To benefit from most of the current digital educational technologies, learners are required to sit down and look closely at a computer monitor or smart device screen for hours, which can have side effects on learners’ health and lifestyle. As an attempt to address this, we developed MathKinetics, an application designed to support the practice of cognitive skills such as arithmetic while engaging in physical activity by integrating the principles of Multimodal Learning, Life Kinetik, and Gamification. MathKinetics is a variant of an endless running game where users control an avatar through their body posture and dodge obstacles. At the same time, they pick up arithmetic problems whose answers need to be verbalized. In this paper, we present an exploratory evaluation of MathKinetics and its user experience. We conducted user tests with 20 participants. Results from our tests indicate that MathKinetics is a fun way to practice arithmetic skills and train executive cognitive functions such as task switching.

Innovation and technology in computer science education is driven by research and practice. Both of these activities involve the gathering and analysis of data in order to develop new tools, methods including software, or strategies to solve recent challenges in the field. However, data as basis for any new solution is hardly shared, reused and recognized. This is due to the fact that the publication of research data encompasses a number of challenges for researchers, while benefits of publishing data remain low. As a result, further analyses of data as part of secondary research are uncommon in the computer science education community. Therefore, the authors of this position paper critically reflect on current practices related to the publication of research data in this community. Moreover, a path forward is outlined for future conferences, such as ITiCSE, to become increasingly FAIR, and open with regard to research data.

In recent years, Learning Analytics (LA) has become a very heterogeneous research field due to the diversity in the data generated by the Learning Management Systems (LMS) as well as the researchers in a variety of disciplines, who analyze this data from a range of perspectives. In this paper, we present the evaluation of a LA tool that helps course designers, teachers, students and educational researchers to make informed decisions about the selection of learning activities and LA indicators for their course design or LA dashboard. The aim of this paper is to present Open Learning Analytics Indicator Repository (OpenLAIR) and provide a first evaluation with key stakeholders (N=41). Moreover, it presents the results of the prevalence of indicators that have been used over the past ten years in LA. Our results show that OpenLAIR can support course designers in designing LA-based learning activities and courses. Furthermore, we found a significant difference between the relevance and usage of LA indicators between educators and learners. The top rated LA indicators by researchers and educators were not perceived as equally important from students' perspectives.

Point-based geometry representations have become widely used in numerous contexts, ranging from particle-based simulations, over stereo image matching, to depth sensing via light detection and ranging. Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data. Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood. Regarding the latter, our approach employs multi-scale neighborhoods, for which weighted covariance measures of local points are determined. Curved line structures are reconstructed via vector field tracing, using a bidirectional piecewise streamline integration. We also introduce an automatic selection of optimal starting points via multi-scale geometric measures. The pipeline development and choice of parameters was driven by an extensive, automated initial analysis process on over a million prototype test cases. The behavior of our approach is controlled by several parameters — the majority being set automatically, leaving only three to be controlled by a user. In an extensive, automated final evaluation, we cover over one hundred thousand parameter sets, including 3D test geometries with varying curvature, sharp corners, intersections, data holes, and systematically applied varying types of noise. Further, we analyzed different choices for the point of reference in the co-variance computation; using a weighted mean performed best in most cases. In addition, we compared our method to current, publicly available line reconstruction frameworks. Up to thirty times faster execution times were achieved in some cases, at comparable error measures. Finally, we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets, extracting power line cables.

Using perception in the context of rendering is a wide spread field. It can be used to speed up calculations or create more detailed images by refining important areas. Saliency, as a perception based method, can identify regions of interest, which should contain more detail. But currently no complete mapping of the 2D operators to an 3D equivalent has been defined. We propose a Bidirectional Saliency Weight Distribution Function (in short BSWDF). Using this function to evaluate a surface element the local saliency value can be obtained. The global value can be retrieved by using a splatting algorithm, which combines the individual features based on the distance of the object. As most objects do not change during rendering, e.g. by deformation, a preprocess can create a lookup cache for these values.