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Playing the role of constructive critic of articles in this special issue, I adopt the term self-reliance to refer to work habits, work styles, volition, and self-regulated learning. Starting with the belief that developing self-reliance is an important goal of education, I examine key issues that influence the likelihood of reaching this goal. I assume first that learners are agents. This human quality ubiquitously affects theorizing, researching, and teaching. I examine the nature of this influence in relation to three issues: How learners experiment to develop self-reliance, conditions that influence how learners express and extend self-reliance, and considerations about how to advance theory and practice in the service of enhancing self-reliance.

The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners' sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues' studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners' choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing "error variance" as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning.

Playing the role of constructive critic, the term self-reliance to refer to work habits, work styles, volition, and self-regulated learning are discussed. The belief that developing self-reliance is an important goal of education is described.

The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners' sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues' studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners' choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing "error variance" as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning. [ABSTRACT FROM AUTHOR]

Background: Today's gold standard for identifying what works, the randomized controlled trial, poorly serves each and any individual learner. Elements of my argument provide grounds for proposed remedies in cases where software can log extensive data about operations each learner applies to learn and each bit of information to which a learner applies those operations. Purpose of Study: Analyses of such big data can produce learning analytics that provide raw material for self-regulating learners, for instructors to productively adapt instructional designs, and for learning scientists to advance learning science. I describe an example of such a software system, nStudy. Research Design: I describe and analyze features of nStudy, including bookmarks, quotes, notes, and note artifacts that can be used to generate trace data. Results: By using software like nStudy as they study, learners can partner with instructors and learning scientists in a symbiotic and progressive ecology of authentic experimentation. Conclusion: I argue that software technologies like nStudy offer significant value in supporting learners and advancing learning science. A rationale and recommendations for this approach arise from my critique of pseudo-random controlled trials. [ABSTRACT FROM AUTHOR]