There is an extensive literature on self-regulated learning (SRL) and its interactions with several environmental and student characteristics. Although several theoretical models have been developed, different authors have focused on several different dimensions or components. While most of them agree that SRL is a complex and dynamic interaction of cognitive, affective, social, and volitional processes in the service of one's own goals, the field is still lacking a unified perspective on these complex phenomena. Definitions of SRL as a relatively stable individual inclination have been shifting to other definitions of SRL as a complex process in situated learning conditions. Several models have explored the relationship between motivational and other SRL processes on performance, and several motivational elements have been studied in integrated self-regulation models. Nevertheless, there is still no comprehensive model of how these complex variables interact in order to explain specific performances for different individuals under specific conditions. More importantly, there is as yet only sketchy understanding of how cognitive, affective, and motivational components interact in the service of goal-directed behavior and the implication of such complex interactions for our understanding of scientific interpretations of models in general. The main focus of this special issue will be on new methodological and conceptual developments in the understanding of self-regulated learning in different domains. We particularly take an interest in manuscripts that report the use of new methodologies that can address the complexities of the variable interactions and better predict learning outcomes or which center on new integrative conceptualizations of the field. Therefore, this special issue will become an international communication platform for researchers to summarize the most recent advances in the explanation, modeling, and prediction of specific performances in specific contexts, considering self-regulation factors, with special emphasis given to the technical and empirical results obtained within the last five years. Potential topics include, but are not limited to:

• Self-regulated learning assessment: new applications and methods
• New online measures to assess self-regulated learning
• Assessing self-regulatory skills in formal and informal learning settings
• Modeling SRL with various statistical methods
• Predictive systems approach in educational assessment: historical perspective and advantages
• Applications of SRL and cognitive data using artificial neural networks in modeling reading, writing, and mathematical performance
• Predicting general academic performance using SRL and cognitive data using artificial neural networks
• Implications of recent advances in the SRL field for our conceptualizations of human performance

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