ABSTRACT
This chapter describes the ways trace data can be used to observe learning processes, and how traced learning processes can be analyzed to test assumptions that underlie process models of self-regulated learning (SRL). It summarizes the assumptions that underlie process models of SRL and demonstrates how trace data are particularly useful for examining such assumptions. The chapter then describes the data and metadata that are logged when learners engage with common learning technologies and explore how methodological choices and technological features impact the validity of traces and the ways they may be used to test and refine assumptions of SRL theories. Depending on the learning environment in which SRL is being studied, SRL events can be observed at different grain sizes. In order to represent SRL appropriately, it is important to consider the time scale on which it occurs, and what individual events or combinations of events reflect an SRL process.
