukünftige Forschung sollte verstärkt longitudinale Designs verwenden, um die Dynamik von Alter, Lebensphasen und Generationen besser zu verstehen.
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
Indigenous people across the globe have all too often been marginalised and not been considered in decisions that directly concern their life. We maintain the significance of incorporating indigenous perspectives in society to create a global dialogue through direct participation. In this light we propose a situated action in which our co-author from the Ju/’hoansi tribe, one of the San ethnicity in Southern Africa, digitally records semi-structured conversations with members of other indigenous communities at the conference and public spaces in Sibu. Then participants are engaged into a participatory exploration of processing the audio files into sound installations and soundscapes which “amplify indigenous voices“. We anticipate that the products can be reused for further initiatives in the different countries, raising awareness and calling for action on matters of concern for indigenous people.
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
In this paper, we shed light on shared hosting services’ security and trust implications and measure their attack surfaces. To do so, we analyzed 30 shared hosters and found that all of them might leak relevant information, which could be abused unnoticed. An adversary could use this attack surface to covertly extract data from various third parties registered with a shared hoster. Furthermore, we found that most hosters suffer from vulnerabilities that can be used by an internal attacker (i.e., someone using the service) to compromise other hosted services or the entire system.
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
This paper challenges the conventional assumption in cybersecurity that users act as rational actors. Despite numerous technical solutions, awareness campaigns, and organizational strategies aimed at bolstering cybersecurity, these often overlook the prevalence of non-rational user behavior. Our study, involving a survey of 208 participants, empirically demonstrates this aspect. We found that a significant portion of users (55.3%) would accept a substantial risk (35%) to click on a potentially malicious link or attachment. This propensity increases to 61% when users are led to believe there is a 65% chance of facing no adverse consequences. To address this irrationality, we explored the efficacy of nudging mechanisms within email systems. Our qualitative user study revealed that incorporating a simple colored nudge in the email intably enhance the ability of users to discern malicious emails, improving decision-making accuracy by an average of 10%.
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.
Experiencing relational devaluation at work through social stressors has been linked to various detrimental outcomes. In the current study, we investigate the role of hardiness and mindfulness as personal resources which help employees to effectively cope with such stressors and thereby prevent burnout.
We focus on trait mindfulness as the innate capacity of paying and maintaining attention to present-moment experiences with an open and nonjudgmental attitude. It has been shown to promote concentration and well-being and to facilitate decision making; it is often seen as an important resource for overcoming challenges in everyday work life.
Hardiness also constitutes a personality profile of dispositional resilience that describes how people deal with stressful events and includes the core aspects of challenge (conviction that challenges offer opportunities), engagement (actively tackling tasks and challenges) and a sense of control (conviction of influence over one's own life circumstances). People with high hardiness show better health and higher job satisfaction and performance.
We build our hypothesis according to the extended version of the Job Demands–Resources model, which states that personal resources protect employees from burnout, because they shape employees’ perceptions of and reactions towards their work environment. In a similar vein, stress theory suggests that personal resources mitigate burnout through lower stress appraisals, greater use of adaptive coping, and flexibility in matching coping to appraisals.
We measured social stressors at work with the scale developed by Frese et al and further asked participants to work on the Maslach Burnout Inventory, the Mindful Attention and Awareness Scale and the short version of the Revised Norwegian Dispositional Resilience (Hardiness) Scale. Our cross-sectional study was based on a sample of N = 174 employees from a broad range of organizations and job types.
Statistical Analyses revealed significant negative correlations of both personal resources with reported symptoms of burnout and the perception of social stressors as well. However, in line with prior research, they indeed did not attenuate the relationship between social stressors and emotional exhaustion at work. Theoretical and practical implications as well as limitations and avenues for future research are discussed.
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.
When organizing intercultural and interdisciplinary Project-Based Learning (PBL) activities across Higher Education Institutions (HEIs), the organizational and resource implications, along with the associated financial challenges, soon become crucial. Even promising approaches may not take off as a simple ‘return on investment’ view and funding decision may not fully address their various impacts on students, staff, institutions and society.
This paper explores the experiences within a distributed interdisciplinary project-based learning program run from 2020 to 2023 and involving more than 150 students from four continents learning 21st century skills by collaborating over one semester to address real-world problems faced by clients in partner countries. While the primary goal of this distributed interdisciplinary and intercultural project-based learning program was to offer students a truly Global Intercultural Project Experience (GIPE), this paper explores its broader impact. We found that the program significantly influenced both the academic and administrative staff at all partner universities. Furthermore, we examine the program's effect on the participating institutions themselves over the four-year period. Our conclusion is that the invaluable benefits of such interdisciplinary project-based learning extend well beyond financial metrics. They include enhanced student learning experiences, strengthened cooperation and mutual learning between academics and administrative staff, improved institutional reputation, and positive societal impact.
Thus, we worked hard to convince both our university management and the world's largest funding organisation for the international exchange of students and researchers to grant financial support for another 3-year period in 2025 to 2027 during which the GIPE concept will be further developed and a permanent organizational structure shall be established based on an extended network of partner institutions and sponsors around the world.
Unleashing Personalized Education Using Large Language Models in Online Collaborative Settings
(2024)
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.
This paper discusses the experiences of a distributed interdisciplinary project-based learning program for students across continents. For the years 2020 until 2023, we received seed-funding for four annual projects to engage students from Germany (Europe), Namibia (Africa), Indonesia (Asia), and Peru (Latin-America) to collaborate over one semester on interdisciplinary projects contributing to the solution of some real-life client’s problems in the partner countries. During this period, more than 150 students embarked on these projects with 116 of them being selected for a scholarship for an international mobility. With the guidance and support by academics from all partner universities, the students success-fully completed each project expressing deep appreciation for the learning opportunities while over¬coming challenges of working across widespread time zones, different cultures, changing requirements, and various technical difficulties.
While the primary aim of this distributed interdisciplinary and intercultural project-based learning program was to provide students with a truly Global Intercultural Project Experience (GIPE), in this paper we investigate on its impact in a broader sense as it was observed that this program also had a significant impact on both academic and administrative staff at all partner universities. Finally, we also reveal the impact of this four-year-program on the participating institutions themselves and conclude that the invaluable returns of such interdisciplinary project-based learning extend far beyond financial metrics. It encompasses enhanced student learning experiences, strengthened cooperation and mutual learning between academics and administrative staff, as well as institutional reputation, and societal impact.