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.

Self-testing has been proven to significantly improve not only simple learning outcomes, but also higher-order skills such as clinical reasoning in medical students. Previous studies have shown that self-testing was especially beneficial when it was presented with feedback, which leaves the question whether an immediate and personalized feedback further encourages this effect. Therefore, we hypothesised that individual feedback has a greater effect on learning outcomes, compared to generic feedback. In a randomised cross-over trial, German medical students were invited to voluntarily answer daily key-feature questions via an App. For half of the items they received a generalised feedback by an expert, while the feedback on the other half was generated immediately through ChatGPT. After the intervention, the students participated in a mandatory exit exam. Those participants who used the app more frequently experienced a better learning outcome compared to those who did not use it frequently, even though this finding was only examined in a correlative nature. The individual ChatGPT generated feedback did not show a greater effect on exit exam scores compared to the expert comment (51.8 ± 22.0% vs. 55.8 ± 22.8%; p = 0.06). This study proves the concept of providing personalised feedback on medical questions. Despite the promising results, improved prompting and further development of the application seems necessary to strengthen the possible impact of the personalised feedback. Our study closes a research gap and holds great potential for further use not only in medicine but also in other academic fields.

(1) Background: With human pose estimation on the rise in the field of biomechanics, the need for scientific investigation of those algorithms is becoming evident. The validity of several of those algorithms has been presented in the literature. However, there is only limited research investigating the applicability of human pose estimation outside the lab. The aim of this research was to quantify the effect of deviating from the standard camera setup used in biomechanics research. (2) Methods: Video data from four camera viewing angles were recorded and keypoints estimated using OpenPose. Kinematic data were compared against a gold-standard marker-based motion capture system to quantify the effect of the camera viewing angle on the validity of joint angle estimation of the knee, hip, elbow and shoulder joints. (3) Results: The results of this study showed reasonable correlations between the joint angles of OpenPose and the gold standard, except for the shoulder. However, the analysis also revealed significant biases when comparing the joint angles inferred from the different viewing angles. In general, back-viewing cameras performed best and resulted in the lowest percental deviations. (4) Conclusions: The findings of this study underscore the importance of conducting a detailed examination of individual movements before proposing specific camera angles for users in diverse settings.

Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.

Objective: Digital approaches have the potential to make activity promotion attractive and age-appropriate for children and adolescents. KIJANI is a mobile application aiming to increase physical activity (PA) in youth via gamification and augmented reality. This study investigates the user experience with KIJANI through a multimethod approach.Approaches: KIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of collected step counts. With these coins, blocks can be bought, which can be used to create virtual buildings and landscapes and integrate these into the player's real-world environment via augmented reality. To evaluate the user experience, participants played KIJANI in groups of three for 25 min. Afterwards KIJANI was evaluated qualitatively with one-on-one semi-structured interviews as well as quantitatively with standardized questionnaires.Results: Overall, 22 participants (12.6 ± 1.7 years, 6 girls) were included in the study. The overall game concept and realization were well received by the target group. Study participants did have various and creative ideas for the further development of KIJANI. The majority ( n = 16) thought that using KIJANI would increase their PA level. User experience based on the UEQ scale was (mean ± SD): attractiveness (1.78 ± 1.82), perspicuity (2.15 ± 0.680), efficiency (0.67 ± 1.25), dependability, (1.21 ± 0.93), stimulation (1.24 ± 1.78), and novelty (1.27 ± 1.34).Conclusion: With these insights, a further step has been taken in the participatory development of KIJANI. Apps like KIJANI appear to be suitable for PA promotion in children and adolescents.

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. Validation of the Apple ARKit framework as a solution for automatic body tracking in daily physical exercises using the smartphones' built-in camera. 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. 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. 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.