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The effects of load reduction instruction on educational outcomes: An intervention study on hands‐on inquiry‐based learning in science.

  • Published In: Applied Cognitive Psychology, 2023, v. 37, n. 4. P. 814 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kadir, Munirah Shaik; Yeung, Alexander Seeshing; Caleon, Imelda Santos; Diallo, Thierno M. O.; Forbes, Anne; Koh, Wei Xun 3 of 3

Abstract

Load reduction instruction (LRI) is an instructional approach designed to manage the cognitive load on students as they learn complex learning materials. According to Cognitive Load Theory, complex learning is associated with high cognitive load and when not effectively managed, could impede learning. Inquiry‐based learning with hands‐on component, where students conduct experiments to find solutions to problems, are known to incur high cognitive load. In this study, we examined the effects on students' educational outcomes when the five key principles of the LRI framework were implemented to reduce the cognitive load of inquiry‐based learning with hands‐on involvement. Multiple regression analysis was used to compare the educational outcomes of the intervention and control groups. The control group also experienced hands‐on inquiry‐based learning, but without LRI. Results showed that students in the intervention group had better outcomes, indicating the effectiveness of LRI in managing the high cognitive load of complex instruction. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Applied Cognitive Psychology. 2023/07, Vol. 37, Issue 4, p814
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2023
  • ISSN:0888-4080
  • DOI:10.1002/acp.4077
  • Accession Number:164877601
  • Copyright Statement:Copyright of Applied Cognitive Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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