Open Access

Daniel Schiffner, Marcel Ritter, Florian Horn

• We propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.