Predicting Robust Learning With the Visual Form of the Moment-by-Moment Learning Curve

Ryan S. Baker, Arnon Hershkovitz, Lisa M. Rossi, Adam B. Goldstein, Sujith M. Gowda

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker, Goldstein, & Heffernan, 2010, 2011). Two coders labeled data from students who used an intelligent tutoring system for college genetics. They coded in terms of 7 forms that the moment-by-moment learning curve can take. These labels are correlated to test data on the robustness of students' learning. We find that different visual forms are correlated with very different learning outcomes. This work suggests that analysis of moment-by-moment learning curves may be able to shed light on the implications of students' different patterns of learning over time.

Original languageEnglish
Pages (from-to)639-666
Number of pages28
JournalJournal of the Learning Sciences
Issue number4
StatePublished - Oct 2013
Externally publishedYes


FundersFunder number
Pittsburgh Science of Learning Center
National Science FoundationDRL-0910188


    Dive into the research topics of 'Predicting Robust Learning With the Visual Form of the Moment-by-Moment Learning Curve'. Together they form a unique fingerprint.

    Cite this