Modeling is an important skill in software engineering. However, it is often not tangible for students and not appreciated. Students prefer coding because they receive immediate feedback from the compiler. Engaging students in modeling is difficult, especially in large introductory courses. We have developed an interactive learning method for modeling which is based on an easy to use online editor. Students learn modeling in guided tutorials in the lecture right after the theory is introduced and deepen their modeling skills in group work and homework exercises. This learning method was applied in a large introductory course with more than 1000 students. An empirical evaluation of the method demonstrated that the students' learning outcome in modeling improved significantly by up to 87 %. Students are motivated to use models in their future projects and understand how to approach problems with models. The use of interactive models in programming exercises improves their understanding of the taught concepts. CCS CONCEPTS • Social and professional topics → Software engineering education; • Applied computing → Interactive learning environments; Learning management systems.
hall with a capacity of about 100 seats. Students could ask for support by simply raising their hand, and tutors were standing by to help. Every participating student brought their own private computer to the lecture hall or was provided with a computer with a preinstalled Xcode integrated development environment (IDE) and tools to take part in the programming exercises and lectures. In general, the programming course is scheduled to run for ten days over a two-week period before the beginning of each semester. During the course, students learn the fundamentals of the Swift programming language and how to develop iOS and iPadOS applications. As Swift, iOS, and iPadOS development concepts change regularly, the curriculum of the course is adapted every semester. Due to the university lockdown starting four weeks prior to the scheduled start of the programming course, we were confronted with additional challenges as we were unable to conduct the course in a personalized, on-site setting. In this paper, we describe the experiences, challenges, methods, and outcomes of transitioning the Swift programming course to a large-scale distributed format. II. TEACHING CONCEPTS In the following paragraphs, we provide a summary of the teaching concepts relating to the Swift programming course and the subsequent multi-project capstone course. A. Capstone Course: iPraktikum The iPraktikum is a practical course which allows students to experience software engineering first-hand in a projectbased structure [1]. It focuses on mobile applications in larger system architectures that can include, for example, IoT devices or server components. With up to 100 students each semester, the course is split into a maximum of 12 teams to develop applications for customers from industry. Each team consists of one project leader (a doctoral candidate or post-doc from the host chair), one coach (a student who has already participated in the capstone course before), and the student developers. The iPraktikum is offered to undergraduate and graduate students from different fields of study such as biomedical computing, computer science, data engineering and analytics, electrical engineering, information systems, management and technology, mathematics, mechanical engineering, physics,
Background: Physical activity helps improve the overall quality of life. The correct execution of physical activity is crucial both in sports as well as disease prevention and rehabilitation. Little to no automated commodity solutions for automated analysis and feedback exist. Objectives: Validation of the Apple ARKit framework as a solution for automatic body tracking in daily physical exercises using the smartphones’ built-in camera. Methods: We deliver insights into ARKit’s body tracking accuracy through a lab experiment against the VICON system as Gold Standard. We provide further insights through case studies using apps built on ARKit. Results: ARKit exposes significant limitations in tracking the full range of motion in joints but accurately tracks the movement itself. Case studies show that applying it to measure the quantity of execution of exercises is possible. Conclusion: ARKit is a light-weight commodity solution for quantitative assessment of physical activity. Its limitations and possibilities in qualitative assessment need to be investigated further.
Background: Mobile apps may encourage a lifestyle that avoids unhealthy behaviors, such as smoking or poor nutrition, which promotes cardiovascular diseases (CVD). Yet, little data is available on the utilization, perception, and long-term effects of such apps to prevent CVD. Objectives: To develop a mobile app concept to reduce the individual CVD risk and collect information addressing research questions on CVD prevention while preserving data privacy and security. Methods: To validate the concept, a prototype will be built, and usability studies will be performed. Results: We expect to determine whether it is possible to reach a broad user base and to collect scientific information while protecting user data sufficiently. Conclusion: To address CVD prevention, we propose a mobile coaching app. We expect high acceptance rates in validation studies.
Background: Postural imbalance can be adopted for the early detection of age-related diseases or monitoring the course of the disease treatment; especially in monitoring, frequent balance measurement is crucial. This is mainly done through regular in-person examinations by a physician currently. Feedback in between examinations is often missing. Objectives: This paper proposes mBalance, a mobile application that uses the Romberg test to detect postural imbalance. mBalance provides a camera-based, low-cost approach to measure imbalance frequently at home using mobile devices. Methods: Imbalance detection accuracy and usability was evaluated in two separate studies with 31 and 30 participants, respectively. Results: mBalance correctly detected imbalance with a sensitivity of 80% and a specificity of 87%. The study found good usability with no significant problems. Conclusion: Overall, this study solves the problem of postural imbalance detection by digitizing a validated balance test into an easy-to-use mobile application.
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