Paving the Road Towards Supporting Scalable Collaborative Writing in High-Diversity Distance Learning Groups

Heutzutage besitzt das Lernen in Gruppen in einer virtuellen Umgebung eine hohe Relevanz, erst recht natürlich in der Fernlehre und während der andauernden Covid-19-­Pandemie. Daher sind die Hochschulen bestrebt, Software-Werkzeuge für das gemeinsame Online-Lernen und zur Unterstützung einer grossen Anzahl von gleichzeitig arbeitenden Studierenden zu entwickeln. Dafür ist es wichtig, Informationen über den laufenden Prozess des kollaborativen Arbeitens zu sammeln, insbesondere Lern- bzw. Interaktions­daten, also z. B. Daten, die zeigen, wie die Studierenden mit den anderen Gruppenmitgliedern interagieren und ob bzw. wie sie sich mit den Lehrenden austauschen. Diese erhobenen Daten werden dann mit Verfahren der Learning-Analytics mithilfe der Software-Werkzeuge untersucht und die Ergebnisse werden zur Unterstützung der Studierenden bzw. Lehrenden verwendet. In der vorliegenden Arbeit wird eine Architektur vorgeschlagen, die das Studieren im Bereich des kollaborativen Schreibens von Hunderten von Studierenden, eingeteilt in viele Gruppen, erleichtert. Sie nutzt die Synergie der Lernumgebung Moodle und des Online-Editors Etherpad Lite. In ihr lassen sich die erforderlichen Software-Werkzeuge leicht integrieren. Ein Prototyp der Architektur sowie erste grundlegende Methoden der Datenerfassung und Learning-Analytics wurden bereits entwickelt und in einem ersten Piloteinsatz mit ca. 300 Studierenden erfolgreich getestet. Die langfristige Ziel des Projekts besteht darin, das kollaborative Schreiben mittels eigenentwickelter Software in nahezu Echtzeit zu unterstützen.Nowadays, collaborative learning in a virtual environment is highly relevant, especially in distance education and during the ongoing Covid-19 pandemic. Therefore, higher education institutions are striving to develop software tools for collaborative online learning and to support large numbers of students working simultaneously. For this purpose, it is important to collect information about the ongoing process of the collaborative work, especially learning and interaction data, e.g. how the students interact with the other group members and whether or how they exchange information with the teachers. These collected data are then analysed with methods of learning analytics with the help of the software tools and the results are used to support learners and teachers. In this paper, an architecture is proposed that enables collaborative writing by hundreds of students divided into many groups. It uses the synergy of the learning environment Moodle and the online editor Etherpad Lite. The needed software tools can be easily integrated into it. A prototype of the architecture and first required methods of data collection and learning analytics have already been developed and successfully tested in a first pilot usage with about 300 students. The long-term goal of this project is to support collaborative writing in near real time using self-developed software

From Diversity to adaptive Personalization: The Next Generation Learning Management System as Adaptive Learning Environment

Learning Management Systems (LMS), as the most widely used online learning systems in formal education, confront all learners with the same learning environment, although the learning-relevant characteristics of the learner are by no means homogeneous. In this article, we highlight ways in which LMSs, by linking institutional data sources and analytics tools, can provide personalized learning environments that adaptivly adjust to learners' needs and learning progress. In future adaptive personalized learning environments, the LMS as we know it today will merely be a building block within an open, modular, and distributed system architecture. We propose a five layer architecture that fits into the existing IT landscape of educational institutions and enables the coexistence of different components and paths for processing, storing, and analyzing data for the adaptation of personalized learning environments. The components in each layers can be complemented or replaced by other systems and services as long as the interfaces of the neighboring layers can still be served. This allows not only a step-by-step construction of a complex system landscape, but also a distribution of the computing load and multiple use of resources and services