Microgenetic analysis of learning a task: ITS implications to cognitive modeling

Jong W. Kim, Frank E. Ritter

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

4 Scopus citations

Abstract

We report a microgenetic and quantitative analysis of a large learning data set. We analyzed performance change by four practice trials (once per day) and by the 14 different subtasks with more than 500 total keystrokes. Specifically, we compared performance change across the subtasks—some subtasks are cognitive problem-solving and others are perceptual-motor driven tasks. This microgenetic approach provides an understanding of how a local performance in a task affects the global performance of a whole task. We computed the learning curve constants for the different subtasks. We found evidence to support the KRK theory of learning and retention (Kim & Ritter, 2015). The results suggest that learning varies by subtask and by its type.

Original languageEnglish (US)
Title of host publicationProceedings of ICCM 2016 - 14th International Conference on Cognitive Modeling
EditorsDavid Reitter, Frank E. Ritter
PublisherThe Pennsylvania State University
Pages21-26
Number of pages6
ISBN (Electronic)9780998508207
StatePublished - 2016
Event14th International Conference on Cognitive Modeling, ICCM 2016 - University Park, United States
Duration: Aug 3 2016Aug 6 2016

Publication series

NameProceedings of ICCM 2016 - 14th International Conference on Cognitive Modeling

Conference

Conference14th International Conference on Cognitive Modeling, ICCM 2016
Country/TerritoryUnited States
CityUniversity Park
Period8/3/168/6/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Modeling and Simulation

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