Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Contemporary Technologies in Education

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bakermans-Kranenburg, M. J., van Ijzendoorn, M. H., & Bradley, R. H. (2005). Those who have, receive: The Matthew effect in early childhood intervention in the home environment. Review of Educational Research, 75, 1–26.

    Article  Google Scholar 

  • Berman, B. (2016, October 14). Electric cars pros and cons. Retrieved January 8, 2016, from http://www.plugincars.com/electric-cars-pros-and-cons-128637.html

  • DiCerbo, K. E., & Behrens, J. T. (2014). Impacts of the digital ocean on education. London: Pearson.

    Google Scholar 

  • Eisenberg, M. B. (2008). Information literacy: Essential skills for the information age. Journal of Library & Information Technology, 28(2), 39–47.

    Google Scholar 

  • Hadwin, A. F., & Winne, P. H. (1996). Study skills have meager support: A review of recent research on study skills in higher education. Journal of Higher Education, 67, 692–715.

    Google Scholar 

  • Hadwin, A. F., & Winne, P. H. (2012). Promoting learning skills in undergraduate students. In M. J. Lawson & J. R. Kirby (Eds.), Enhancing the quality of learning: Dispositions, instruction, and mental structures (pp. 201–227). New York: Cambridge University Press.

    Chapter  Google Scholar 

  • Hadwin, A. F., Tevaarwerk, K. L., & Ross, S. (2005, April). Do study skills texts foster self-regulated learning: A content analysis. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Quebec.

    Google Scholar 

  • Hart Research Associates. (2013). It takes more than a major: Employer priorities for college learning and student success. Washington, DC: Author. Retrieved November 30, 2015, from https://www.aacu.org/sites/default/files/files/LEAP/2013_EmployerSurvey.pdf.

  • Pistilli, M. D., Willis, J. E., & Campbell, J. P. (2014). Analytics through an institutional lens: Definition, theory, design, and impact. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 79–102). New York: Springer.

    Chapter  Google Scholar 

  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.

    Google Scholar 

  • SSHRC. (2017, February 21). Future challenge areas. Retrieved from http://www.sshrc-crsh.gc.ca/funding-financement/programs-programmes/challenge_areas-domaines_des_defis/index-eng.aspx

  • What Works Clearinghouse. (n.d.). Procedures and standards handbook, version 3.0. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_procedures_v3_0_standards_handbook.pdf

  • Winne, P. H. (1982). Minimizing the black box problem to enhance the validity of theories about instructional effects. Instructional Science, 11, 13–28.

    Article  Google Scholar 

  • Winne, P. H. (1992). State-of-the-art instructional computing systems that afford instruction and bootstrap research. In M. Jones & P. H. Winne (Eds.), Adaptive learning environments: Foundations and frontiers (pp. 349–380). Berlin: Springer.

    Chapter  Google Scholar 

  • Winne, P. H. (2006). How software technologies can improve research on learning and bolster school reform. Educational Psychologist, 41, 5–17.

    Article  Google Scholar 

  • Winne, P. H. (2013). Learning strategies, study skills and self-regulated learning in postsecondary education. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 28, pp. 377–403). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Winne, P. H. (2017a). Leveraging big data to help each learner upgrade learning and accelerate learning science. Teachers College Record, 119(3), 1–24.

    Article  Google Scholar 

  • Winne, P. H. (2017b). Learning analytics for self-regulated learning. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 241–249). Beaumont: Society for Learning Analytics.

    Chapter  Google Scholar 

  • Winne, P. H., & Baker, R. S. J. D. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1–8.

    Google Scholar 

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Winne, P. H., Vytasek, J. M., Patzak, A., Rakovic, M., Marzouk, Z., Pakdaman-Savoji, A., Ram, I., Samadi, D., Lin, M. P. C., Liu, A., Liaqat, A., Nashaat, N., Mozaffari, Z., Stewart-Alonso, J., & Nesbit, J. C. (2017a). Designs for learning analytics to support information problem solving. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 249–272). New York: Springer.

    Chapter  Google Scholar 

  • Winne, P. H., Nesbit, J. C., & Popowich, F. (2017b). nStudy: A system for researching information problem solving. Technology, Knowledge and Learning, 22(3), 369–376.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Philip H. Winne

Authors
  1. Philip H. Winne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philip H. Winne .

Editor information

Editors and Affiliations

  1. College of Education, Washington State University, Pullman, WA, USA

    Olusola O. Adesope  & A. G. Rud  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

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

Download citation

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

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-319-89679-3

  • Online ISBN: 978-3-319-89680-9

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation