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The ability of learning analytics to improve the learning/teaching processes is widely recognized. In this paper, the learning analytics architecture developed at the Digital Content Production Center of the Technical University of Cartagena (Spain) is presented. This architecture contributes to the field of learning analytics in two aspects: it allows for dashboard customization and improves the efficiency of the analysis of learners' interaction data. Events resulting from learners' interaction are captured and stored in Caliper standard format, to be further processed incrementally to allow dashboards to be shown without delay to teachers. Customization is considered a mandatory requirement for learning analytics tools, however, although some proposals have recently been made, a greater research effort in this topic is necessary. In the present work, this requirement is addressed by defining a domain-specific language (DSL) that allows teachers to customize dashboards. This language allows to express indicators (logical expressions) that classify students into different groups depending on their performance level. The paper also shows how our learning analytics approach was evaluated with a course that applies a flipped classroom method, and how it compares to the most relevant related works that have been published.

A Customizable and Incremental Processing Approach for Learning Analytics