Learn More
There are many different ways and models how to characterize usage data to enable representation of user actions across learning management system, and systems in general. Based on this data, learning analytics can perform different analysis and provide personalized and meaningful information to improve the learning and teaching processes. There is a(More)
eLearning erfreut sich auch im organisationalen Kontext immer grö-ßerer Beliebtheit. Mit den Möglichkeiten mobiler Endgeräte wie Tablets erge-ben sich für professionelles Lernen und Wissensmanagement neue Perspekti-ven. Das hier vorgestellte Projekt PRiME (Professional Reflective Mobile Personal Learning Environments) vereint Lern-und Arbeitsprozesse und(More)
A key area of application for Learning Analytics (LA) and Educational Data Mining (EDM) is lifelong learner modeling. The aim is that data gathered from different learning environments would be fed into a personal lifelong learner model that can be used to foster personalized learning experiences. As learning is increasingly happening in open and networked(More)
Recommender systems are essential to overcome the information overload problem in professional learning environments. In this paper,weinvestigate interest-based recommendation in academic networks using social network analytics (SNA) methods. We implemented 21 different recommendation approaches based on traditional Collaborative Filtering (CF), Singular Va(More)
Learning analytics (LA) and Educational data mining (EDM) have emerged as promising technology-enhanced learning (TEL) research areas in recent years. Both areas deal with the development of methods that harness educational data sets to support the learning process. A key area of application for LA and EDM is learner modelling. Learner modelling enables to(More)