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Teaching advanced web technologies with a mobile learning companion application

 
: Krauss, Christopher; Merceron, Agathe; An, Truong-Sinh; Zwicklbauer, Miggi; Steglich, Stephan; Arbanowski, Stefan

:
Fulltext urn:nbn:de:0011-n-4735326 (399 KByte PDF)
MD5 Fingerprint: 083c1a3b2a80cacbb5895c414f90a896
Created on: 16.11.2017


Loizides, Fernando (Ed.) ; Association for Computing Machinery -ACM-:
mLearn 2017, 16th World Conference on Mobile and Contextual Learning. Proceedings : Larnaca, Cyprus, October 30 - November 01, 2017
New York: ACM, 2017
ISBN: 978-1-4503-5255-0
Art. 29, 4 pp.
World Conference on Mobile and Contextual Learning (mLearn) <16, 2017, Larnaca>
English
Conference Paper, Electronic Publication
Fraunhofer FOKUS ()
adaptive learning; Recommender System; Web technologies

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.

: http://publica.fraunhofer.de/documents/N-473532.html