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ZDM

, Volume 49, Issue 5, pp 735–753 | Cite as

Learner-controlled scaffolding linked to open-ended problems in a digital learning environment

  • Alden Jack Edson
Original Article
First Online:

Abstract

This exploratory study reports on how students activated learner-controlled scaffolding and navigated through sequences of connected problems in a digital learning environment. A design experiment was completed to (re)design, iteratively develop, test, and evaluate a digital version of an instructional unit focusing on binomial distributions and their applications for statistical inference. The developed materials are organized around open-ended problems linked to learner-controlled scaffolding. This study reports on a retrospective analysis of classroom observations, digital artifacts of student work, and interviews and surveys that document: (a) the ways students activated learner-controlled scaffolding linked to open-ended problems in the digital environment and (b) the observed student problem-solving pathways of activated scaffolding across connected sequences of problems related to binomial distributions and their applications for statistical inference. The results suggest that when students have the opportunity to control the level of access and challenge during problem solving using a digital medium, new opportunities are possible for the sequence of problems through which students can progress.

Keywords

Mathematics education Digital curriculum Scaffolding Problem-solving pathways 
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Notes

Acknowledgements

This research was supported by the Transition to College Mathematics and Statistics project with funding from the National Science Foundation Grant DRL-1020312. All opinions and analysis expressed herein are those of the author and do not necessarily represent the position or policies of the Foundation. The research and its interpretation reported in this article are based on the author’s doctoral dissertation completed at Western Michigan University under the direction of Christian R. Hirsch and Steven W. Ziebarth. A previous version of this article was presented at the American Educational Research Association 2016 Annual Meeting. The author gratefully acknowledges the work of Ann Watkins, California State University–Northridge, in developing the print version of the Binomial Distributions and Statistical Inference unit and that of James Laser, Western Michigan University, in coding the initial shell for the digital prototype.

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© FIZ Karlsruhe 2017

Authors and Affiliations

  1. 1.Michigan State UniversityEast LansingUSA

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