Alexander Streicher

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.

Interoperable Adaptivity and Learning Analytics for Serious Games in Image Interpretation

Personalization and adaptivity in computer simulations and serious games are being used to achieve positive long term effects on the users’ engagement, motivation and ultimately on the learning outcome. Interoperability regarding the collection of usage data allows for an effective analysis of the interaction and learning progress data. This paper presents an interoperable adaptivity framework combined with a web-based tutoring interface which gives learning analytics insights. The developed framework “E-Learning A.I.” (ELAI) acts as an intelligent tutoring agent for simulations and serious games and uses the Experience API (xAPI) protocol. The application of the ELAI has been demonstrated in an adaptive map-based learning game for aerial image interpretation. The scientific research questions affect the possible usages of the collected interaction data, how to manifest adaptivity in games, how to realize interoperable adaptivity mechanisms for simulations and serious games, and how to make use of collected usage data.

Using Text Segmentation Algorithms for the Automatic Generation of E-Learning Courses

With the advent of e-learning, there is a strong demand for tools that help to create e-learning courses in an automatic or semi-automatic way. While resources for new courses are often freely available, they are generally not properly structured into easy to handle units. In this paper, we investigate how state of the art text segmentation algorithms can be applied to automatically transform unstructured text into coherent pieces appropriate for e-learning courses. The feasibility to course generation is validated on a test corpus specifically tailored to this scenario. We also introduce a more generic training and testing method for text segmentation algorithms based on a Latent Dirichlet Allocation (LDA) topic model. In addition we introduce a scalable random text segmentation algorithm, in order to establish lower and upper bounds to be able to evaluate segmentation results on a common basis.

Game-Based Training for Complex Multi-institutional Exercises of Joint Forces

This paper presents a new concept for modular, Web-based training tools for complex, multi-institutional joint forces exercise scenarios, based on the motivational principles of digital game based learning (serious games). In multi-national, large-scale exercises for NATO Joint Intelligence, Surveillance, and Reconnaissance (JISR) various participants in different roles and backgrounds must understand the processes and information flow between the participating heterogeneous hardware systems and software appliances. The high variability of multi-dimensional requirements result in the need for pre-exercise preparation and training tools. Further, participants must be motivated to engage in the preceding training activities. This paper presents the modular concept for the game-based exercise training tool as well as its application for a real exercise scenario.