@inproceedings{00f945ada3b94876a8a9753668562099,
title = "Microgenetic analysis of learning a task: ITS implications to cognitive modeling",
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.",
author = "Kim, {Jong W.} and Ritter, {Frank E.}",
note = "Publisher Copyright: Copyright {\textcopyright} TRECVID 2016.All rights reserved.; 14th International Conference on Cognitive Modeling, ICCM 2016 ; Conference date: 03-08-2016 Through 06-08-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of ICCM 2016 - 14th International Conference on Cognitive Modeling",
publisher = "The Pennsylvania State University",
pages = "21--26",
editor = "David Reitter and Ritter, {Frank E.}",
booktitle = "Proceedings of ICCM 2016 - 14th International Conference on Cognitive Modeling",
}