Daniel Dahl’s research while affiliated with University of Münster and other places

Mashups have become the driving force behind the development of personal learning environments (PLEs). Creating mashups in an ad hoc manner is, however, for end users with little or no programming background not an easy task. Various tools and platforms have been built in an attempt to support mashup creation. These platforms, however, are still considered complex and do not address crucial challenges in mashup development today, such as scalability, interoperability, reuse, and automatic service invocation and mediation. In this paper, we leverage the possibility to use the concept of model-driven mashup development (MDMD) as an approach that can drastically tackle the aforementioned issues and drive the rapid and user-friendly creation of mashups. We then present the conceptual and technical details of PLEF-Ext as a flexible framework for end-user, model-driven development (MDD) of mashup PLEs.

When introducing the metadata standard LOM, objectives such as the ability to find or to reuse learning objects were followed. These objectives are actually achieved in LOM to a limited degree only, despite the designation as de-facto standard for description of electronic learning content. Based on the complexity of the standard, a high theoretical potential faces rejection in practice. One reason for this is that the process of metadata generation—for example, who creates which metadata attributes—is not defined in detail yet. This paper illustrates an approach which guarantees a high quantity as well as a high quality of learning object metadata records, bringing together known ways of metadata creation and the new paradigm of users describing content as implemented in recent Web 2.0 applications. In the context of a concrete e-learning platform, we exemplarily illustrate who creates which metadata records of LOM in which way at what time. Finally, we show why this approach of creating metadata matters as we measure our metadata quality and compare it with other's findings.

When introducing the metadata standard learning object metadata (LOM), objectives such as the ability to find or reuse learning objects were followed. These objectives are actually achieved in LOM only to a limited degree, despite the designation as de-facto standard for description of electronic learning content: Based on the complexity of the standard, a high the oretical potential faces rejection inpractice. One reason for this is that the process of metadata generation (e.g.., who creates which metadata attributes) is not defined in detail yet. This article illustrates an approach that guarantees a high quantity as well as a high quality of learning object metadata records, bringing together known ways of metadata creation and the new paradigm of users describing content as implemented in recent Web 2. 0 applications. In the context of a concrete e-learning platform, we exemplarily define who creates which metadata records of LOM in which way at what time.

Since blended learning has become increasingly popular, teachers as well as learners are supported by a number of electronic platforms: starting from the distribution of digital learning content, via automatically advised online exercises to final exams being carried out in a university's PC lab. However, the concept of intensive learning, for example immediately prior to an exam, is only supported to a minor degree, if at all; thus, a recapitulation of learning content often still takes place in a traditional way where digital content is printed on paper and then annotated with highlighters and post-its, for example. The Learnr Web platform under development at the University of Münster bridges this gap within the process of technology enhanced learning by adopting well-known learning paradigms to the digital world. Following a community-driven, Web 2.0-coined approach, on the one side structures representing the learners' language, collaboratively created summaries and file cards arise; at a social level bottom-up networking and learning communities are fostered through sharing of individually created material.

Repositories for learning objects typically offer two different views for the discovery of stored content: search based on keywords and navigation in a context-dependent directory. In the course of the Web 2.0 phenomenon, the social tagging mechanism provides a new way to categorise and organise content. This user-centred approach, generating the so-called folksonomies, is also offering high potential for the context of e-Learning repositories. The paper illustrates how this particular Web 2.0 technique can be exploited within the metadata repository share.loc and the content repository Learnr, both under development at the University of Muenster.. His research areas of interest include e-learning, in general, and especially web 2.0 and its implications for technology-enhanced learning.

Die Ziele, die mit der Einführung des Metadatenstandards LOM verfolgt wurden (z. B. die Auffindbarkeit und die Wiederverwendbarkeit von Lernobjekten) werden aktuell trotz einer weit reichenden Verbreitung und Akzeptanz nur in geringem Maße erreicht: Aufgrund der großen Komplexität des Standards steht dem in der Theorie immensen Potential eine deutliche Zurückhaltung in der Praxis gegenüber. Dies lässt sich u. a. darauf zurückführen, dass der Prozess der Metadatenerstellung (wer erstellt wann wie welche Attribute) bislang nicht im Detail definiert ist. Dieses Papier zeigt einen Weg auf, der gleichzeitig eine hohe Quantität wie Qualität bei der Erstellung von Lernobjekt-Metadaten sicherstellt. Dafür werden bekannte Ansätze der Metadatenerstellung kombiniert und erstmals die Potentiale des im Kontext der Web 2.0-Entwicklungen vorbildlich umgesetzten User-zentrierten Paradigmas zur Communitygetriebenen Metadatenerstellung von Lernobjekten genutzt. Am konkreten Beispiel des Einsatzes einer Lernplattform wird definiert, wer wann welche Metadatenattribute des Standards auf welche Art und Weise füllt.

The importance of metadata for e-learning is undisputed, particularly in terms of the semantic Web. Based on metadata standards such as LOM (learning object metadata), e-learning repositories can offer an effective and efficient search for e-learning content. The emerging paradigm of service-oriented architectures (SOAs) enables developers to implement open systems instead of monolithic ones. Well-defined interfaces for distributed systems have to be provided to enable easy integration of functionalities. In this context it is important to build the entire system architecture on a suitably chosen metadata standard. The e-learning repository share.loc presented in this paper uses special implementations of LOM within its different tiers of the entire architecture. An additional service tier that is implemented vertically to the common application tiers enables remote systems to request LOM-compliant representations of metadata from each tier that are tailored towards their individual needs