Efficient Learning Processes by Design: Analysis of Usage Patterns in Differently Designed Digital Self-Learning Environments

Malte Neugebauer, Ralf Erlebach, Christof Kaufmann, Janis Mohr, Jörg Frochte

Abstract: The relevance of e-learning for higher education has resulted in a wide variety of online self-learning materials over the last decade like pedagogical agents (PA) or learning games. Regardless of this variety, educators wonder whether they can make use of these tools for their goals and if so, which tool to choose and in which context a specific tool performs best. To do so, the collection and analysis of learning data – referred to as Learning Analytics (LA) – is required. Along with digital learning environments the possibilities of applying LA are growing. Often, LA focuses on data that can easily be quantified: drop-out quota, time or grade performance. To facilitate learning in a more procedural sense, a deeper understanding of learners’ behavior in specific contexts with specific exercise designs is desired. This study therefore focuses on usage patterns. Learners’ movements through three different designs of mathematical exercises – (i) plain exercises, (ii) PA supported and