Learning, moment-by-moment and over the long term

Yang Jiang, Ryan S. Baker, Luc Paquette, Maria San Pedro, Neil T. Heffernan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 longerterm outcomes, and conclude with potential implications for instruction.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
EditorsCristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
Number of pages4
ISBN (Print)9783319197722
StatePublished - 2015
Externally publishedYes
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: Jun 22 2015Jun 26 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on Artificial Intelligence in Education, AIED 2015


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Learning, moment-by-moment and over the long term'. Together they form a unique fingerprint.

Cite this