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

• Published in: The Journal of the learning sciences Vol. 22; no. 4; pp. 639 - 666
• Main Authors: Baker, Ryan S., Hershkovitz, Arnon, Rossi, Lisa M., Goldstein, Adam B., Gowda, Sujith M.
• Format: Journal Article
• Language: English
• Published: Philadelphia Routledge 01.10.2013; Taylor & Francis Group; Taylor & Francis Ltd
• Subjects: Biology; Coding; Correlation; Data Collection; Data mining; Evaluation Methods; Genetics; Graphs; Intelligent Tutoring Systems; Learning; Learning Modules; Learning Processes; Pennsylvania; Predictor Variables; Pretests Posttests; Probability; Problem Solving; Regression (Statistics); Skill Development; Students; Tutoring; Undergraduate Students; Pennsylvania
• ISSN: 1050-8406; 1532-7809
• DOI: 10.1080/10508406.2013.836653

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.