Peter A. Henning

Advancement of MOOCs with Learning Pathways

In this paper, educational and technical challenges in the field of Massive Open Online Courses (MOOCs), such as cultural adaptation, consideration of learning habits or the efficient construction of educational content, are outlined. We argue that learning pathways in combination with learner-centered metadata are optimal methods to meet those challenges and hence optimize learning efficiency and learning experience substantially by creating personalized learning recommendations and feedback. This is illustrated from a conceptual and technological perspective, as is currently in development in the EU-project INTUITEL. We believe that this approach not only opens up new possibilities for MOOCs, but also provides a variety of new dimensions for eLearning in general.

A Semantic MediaWiki-Based Approach for the Collaborative Development of Pedagogically Meaningful Learning Content Annotations

In this work, we present an approach that allows educational resources to be collaboratively authored and annotated with well-defined pedagogical semantics using Semantic MediaWiki as collaborative knowledge engineering tool. The approach allows for the exposition of pedagogically annotated learning content as Linked Open Data to enable its reuse across e-learning platforms and its adaptability in different educational contexts. We employ Web Didactics as knowledge organization concept and detail its manifestation in a Semantic MediaWiki system using import and mapping declarations. We also show how the inherent pedagogical semantics of Web Didactics can be retained when learning material is exported as RDF data. The advantage of the presented approach lies in addressing the constructivist view on educational models: The different roles involved in the content development process are not forced to adapt to new vocabularies but can continue using the terms and classification systems they are familiar with. Results of the usability test with computer scientists and education researchers are positive with significantly more positive results for computer scientists.

Learning 4.0

Didactical methods and models of learning are determined by the questions of where learning content is stored and how it is accessed. The digital transformation of information storage and access therefore necessitates new models of learning and dramatic changes in educational systems. In this article, these new learning paradigms are outlined, classified and weighted for their disruptive impact on societal and industrial processes—ranging from the everywhere, every-time of digital mobile devices to human strategies for coping with information overflow.

Visualizing Learning Behavior as Spatio-Temporal Trajectories

To tackle the proposed problem, several mature research streams can be considered. For this PhD project, especially CBR, schema matching for integration scenarios and component adaptation are currently regarded as relevant. The digitalization of teaching and learning has become an increasing desire for schools and universities. In order to apply digital media purposefully, educational organizations need to understand if and how students make use of digital contents and platforms. In the following we present a technique that uses arbitrary logging data as they may be present in any ICT systems that are commonly used to distribute digital learning contents. It transforms arbitrary data into spatio-temporal trajectories that can be analyzed only on the basis of their geometric relationships and characteristics. Through this we lift heterogeneous data to a highly abstract level. In an example, we illustrate how we can distinguish different types of users regarding temporal patterns and the learners’ mobility. We are also able to recognize groups of students working on similar topics. We mostly understand the current state of our system as a tool that can give both researchers and teachers the possibility to examine student’s behavior on a qualitative basis. In an outlook we furthermore describe how the system can be extended to support automatic clustering of learning behaviors.