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

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Artificial Intelligence in Education (AIED 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9112))

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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.

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References

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Correspondence to Yang Jiang .

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© 2015 Springer International Publishing Switzerland

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Jiang, Y., Baker, R.S., Paquette, L., Pedro, M.S., Heffernan, N.T. (2015). Learning, Moment-by-Moment and Over the Long Term. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_84

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  • DOI: https://doi.org/10.1007/978-3-319-19773-9_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19772-2

  • Online ISBN: 978-3-319-19773-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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