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  4. Teaching advanced web technologies with a mobile learning companion application
 
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2017
Conference Paper
Title

Teaching advanced web technologies with a mobile learning companion application

Abstract
The Learning Companion Application was actually designed to fit the needs of master craftsmen in a blended learning Energy Consultant Training at the chamber of crafts. It supports mobile learning particularly through its responsive design and recommendation engine. However, its design follows the recommended practice taught in a university course for master students about Advanced Web Technologies. That is why we introduced the same application for this computer science course to provide students with a contextual and situated learning experience: students learn with the help of a system that implements many concepts they have to learn. Topics, such as HTML5, the development of responsive web applications and recommender systems, are introduced in the lecture and can be experienced as real world examples by the students in the learning app as well. Similar to common learning management systems, our Learning Companion Application offers the lecture materials as digital media assets, such as texts, source code, animations or videos. In addition, the application tracks the interactions of the students in order to give overviews of the learners' knowledge levels on the different learning objects at every time, in order to identify learning weaknesses to improve teaching with the help of a learning analytics module. It can recommend appropriate learning objects which fit the predicted knowledge and the current situation of the learner, e.g. available time for learning. This paper presents taught concepts in the lecture and their implementation in the Learning Companion Application as well as a study of the interaction and learning behavior of the computer science students.
Author(s)
Krauss, Christopher  orcid-logo
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Merceron, Agathe
Beuth Hochschule für Technik, Berlin
An, Truong-Sinh
Beuth Hochschule für Technik, Berlin
Zwicklbauer, Miggi
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Steglich, Stephan  
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Arbanowski, Stefan  
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Mainwork
mLearn 2017, 16th World Conference on Mobile and Contextual Learning. Proceedings  
Conference
World Conference on Mobile and Contextual Learning (mLearn) 2017  
Open Access
DOI
10.24406/publica-r-398492
10.1145/3136907.3136937
File(s)
N-473532.pdf (399.73 KB)
Language
English
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Keyword(s)
  • adaptive learning

  • Recommender System

  • Web technologies

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