Microgenetic Analysis Of Learning A Task: Its Implications To Cognitive Modeling

Keywords

Cognitive modeling; Learning; Microgenetic analysis

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.

Publication Date

1-1-2016

Publication Title

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

Number of Pages

21-26

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85068700208 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85068700208

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