J
Jürgen Buder
Researcher at Leibniz Institute for Neurobiology
Publications - 38
Citations - 1234
Jürgen Buder is an academic researcher from Leibniz Institute for Neurobiology. The author has contributed to research in topics: Collaborative learning & Preference. The author has an hindex of 15, co-authored 36 publications receiving 1066 citations. Previous affiliations of Jürgen Buder include University of Tübingen & Media Research Center.
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Book ChapterDOI
A Framework for Teachable Collaborative Problem Solving Skills
TL;DR: There is a growing awareness that collaborative skills require dedicated teaching efforts and collaborative problem solving has been identified as a particularly promising task that draws upon various social and cognitive skills, and that can be analysed in classroom environments where skills are both measurable and teachable.
Journal ArticleDOI
Learning with personalized recommender systems: A psychological view
Jürgen Buder,Christina Schwind +1 more
TL;DR: The potentials of recommender systems for learning from a psychological point of view are explored and system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases are distinguished.
Journal ArticleDOI
Knowledge awareness in CSCL: A psychological perspective
TL;DR: This paper classifies knowledge awareness in relation to already well-established concepts like shared mental models, common ground, and transactive memory system in order to provide a comprehensive definition of this approach.
Journal ArticleDOI
Guiding knowledge communication in CSCL via group knowledge awareness
TL;DR: Characteristics of GKA tools are discussed and their impact on collaboration is discussed, including a mechanism for perceived learning gains and perceived knowledge convergence.
Journal ArticleDOI
Supporting controversial CSCL discussions with augmented group awareness tools
Jürgen Buder,Daniel Bodemer +1 more
TL;DR: Development of augmented group awareness tools that take mutual user ratings of their online discussion contributions as input, aggregate these data, and visually feed these data back to the members in real time are described, thereby informing participants about how the group as a whole perceives their contributions.