ABSTRACT
This chapter briefly reviews the relevant theoretical ideas and established methods of using think-aloud protocols (TAPs) to capture self-regulated learning (SRL) processing. It reviews current scholarship on how to analyze TAP data, and focuses on the challenges associated with properly modeling these data within and across disciplines and contexts. This discussion of challenges provides background for the updated review of SRL scholarship utilizing TAPs, where the authors synthesize findings within and across disciplines. The majority of SRL researchers using TAP data have first segmented the transcriptions into codable units, and then used an a priori coding scheme to categorize each segment as evidence of a particular SRL process. These coding schemes can be derived from theory and refined through use across multiple datasets. Finally, the chapter focuses on modeling and capturing TAPs points to a number of important directions and implications for future research.
