I provide a summary of the four invited articles in this special issue and compare and contrast different methods for measuring self-regulation in computer-based learning environments (CBLEs). I present a taxonomy that distinguishes between offline and online measures and further distinguishes subcategories within each of these categories. I discuss four measurement challenges, including orchestrating a repertoire of automated assessments to capture these ongoing activities, developing scoring rubrics and scoring criteria, interpreting assessments, and aligning multiple outcome measures. I also discuss how measures of self-regulated learning in CBLEs can be used to assess important cognitive processes such as planning, strategy use, monitoring, and increased learning. I conclude with several suggestions for measurement practices that enhance and further strengthen the validity of research conclusions. (Contains 1 table and 1 figure.)