Auflistung nach Autor:in "Fuchs, Kevin"
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- ZeitschriftenartikelA Consolidated Framework for Implementing Robotic Process Automation Projects (Extended Abstract)(EMISA Forum: Vol. 41, No. 1, 2021) Herm, Lukas-Valentin; Janiesch, Christian; Helm, Alexander; Imgrund, Florian; Fuchs, Kevin; Hofmann, Adrian; Winkelmann, Axel
- KonferenzbeitragPersonalized web learning by joining OER(DeLFI 2014 - Die 12. e-Learning Fachtagung Informatik, 2014) Henning, Peter A.; Fuchs, Kevin; Bock, Jürgen; Zander, Stefan; Streicher, Alexander; Zielinski, Andrea; Swertz, Christian; Forstner, Alexandra; Badii, Atta; Thiemert, Daniel; Perales, Oscar GarciaWe argue that quality issues and didactical concerns of MOOCs may be overcome by relying on small Open Educational Resources, joining them into concise courses by gluing them together along predefined learning pathways with proper semantic annotations. This new approach to adaptive learning does not attempt to model the learner, but rather concentrates on the learning process and established models thereof. Such a new approach does not only require conceptual work and corresponding support tools, but also a new meta data format and an engine which may interpret the semantic annotations as well as measure a learner's response to these. The EU FP7 project INTUITEL7 is introduced, which employs these technologies in a novel learning environment.
- TextdokumentVisualizing Learning Behavior as Spatio-Temporal Trajectories(INFORMATIK 2017, 2017) Fuchs, Kevin; Henning, Peter A.To tackle the proposed problem, several mature research streams can be considered. For this PhD project, especially CBR, schema matching for integration scenarios and component adaptation are currently regarded as relevant. The digitalization of teaching and learning has become an increasing desire for schools and universities. In order to apply digital media purposefully, educational organizations need to understand if and how students make use of digital contents and platforms. In the following we present a technique that uses arbitrary logging data as they may be present in any ICT systems that are commonly used to distribute digital learning contents. It transforms arbitrary data into spatio-temporal trajectories that can be analyzed only on the basis of their geometric relationships and characteristics. Through this we lift heterogeneous data to a highly abstract level. In an example, we illustrate how we can distinguish different types of users regarding temporal patterns and the learners’ mobility. We are also able to recognize groups of students working on similar topics. We mostly understand the current state of our system as a tool that can give both researchers and teachers the possibility to examine student’s behavior on a qualitative basis. In an outlook we furthermore describe how the system can be extended to support automatic clustering of learning behaviors.