Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


d'Aquin Mathieu , Adamou Alessandro , Dietze Stefan , Fetahu Besnik , Gadiraju Ujwal , Hasani-Mavriqi Ilire, Holz Peter, Kümmerle Joachim, Kowald Dominik, Lex Elisabeth, Lopez Sola Susana, Mataran Ricardo, Sabol Vedran, Troullinou Pinelopi, Veas Eduardo, Veas Eduardo Enrique

AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning

7th Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL 2017), Kravcik M., Mikroyannidis A., Pammer-Schindler V., Prilla M., CEUR-WS, Tallinn, Estonia, 2017

More and more learning activities take place online in a self-directed manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for awareness and self-reflection on one's own online learning behavior appear as an emerging need for both formal and informal learners. Addressing this need is one of the key objectives of the AFEL (Analytics for Everyday Learning) project. In this paper, we discuss the different aspects of what needs to be put in place in order to enable awareness and self-reflection in online learning. We start by describing a scenario that guides the work done. We then investigate the theoretical, technical and support aspects that are required to enable this scenario, as well as the current state of the research in each aspect within the AFEL project. We conclude with a discussion of the ongoing plans from the project to develop learner-facing tools that enable awareness and self-reflection for online, self-directed learners. We also elucidate the need to establish further research programs on facets of self-tracking for learning that are necessarily going to emerge in the near future, especially regarding privacy and ethics.

Tschinkel Gerwald, Sabol Vedran

Evaluating the Memorability and Readability of Micro-Filter Visualisations

International Conference on Information Visualization Theory and Applications, Porto, Portugal, 2017

When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase the memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper.

Hasitschka Peter, Sabol Vedran, Thalmann Stefan

Toward a Visual Analytics Framework for Learning Communities in Industry 4.0

9. Konferenz Professionelles Wissensmanagement (Professional Knowledge Management), York Sure-Vetter, Stefan Zander, Andreas Harth, Karlsruhe, Deutschland, 2017

Industry 4.0 describes the digitization and the interlinkingof companies working together in a supply chain [1]. Thereby,the digitization and the interlinking does not only affects themachines and IT infrastructure, rather also the employees areaffected [3]. The employees have to acquire more and morecomplex knowledge within a shorter period of time. To copewith this challenge, the learning needs to be integrated into thedaily work practices, while the learning communities shouldmap the organizational production networks [2]. Such learningnetworks support the knowledge exchange and joint problemsolving together with all involved parties [4]. However, insuch communities not all involved actors are known and hencesupport to find the right learning material and peers is needed.Nowadays, many different learning environments are usedin the industry. Their complexity makes it hard to understandwhether the system provides an optimal learning environment.The large number of learning resources, learners and theiractivities makes it hard to identify potential problems inside alearning environment. Since the human visual system providesenormous power for discovering patterns from data displayedusing a suitable visual representation [5], visualizing such alearning environment could provide deeper insights into itsstructure and activities of the learners.Our goal is to provide a visual framework supporting theanalysis of communities that arise in a learning environment.Such analysis may lead to discovery of information that helpsto improve the learning environment and the users’ learningsuccess.
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