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In Order to LearnHow the sequence of topics influences learning$
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Frank E. Ritter, Josef Nerb, Erno Lehtinen, and Timothy O'Shea

Print publication date: 2007

Print ISBN-13: 9780195178845

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195178845.001.0001

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The Effects of Order: A Constraint-Based Explanation

The Effects of Order: A Constraint-Based Explanation

(p.151) Chapter 11 The Effects of Order: A Constraint-Based Explanation
In Order to Learn

Stellan Ohlsson

Oxford University Press

This chapter presents a computational model that shows how information migrates from declarative to procedural knowledge and provides a powerful new learning mechanism for machine-learning algorithms. The model is used to examine the effects of learning three different counting tasks. The model predicts order effects that vary in several dimensions, including the number of times the model has to revise its knowledge and how long it will take to learn. Although some of these effects are quite large within a subtask, the overall effect is muted by other aspects of the task, including interaction. This model suggests that the complexity of a task's constraints is important for computing transfer between similar tasks. The model's behavior has been compared to human performance, and a general summary is provided.

Keywords:   information migration, declarative knowledge, procedural knowledge, learning, machine-learning algorithms

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