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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Learning, Moment-by-Moment and Over the Long Term
verfasst von : Yang Jiang, Ryan S. Baker, Luc Paquette, Maria San Pedro, Neil T. Heffernan
Erschienen in: Artificial Intelligence in Education
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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.