Mental capacity and working memory in chemistry: Algorithmic versus open-ended problem solving

Clair-Thompson, Helen St; Overton, Tina; Bugler, Myfanwy

Abstract

Previous research has revealed that problem solving and attainment in chemistry are constrained by mental capacity and working memory. However, the terms mental capacity and working memory come from different theories of cognitive resources, and are assessed using different tasks. The current study examined the relationships between mental capacity, working memory, algorithmic and open-ended problem solving, and A level chemistry grades. The results revealed that the best predictor of algorithmic problem solving and A level grades was performance on a counting recall task, which requires the simultaneous processing and storage of information within working memory. The best predictors of open-ended problem solving were backwards digit recall and the figural intersection test. The results therefore demonstrated a dissociation between the cognitive resources underlying algorithmic and open-ended problem solving. The results are discussed in terms of both theoretical and practical implications.

Citation

Clair-Thompson, H. S., Overton, T., & Bugler, M. (2012). Mental capacity and working memory in chemistry: Algorithmic versus open-ended problem solving. Chemistry Education Research and Practice, 13(4), 484-489. https://doi.org/10.1039/c2rp20084h

• Journal Article Type: Article
• Acceptance Date: Sep 6, 2012
• Online Publication Date: Sep 27, 2012
• Publication Date: Sep 27, 2012
• Deposit Date: Nov 13, 2014
• Journal: Chemistry Education Research And Practice
• Print ISSN: 1756-1108
• Publisher: Royal Society of Chemistry
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 4
• Pages: 484-489
• DOI: https://doi.org/10.1039/c2rp20084h
• Keywords: Education; Chemistry (miscellaneous)
• Public URL: https://hull-repository.worktribe.com/output/468492
• Publisher URL: http://pubs.rsc.org/en/Content/ArticleLanding/2012/RP/C2RP20084H#!divAbstract
• Contract Date: Nov 23, 2017