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Enhancing Self-Regulated Learning for Information Problem Solving with Ambient Big Data Gathered by nStudy

Abstract

Learning projects are major academic assignments. They benefit from productive self-regulated learning to improve skills for solving information problems of searching for, analyzing, mining and organizing unfamiliar content. Findings from randomized controlled trials (RCTs), the “gold standard” for research, are recommended to meet these needs but RCTs poorly serve this purpose. A state-of-the-art learning technology, nStudy, is proposed to support a new approach to learning science and help fill gaps RCTs cannot. In the course of learners’ everyday studying activities, nStudy gathers ambient, fine-grained, trace data fully cataloging information learners operate on and operations they apply to information. Big ambient trace data are raw material for developing learning analytics that support self-regulated learning for improving information problem solving.

Keywords

  • Self-regulated learning
  • nStudy
  • Learning analytics
  • Problem solving
  • Big data

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Winne, P.H. (2019). Enhancing Self-Regulated Learning for Information Problem Solving with Ambient Big Data Gathered by nStudy. In: Adesope, O.O., Rud, A.G. (eds) Contemporary Technologies in Education. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-89680-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-89680-9_8

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