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Learning, Moment-by-Moment and Over the Long Term

  • Yang Jiang
  • Ryan S. Baker
  • Luc Paquette
  • Maria San Pedro
  • Neil T. Heffernan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9112)

Abstract

The development of moment-by-moment learning graphs (MBMLGs), which plot predictions about the probability that a student learned a skill at a specific time, has already helped to improve our understanding of how student performance during the learning process relates to robust learning [1]. In this study, we extend this work to study year-end learning outcomes and to account for differences in learning on original questions and within knowledge-construction scaffolds. We discuss which quantitative features of moment-by-moment learning in these two contexts are predictive of the longer-term outcomes, and conclude with potential implications for instruction.

Keywords

Moment-by-moment learning Scaffolding Intelligent tutoring system Educational data mining 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yang Jiang
    • 1
  • Ryan S. Baker
    • 1
  • Luc Paquette
    • 1
  • Maria San Pedro
    • 1
  • Neil T. Heffernan
    • 2
  1. 1.Teachers CollegeColumbia UniversityNew YorkUSA
  2. 2.Worcester Polytechnic InstituteWorcesterUSA

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