Working Memory Load, Automaticity, and Problem Solving in College Engineering Students: Two Applications

Yi Ding, Qian Wang, Ru- De Liu, Jolene Trimm, Jiayi Wang, Shu Feng, Wei Hong and Xian-Tong Yang

SAGE Open, 2024, vol. 14, issue 4, 21582440241305082

Abstract: The paper examined the relations among problem solving, automaticity, and working memory load (WML) by changing the difficulty level of task characteristics through two applications. In Study 1, involving 68 engineering students, a 2 (automaticity) × 2 (WML) design was utilized for arithmetic problems. In Study 2, involving 76 engineering students, a 2 (automaticity) × 2 (WML) design was used for linear algebra tasks. In both studies, there were statistically significant main effects and interaction effects of automaticity and WML on the variable of response time, concurring with the cognitive load theory. The simple effect of WML rendered a larger effect size under the conditions with low automaticity. When the testing condition was easy but contained more steps, the students were more accurate, and response times were faster. When the testing condition was difficult but contained fewer steps, the students were less accurate, and response times were slower. The findings underscore the important role of automaticity in helping engineering students bypass the limits of working memory.

Keywords: working memory load; automaticity; multiplication; algebra; cognitive load theory (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:14:y:2024:i:4:p:21582440241305082

DOI: 10.1177/21582440241305082

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