Maren Scheffel

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This paper presents an approach of collecting contextualized attention metadata combined from inside as well as outside a LMS and analyzing them to create feedback about the student activities for the teaching staff. Two types of analyses were run on the collected data: first, key actions were extracted to identify usage patterns and tendencies throughout(More)
This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results(More)
We present new ways of detecting semantic relations between learning resources, e. g. for recommendations, by only taking their usage but not their content into account. We take concepts used in linguistic lexicology and transfer them from their original field of application, i. e. sequences of words, to the analysis of sequences of resources extracted from(More)
Successful self-regulated learning in a personalized learning environment (PLE) requires self-monitoring of the learner and reflection of learning behaviour. We introduce a tool called CAMera for monitoring and reporting on learning behaviour and thus for supporting learning reflection. The tool collects usage metadata from diverse application programs,(More)
Recently, a number of usage data representations have emerged that enable the representation of user activities across system and application boundaries. Based on these user activity data, systems can adapt to the users and provide personalized information. A lot of usage data representation formats are already successfully used in real world applications.(More)