Fellendorf Martin, Brandstätter Michael, Reiter Thomas, Lindstaedt Stefanie , Breitwieser Christian, Haberl Michael, Hebenstreit Cornelia, Scherer Reinhold, Kraschl-Hirschman Karin, Kröll Mark, Ruthner Thomas, Walther Bernhard
2012
Das mobile Verkehrsmanagementsystem MOVEMENTS soll als einfaches und zuverlässiges System entwickelt werden, das durch mobile Anzeigemöglichkeiten mit dezentralen Ansteuerungsmöglichkeiten und zentraler Überwachungsfunktion flächendeckend einsetzbar ist. Bei den Anzeigetafeln ist auf Lesbarkeit und Verständlichkeit von Texten und Piktogrammen zu achten, um für die Verkehrsteilnehmer auch unter schlechten Sichtbedingungen wahrnehmbar zu sein. Die mobile Anzeige soll sowohl für planbare Ereignisse (Veranstaltungen, Baustellen, ...), als auch für ungeplante Ereignisse längerer Dauer (Unfälle mit verkehrsbeeinträchtigender Wirkung, Straßensperren durch Naturereignisse, wie Hangrutschungen, ...) eingesetzt werden. Generell sollen durch den Einsatz von MOVEMENTS die Lenkungs- und Informationsmöglichkeiten der ASFINAG in Netzteilen ohne Verkehrsbeeinflussungsanlagen verbessert werden
Seitlinger Christian, Schöfegger Karin, Lindstaedt Stefanie , Ley Tobias
2012
Ravenscroft Andrew, Lindstaedt Stefanie , Delgado Kloos Carlos, Hernández-Leo Davinia
2012
This book constitutes the refereed proceedings of the 7th European Conference on Technology Enhanced Learning, EC-TEL 2012, held in Saarbrücken, Germany, in September 2012. The 26 revised full papers presented were carefully reviewed and selected from 130 submissions. The book also includes 12 short papers, 16 demonstration papers, 11 poster papers, and 1 invited paper. Specifically, the programme and organizing structure was formed through the themes: mobile learning and context; serious and educational games; collaborative learning; organisational and workplace learning; learning analytics and retrieval; personalised and adaptive learning; learning environments; academic learning and context; and, learning facilitation by semantic means.
Drachsler Hendrik, Verbert Katrien, Manouselis Nikos, Vuorikari Riina, Wolpers Martin, Lindstaedt Stefanie
2012
Technology Enhanced Learning is undergoing a significant shift in paradigm towards more data driven systems that will make educational systems more transparent and predictable. Data science and data-driven tools will change the evaluation of educational practice and didactical interventions for individual learners and educational institutions. We summarise these developments and new challenges in the preface of this Special Issue under the keyword dataTEL that stands for ‘Data-Supported Technology-Enhanced Learning’.
Kump Barbara, Seifer Christin, Beham Günter, Lindstaedt Stefanie , Ley Tobias
2012
User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.
Pammer-Schindler Viktoria, Kump Barbara, Lindstaedt Stefanie
2012
Collaborative tagging platforms allow users to describe resources with freely chosen keywords, so called tags. The meaning of a tag as well as the precise relation between a tag and the tagged resource are left open for interpretation to the user. Although human users mostly have a fair chance at interpreting this relation, machines do not. In this paper we study the characteristics of the problem to identify descriptive tags, i.e. tags that relate to visible objects in a picture. We investigate the feasibility of using a tag-based algorithm, i.e. an algorithm that ignores actual picture content, to tackle the problem. Given the theoretical feasibility of a well-performing tag-based algorithm, which we show via an optimal algorithm, we describe the implementation and evaluation of a WordNet-based algorithm as proof-of-concept. These two investigations lead to the conclusion that even relatively simple and fast tag-based algorithms can yet predict human ratings of which objects a picture shows. Finally, we discuss the inherent difficulty both humans and machines have when deciding whether a tag is descriptive or not. Based on a qualitative analysis, we distinguish between definitional disagreement, difference in knowledge, disambiguation and difference in perception as reasons for disagreement between raters.