D
Dan Kohen-Vacs
Researcher at Holon Institute of Technology
Publications - 32
Citations - 241
Dan Kohen-Vacs is an academic researcher from Holon Institute of Technology. The author has contributed to research in topics: Computer science & Educational technology. The author has an hindex of 8, co-authored 28 publications receiving 223 citations. Previous affiliations of Dan Kohen-Vacs include Linnaeus University.
Papers
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Proceedings Article
Adopt & adapt: structuring, sharing and reusing asynchronous collaborative pedagogy
TL;DR: This paper presents a new approach for creating and conducting structured asynchronous collaborative activities and incorporating them in the existing instructional setting for all subjects and levels.
Mobile Treasure Hunt Games for Outdoor Learning
TL;DR: Treasure-HIT allows teachers to define a set of locations (usin locations) that can be used for designing location based games conducted via mobile phones.
Journal ArticleDOI
Integration of an intelligent tutoring system in a course of computer network design
Elena Verdú,Luisa M. Regueras,Eran Gal,Juan Pablo de Castro,María Jesús Verdú,Dan Kohen-Vacs +5 more
TL;DR: The current paper describes the initial user test, which was conducted at the University of Valladolid for the course “Network Design” and shows that students with INTUITEL follow learning paths that are more suitable for them.
An Integrated Approach for the Enactment of Collaborative Pedagogical ScriptsUsing Mobile Technologies
TL;DR: This paper describes the current efforts that combine the potential and affordances of two approaches and systems aiming to provide educators and researchers with the ability to design and enact pedagogical policies.
Journal ArticleDOI
Evaluation of enhanced educational experiences using interactive videos and web technologies: pedagogical and architectural considerations
TL;DR: This paper presents EDU.Tube: an interactive environment that relies on web and mobile solutions offered to teachers and students for authoring and incorporating educational interactions at specific moments along the time line of occasional YouTube video-clips, and describes its aspects in relation to learning analytics.