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Characterization and analysis with xAPI based graphs for adaptive interactive learning environments

 
: Streicher, Alexander; Pickl, Stefan Wolfgang

:
Postprint urn:nbn:de:0011-n-6182462 (819 KByte PDF)
MD5 Fingerprint: 14b26f4944fa546c3c1d434a3e8fcb6a
Erstellt am: 10.12.2020


Bracho, R. ; International Institute of Informatics and Systemics -IIIS-:
24rd World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2020. Vol.2 : September 13-16, 2020, Virtual Conference
Red Hook/NY: Curran Associates, 2020
ISBN: 978-1-71381-906-6
S.115-120
World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) <24, 2020, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
graph algorithms; graph mining; E-Learning; learning analytics; adaptivity

Abstract
In e-learning, insights from the analysis of usage tracking data can help improve teaching and learning, e.g., with learning analytics to identify strengths and weaknesses of learners or course material, or for targeted help for individual students. One analysis approach is to examine the graph networks of interaction usages. Adaptive e-learning systems (ALS), which personalize the learning experience to the learners' needs, can make use of relationship information in graph networks to determine the best adaptation strategy. For example, ALS can use graph algorithms to detect central activities that have high influence to the users or to learning objects. This paper shows how to make use of the Experience API (xAPI) protocol and graph networks for its application in adaptive interactive learning environments such as computer simulations and serious games. A prototype implementation hints at the feasibility of the concept and its practical implications.

: http://publica.fraunhofer.de/dokumente/N-618246.html