Literature DB >> 23572251

Development of an instrument for measuring different types of cognitive load.

Jimmie Leppink1, Fred Paas, Cees P M Van der Vleuten, Tamara Van Gog, Jeroen J G Van Merriënboer.   

Abstract

According to cognitive load theory, instructions can impose three types of cognitive load on the learner: intrinsic load, extraneous load, and germane load. Proper measurement of the different types of cognitive load can help us understand why the effectiveness and efficiency of learning environments may differ as a function of instructional formats and learner characteristics. In this article, we present a ten-item instrument for the measurement of the three types of cognitive load. Principal component analysis on data from a lecture in statistics for PhD students (n = 56) in psychology and health sciences revealed a three-component solution, consistent with the types of load that the different items were intended to measure. This solution was confirmed by a confirmatory factor analysis of data from three lectures in statistics for different cohorts of bachelor students in the social and health sciences (ns = 171, 136, and 148), and received further support from a randomized experiment with university freshmen in the health sciences (n = 58).

Mesh:

Year:  2013        PMID: 23572251     DOI: 10.3758/s13428-013-0334-1

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  46 in total

1.  A preliminary psychometric evaluation of the eight-item cognitive load scale.

Authors:  Grant A Pignatiello; Emily Tsivitse; Ronald L Hickman
Journal:  Appl Nurs Res       Date:  2018-01-31       Impact factor: 2.257

Review 2.  Working memory is limited: improving knowledge transfer by optimising simulation through cognitive load theory.

Authors:  Michael Meguerdichian; Katie Walker; Komal Bajaj
Journal:  BMJ Simul Technol Enhanc Learn       Date:  2016-07-04

3.  An exploratory investigation of the measurement of cognitive load on shift: Application of cognitive load theory in emergency medicine.

Authors:  Kimberly M Vella; Andrew K Hall; Jeroen J G van Merrienboer; Wilma M Hopman; Adam Szulewski
Journal:  AEM Educ Train       Date:  2021-08-01

4.  Validity evidence for an instrument for cognitive load for virtual didactic sessions.

Authors:  Grace Hickam; Jaime Jordan; Mary R C Haas; Jason Wagner; David Manthey; Stephen John Cico; Margaret Wolff; Sally A Santen
Journal:  AEM Educ Train       Date:  2022-02-01

5.  Effects of learning content in context on knowledge acquisition and recall: a pretest-posttest control group design.

Authors:  Esther M Bergman; Anique B H de Bruin; Marc A T M Vorstenbosch; Jan G M Kooloos; Ghita C W M Puts; Jimmie Leppink; Albert J J A Scherpbier; Cees P M van der Vleuten
Journal:  BMC Med Educ       Date:  2015-08-15       Impact factor: 2.463

6.  Applying cognitive load theory to medical education: construct and measurement challenges.

Authors:  John Q Young; Justin L Sewell
Journal:  Perspect Med Educ       Date:  2015-06

7.  The evolution of cognitive load theory and its application to medical education.

Authors:  Jimmie Leppink; Angelique van den Heuvel
Journal:  Perspect Med Educ       Date:  2015-06

8.  Assessing Instructional Cognitive Load in the Context of Students' Psychological Challenge and Threat Orientations: A Multi-Level Latent Profile Analysis of Students and Classrooms.

Authors:  Andrew J Martin; Paul Ginns; Emma C Burns; Roger Kennett; Vera Munro-Smith; Rebecca J Collie; Joel Pearson
Journal:  Front Psychol       Date:  2021-07-01

9.  Do research findings on schema-based instruction translate to the classroom?

Authors:  Sarah Blissett; Mark Goldszmidt; Matt Sibbald
Journal:  Perspect Med Educ       Date:  2015-12

10.  Using cognitive theory to facilitate medical education.

Authors:  Yu Qi Qiao; Jun Shen; Xiao Liang; Song Ding; Fang Yuan Chen; Li Shao; Qing Zheng; Zhi Hua Ran
Journal:  BMC Med Educ       Date:  2014-04-14       Impact factor: 2.463

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.