Systeme im Einsatz. WBT, LMS, E-Portfolio-Systeme, PLE und andere

Dieser Beitrag stellt mehrere Formen von Informationssystemen vor, die derzeit im Bereich des Lernens und Lehrens eingesetzt bzw. diskutiert werden. Neben webbasierten Trainingssystemen (WBT) sind Lernmanagementsysteme (LMS) weit verbreitet. Letztere werden insbesondere zur Verwaltung von Lerninhalten und Kursabwicklung in (Hoch-) Schulen genutzt. Für die E-Portfolio-Methode werden seit Anfang des Jahrtausends passende Systeme entwickelt. Und in den letzten Jahren hat ein neues Konzept von webbasierten persönlichen Informations- und Lernmanagementsystemen an Aufmerksamkeit gewonnen, die sogenannte ,persönliche Lernumgebung\u27 (engl. ,Personal Learning Environment\u27, PLE). Schließlich werden die erst im Jahr 2012 bekannter gewordenen MOOC-Systeme vorgestellt. In diesem Beitrag werden keine technologischen Herausforderungen und Lösungen, sondern die praktischen Anforderungen und Wirkungen des Einsatzes der Systeme aus pädagogischer bzw. praktischer Perspektive betrachtet. Abschließend wird ein Ausblick gegeben, wie die Entwicklung solcher Systeme voranschreiten könnte. (DIPF/Orig.

Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work

First steps towards an integration of a Personal Learning Environment at university level

Ebner, M., Schön, S., Taraghi, B., Drachsler, H., & Tsang, P. (2011). First steps towards an integration of a Personal Learning Environment at university level. In R. Kwan et al. (Eds.), ICT 2011, CCIS 177 (pp. 22–36), Springer-Verlag Berlin: Heidelberg 2011.Personalization is seen as the key approach to handle the plethora of information in today’s knowledge-based society. It is expected that personalized teaching and learning will address the needs of the learners more efficiently. The education of the future will change by the influence of Web 2.0 contents and the steadily increasing amount of data. This means that the students of tomorrow will regularly have to deal with sharing and merging contents from different sources. Therefore, mashup technology will become a very important means to focus on individual learning needs and to personalize the access to particular information. The following article describes the challenges of Personal Learning Environments at higher education institutions. In the first section, the concept of Personal Learning Environments is presented, while the second section discusses the new challenges that arise for learning with the help of Personal Learning Environments. The third section of the article describes the technical background of Personal Learning Environments and the widget standard in general. In section four, a first prototype of a personal learning environment will be presented, which is integrated into the Technical University of Graz. A detailed description of the available widgets for the prototype, along with a first expert evaluation, will be provided. Finally, the conclusion of the article will sum up the main points of this paper and present the plans for future research together with the prospective developments.NeLLL AlterEg

Mining and Visualizing Usage of Educational Systems Using Linked Data

This work introduces a case study on usage of semantic context modelling and creation of Linked Data from logs in educational systems like a Personal Learning Environment (PLE) with focus on improvements and monitoring such systems, in generally, with respect to social, functional, user and activity centric level [7,15]. The case study demonstrates the application of semantic modelling of the activity context, from data collected over two years from the PLE at Graz University of Technology, using adequate domain ontologies, semantic technologies and visualization as reflection for potential technical and functional improvements. As it will be shown, this approach offers easy interfacing and extensibility on technological level and fast insight on statistical and preference trends for analytic tasks

Ubiquitous Personal Learning Environment (UPLE)

Web 2.0 technologies opened up new perspectives in learning and teaching activities. Collaboration, communication and sharing between learners contribute to the self-regulated learning, a bottom-up approach. The market for smartphones and tablets are growing rapidly. They are being used more often in everyday life. This allows us to support self-regulated learning in a way that learning resources and applications are accessible any time and at any place. This publication focuses on the Personal Learning Environment (PLE) that was launched at Graz University of Technology in 2010. After a first prototype a complete redesign was carried out to fulfill a change towards learner-centered framework. Statistical data show a high increase of attractiveness of the whole system in general. As the next step a mobile version is integrated. A converter for browser-based learning apps within PLE to native smartphone apps leads to the Ubiquitous PLE, which is discussed in this paper in detail

Ubiquitous Personal Learning Environment (UPLE)

Web 2.0 technologies opened up new perspectives in learning and teaching activities. Collaboration, communication and sharing between learners contribute to the self-regulated learning, a bottom-up approach. The market for smartphones and tablets are growing rapidly. They are being used more often in everyday life. This allows us to support self-regulated learning in a way that learning resources and applications are accessible any time and at any place. This publication focuses on the Personal Learning Environment (PLE) that was launched at Graz University of Technology in 2010. After a first prototype a complete redesign was carried out to fulfill a change towards learner-centered framework. Statistical data show a high increase of attractiveness of the whole system in general. As the next step a mobile version is integrated. A converter for browser-based learning apps within PLE to native smartphone apps leads to the Ubiquitous PLE, which is discussed in this paper in detail