Nanogenetic learning analytics: illuminating student learning pathways in an online fraction game

  • Authors:

  • Taylor Martin;Ani Aghababyan;Jay Pfaffman;Jenna Olsen;Stephanie Baker;Philip Janisiewicz;Rachel Phillips;Carmen Petrick Smith

  • Affiliations:

  • Utah State University, Logan, UT;Utah State University, Logan, UT;Utah State University, Logan, UT;Utah State University, Logan, UT;University of Texas at Austin, Austin, TX;University of Texas at Austin, Austin, TX;University of Washington, Seattle, WA;The University of Vermont, Burlington, VT

  • Venue:
  • Proceedings of the Third International Conference on Learning Analytics and Knowledge
  • Year:
  • 2013

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Abstract

A working understanding of fractions is critical to student success in high school and college math. Therefore, an understanding of the learning pathways that lead students to this working understanding is important for educators to provide optimal learning environments for their students. We propose the use of microgenetic analysis techniques including data mining and visualizations to inform our understanding of the process by which students learn fractions in an online game environment. These techniques help identify important variables and classification algorithms to group students by their learning trajectories.