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Technology, Knowledge and Learning

, Volume 22, Issue 3, pp 369–376 | Cite as

nStudy: A System for Researching Information Problem Solving

  • Philip H. Winne
  • John C. Nesbit
  • Fred Popowich
Original research
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Abstract

A bottleneck in gathering big data about learning is instrumentation designed to record data about processes students use to learn and information on which those processes operate. The software system nStudy fills this gap. nStudy is an extension to the Chrome web browser plus a server side database for logged trace data plus peripheral modules that analyze trace data and assemble web pages as learning analytics. Students can use nStudy anywhere they connect to the internet. Every event related to creating, modifying, reviewing, linking and organizing information artifacts is logged in fine grain with a time stamp. These data fully trace information students operate on and how they operate on it. Ambient big data about studying gathered au naturel can be tailored by configuring several of nStudy’s features. Thus the system can be used to gather data across a wide range lab studies and field trials designed to test a range of models and theories.

Keywords

Information problem solving Trace data Big data Online learning 
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Notes

Acknowledgements

This research was supported by grants from the Social Sciences and Humanities Research Council of Canada.

References

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Faculty of EducationSimon Fraser UniversityBurnabyCanada
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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