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Measuring cognitive load in introductory CS: adaptation of an instrument

Authors:

Briana B. Morrison,

Mark GuzdialAuthors Info & Claims

ICER '14: Proceedings of the tenth annual conference on International computing education research

Pages 131 - 138

https://doi.org/10.1145/2632320.2632348

Published: 28 July 2014 Publication History

Abstract

A student's capacity to learn a concept is directly related to how much cognitive load is used to comprehend the material. The central problem identified by Cognitive Load Theory is that learning is impaired when the total amount of processing requirements exceeds the limited capacity of working memory. Instruction can impose three different types of cognitive load on a student's working memory: intrinsic load, extraneous load, and germane load. Since working memory is a fixed size, instructional material should be designed to minimize the extraneous and intrinsic loads in order to increase the amount of memory available for the germane load. This will improve learning. To effectively design instruction to minimize cognitive load we must be able to measure the specific load components for any pedagogical intervention. This paper reports on a study that adapts a previously developed instrument to measure cognitive load. We report on the adaptation of the instrument to a new discipline, introductory computer science, and the results of measuring the cognitive load factors of specific lectures. We discuss the implications for the ability to measure specific cognitive load components and use of the tool in future studies.

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Cited By

Singh DMishra AAggarwal A(2024)Investigating the impact of logical reasoning in honing code comprehension skills: An empirical analysisProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676024(35-41)Online publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1145/3675888.3676024
Vriend SVidyapu SRama AChen KWeiskopf D(2024)Which Experimental Design is Better Suited for VQA Tasks?: Eye Tracking Study on Cognitive Load, Performance, and Gaze AllocationsProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653519(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3653519
Watkins AMiller CSettle AMonga MLonati VBarendsen ESheard JPaterson J(2024)Comparing the Experiences of Live Coding versus Static Code Examples for Students and InstructorsProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653562(506-512)Online publication date: 3-Jul-2024
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Index Terms

Measuring cognitive load in introductory CS: adaptation of an instrument
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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Published In

cover image ACM Conferences

ICER '14: Proceedings of the tenth annual conference on International computing education research

July 2014

186 pages

ISBN:9781450327558

DOI:10.1145/2632320

General Chairs:
Quintin Cutts
University of Glasgow, UK
,
Beth Simon
University of California, San Diego, USA
,
Brian Dorn
University of Nebraska, Omaha, USA

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Published: 28 July 2014

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ICER '14

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ICER '14: International Computing Education Research Conference

August 11 - 13, 2014

Scotland, Glasgow, United Kingdom

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ICER '14 Paper Acceptance Rate 17 of 69 submissions, 25%;

Overall Acceptance Rate 189 of 803 submissions, 24%

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Cited By

Singh DMishra AAggarwal A(2024)Investigating the impact of logical reasoning in honing code comprehension skills: An empirical analysisProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676024(35-41)Online publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1145/3675888.3676024
Vriend SVidyapu SRama AChen KWeiskopf D(2024)Which Experimental Design is Better Suited for VQA Tasks?: Eye Tracking Study on Cognitive Load, Performance, and Gaze AllocationsProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653519(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3653519
Watkins AMiller CSettle AMonga MLonati VBarendsen ESheard JPaterson J(2024)Comparing the Experiences of Live Coding versus Static Code Examples for Students and InstructorsProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653562(506-512)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653562
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Jin HKim J(2024)CodeTree: A System for Learnersourcing Subgoal Hierarchies in Code ExamplesProceedings of the ACM on Human-Computer Interaction10.1145/36373088:CSCW1(1-37)Online publication date: 26-Apr-2024
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Thorgeirsson SZhang CWeidmann TWeidmann KSu Z(2024)An Electroencephalography Study on Cognitive Load in Visual and Textual ProgrammingProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671124(280-292)Online publication date: 12-Aug-2024
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Thorgeirsson SWeidmann TWeidmann KSu ZStephenson BStone JBattestilli LRebelsky SShoop L(2024)Comparing Cognitive Load Among Undergraduate Students Programming in Python and the Visual Language AlgotProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630808(1328-1334)Online publication date: 7-Mar-2024
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Jin HLee SShin HKim J(2024)Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming EducationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642349(1-28)Online publication date: 11-May-2024
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