Learning linear equations: capitalizing on cognitive load theory and learning by analogy

Title
Learning linear equations: capitalizing on cognitive load theory and learning by analogy
Publication Date
2022
Author(s)
Ngu, Bing Hiong
( author )
OrcID: https://orcid.org/0000-0001-9623-2938
Email: bngu@une.edu.au
UNE Id une-id:bngu
Phan, Huy P
( author )
OrcID: https://orcid.org/0000-0002-3066-4647
Email: hphan2@une.edu.au
UNE Id une-id:hphan2
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Taylor & Francis
Place of publication
United Kingdom
DOI
10.1080/0020739X.2021.1902007
UNE publication id
une:1959.11/52329
Abstract

Capitalizing on cognitive load theory and learning by analogy, we propose two instructional methods to learn a complex linear equation (e.g. two-step equation) by building on prior knowledge of a simpler linear equation (e.g. one-step equation). We will examine the proposal theoretically in this paper. In line with the design principles of cognitive load theory, we propose to strengthen students' prior knowledge of simpler linear equations before they learn complex linear equations with the aid of worked examples. Because a subset of the complex linear equation shares the same schema as the simpler linear equation, students can draw on their schema for the simpler linear equation to understand the complex linear equation, thus alleviating the limitation on working memory load. Based on the principles of learning by analogy, we place a simpler linear equation and a complex linear equation side-by-side and label the solution procedure of both linear equations to encourage active analogical comparison between these two equations. Making both the simpler linear equation and the complex linear equation visible to learners may help to reduce cognitive load demands in retrieving the simpler linear equation in order to facilitate the learning of the complex linear equation.

Link
Citation
International Journal of Mathematical Education in Science and Technology, 53(10), p. 2686-2702
ISSN
1464-5211
0020-739X
Start page
2686
End page
2702

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