Purpose – This paper is part of an extensive action research project on learning analytics and focuses on the analysis criteria in Virtual Collaborative Learning (VCL) settings. We analyse how the efficiency of virtual learning facilitation can be increased by (semi-) automated learning analytics. Monitoring items are the starting point that enable the learning facilitator to identify learning problems and deduce adequate actions of intervention. However, the sophisticated media-based learning environment does not allow monitoring of vast amounts of items and appreciate the learning processes simultaneously. Design/methodology/approach – This paper fulfils the sub-goal of selecting and prioritising monitoring items for e-collaboration. The procedure is split into two Research Questions (RQ). A specification of the monitoring items will be compiled by a comparison and a consolidation of the already existing monitoring sheets. Therefore, we interviewed the responsible docents on differences and similarities. Additionally, we coded each monitoring item inductively due to their monitoring objective. As a result, we reduced the monitoring sheets to 40 final monitoring items (RQ1). In order to prioritise them, the learning facilitators scored the relevance and the complexity of the collection and assessment of data using a questionnaire. The analysis focused on differences in understanding of relevance and complexity. Further, we identified the highest scored monitoring items as well as scores with leverage potential. Afterwards we prioritised the items based on the applied analysis (RQ2). Originality/value – While previous studies on learning analytics were mostly driven by the educational data mining field and as a consequence had a technological focus. This paper is based on an existing pedagogical concept of VCL and therefore prioritises monitoring items to be implemented as selected learning analytics. Hence, it is guaranteed that the analysis is related directly to the learning content. Practical implications – This research paper achieved two outcomes: Firstly, a courseindependent standardised monitoring sheet. Thus, the reduction of the monitoring items should simplify and objectify the observation and clarify the performance review. Secondly, an insight into the relevance of each monitoring item had been delivered to the facilitators and provides significance on the quality of e-collaboration. Furthermore, the complexity score shows the necessary effort for data collection and assessment while the combination of relevance and complexity scores leads to the prioritisation of the needs of (semi-) automated learning analytics to support the learning facilitation.

Die Erforschung der Zusammenstellung kleiner Lerngruppen beschäftigt sich mit dem Problem, eine passende Gruppenzusammensetzung in einer Population von Lernern zu finden, die jeder Gruppe optimalen Nutzen bringen könnte. In letzter Zeit sind viele Studien zu diesem Problem der Kleingruppenzusammenstellung durchgeführt worden. Allerdings waren diese Forschungen nur selten auf den Kontext großer Lerner-Populationen ausgerichtet. Angesichts des zunehmenden Aufkommens von MOOCs muss jedoch das Problem der Gruppenzusammenstellung entsprechend erweitert betrachtet werden, und zwar mit neuen Forschungen, die den Kontext derartig großer Lerner-Populationen berücksichtigen. Anders als in Klassenzimmer-Settings könnte die beobachtete hohe Abbruchquote in MOOCs in einer Unterbesetzung der Gruppengröße resultieren und könnte somit viele Lerner dazu bringen, neue Gruppen zu bilden. Zusätzlich zur Gruppenzusammenstellung muss daher die Gruppenneuzusammenstellung als neues Thema in aktuellen Kontexten großer Lerner-Populationen ebenfalls erforscht werden. Die Untersuchungen der vorliegenden Arbeit gliedern sich in zwei Teile. Der erste Teil beschäftigt sich mit Gruppenzusammenstellung. In diesem Teil stelle ich einen diskreten-PSO Algorithmus zur Zusammenstellung kleiner Lerngruppen vor und vergleiche bislang bestehende Gruppenzusammenstellungs-Algorithmen unter den Gesichtspunkten Zeitaufwand und Gruppierungsqualität. Um Gruppenzusammenstellung in MOOCs anzuwenden wurde ein Gruppenzusammenstellungsexperiment in einem MOOC durchgeführt. Die Hauptergebnisse deuten darauf hin, dass die Gruppenzusammenstellung die Abbruchsquote reduzieren kann, jedoch lediglich einen sehr schwachen Bezug zur Lernperformanz der Lerner aufweist. Der zweite Teil beschäftigt sich mit Gruppenneuzusammenstellung. Die vorliegende Arbeit stellt eine datengesteuerte Herangehensweise vor, die umfassenden Gebrauch von Gruppeninteraktionsdaten macht sowie Gruppendynamik mit einbezieht. Mittels einer in einem Simulationsexperiment durchgeführten Evaluation zeigen sich die Vorteile dieses Verfahrens: Der Lerngruppenzusammenhalt wird verbessert und die Abbruchsquote im Vergleich zu einer Zufallsverteilung reduziert. Darüberhinaus wurde hier ein Gruppen-Lern-Werkzeug entwickelt und für die Praxis vorbereitet, das die Anforderungen des geforderten Ansatzes der Gruppenneuzusammenstellung erfüllt. / Small learning group composition addresses the problem of seeking such matching among a population of students that it could bring each group optimal benefits. Recently, many studies have been conducted to address this small group composition problem. Nevertheless, the focus of such a body of research has rarely been cast to large-scale contexts. Due to the recent come of MOOCs, the topic of group composition needs to be accordingly extended with new investigations in such large learning contexts. Different from classroom settings, the reported high drop-out rate of MOOCs could result in group’s incompletion in size and thus might compel many students to compose new groups. Thus, in addition to group composition, group re-composition as a new topic needs to be studied in current large-scale learning contexts as well. In this thesis, the research is structured in two stages. The first stage is group composition. In this part, I proposed a discrete-PSO algorithm to compose small learning groups and compared the existing group composition algorithms from the perspectives of time cost and grouping quality. To implement group composition in MOOCs, a group composition experiment was conducted in a MOOC. The main results indicate that group composition can reduce drop-out rate, yet has a very weak association with students’ learning performance. The second stage is to cope with group re-composition. This thesis suggests a data-driven approach that makes full use of group interaction data and accounts for group dynamics. Through evaluation in a simulation experiment, it shows its advantages of bringing us more cohesive learning groups and reducing the drop-out rate compared to a random condition. Apart from these, a group learning tool that fulfills the goals of the proposed group re-composition approach has been developed and is made ready for practice.