Feedback has been identified as one of the factors with the largest potential for a positive impact in a learning experience. There is a significant body of knowledge studying feedback and providing guidelines for its implementation in learning environments. In parallel, the areas of learning analytics or educational data mining have emerged to…

Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather…

The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper…

Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…

In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real-time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self-regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the…