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Analysing eCollaboration: Prioritisation of Monitoring Criteria for Learning Analytics in the Virtual Classroom

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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.


Rietze, Michel (2016): Analysing eCollaboration: Prioritisation of Monitoring Criteria for Learning Analytics in the Virtual Classroom. Workshop Gemeinschaften in Neuen Medien (GeNeMe) 2016. Dresden: TUDpress. pp. 78-92