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  4. Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning
 
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2021
Konferenzbeitrag
Titel

Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning

Abstract
Artificial intelligence offers great opportunities for the future, including for teaching and learning. Applications such as personalized recommendations and learning paths based on learning analytics [i.e. 1], the integration of serious games in intelligent tutoring systems [2], intelligent agents in the form of chatbots [3], and other emerging applications promise great benefits for individualized digital learning. However, what value do these applications really add and how can these benefits be measured? With this article, we would like to give a brief overview of AI-supported functionalities for learning management system as well as their possible benefits for future learning environments. Furthermore, we outline methods for a comprehensive evaluation that meets the users' needs and concretizes the actual benefit of an AI-supported LMS.
Author(s)
Rerhaye, Lisa
Fraunhofer-Institut für Kommunikation, Informationsverarbeitung und Ergonomie FKIE
Altun, Daniela
Fraunhofer-Institut für Kommunikation, Informationsverarbeitung und Ergonomie FKIE
Krauss, Christopher
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Müller, Christoph
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Hauptwerk
Adaptive Instructional Systems. Design and Evaluation. Third International Conference, AIS 2021. Proceedings. Pt.I
Konferenz
International Conference on Adaptive Instructional Systems (AIS) 2021
International Conference on Human-Computer Interaction (HCI International) 2021
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DOI
10.1007/978-3-030-77857-6_28
Language
Englisch
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FKIE
FOKUS
Tags
  • artificial intelligen...

  • Learning Management S...

  • evaluation

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