Cited by 220Visual and Cognitive Learning ProcessesIntelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.

Measuring Knowledge to Optimize Cognitive Load Factors During Instruction.

Open access

Kalyuga, Slava, Sweller, John·2004·Journal of Educational Psychology

DOI 10.1037/0022-0663.96.3.558

Open this paper in CoChat — free account

Abstract

The expertise reversal effect occurs when a learning procedure that is effective for novices becomes ineffective for more knowledgeable learners. The authors consider how to match instructional presen-tations to levels of learner knowledge. Experiments 1–2 were designed to develop a schema-based rapid method of measuring learners ’ knowledge in a specific area. Experimental data using algebra and geometry materials for students in Grades 9–10 indicated a highly significant correlation (up to.92) between performance on the rapid measure and traditional measures of knowledge, with test times reduced by factors of 4.9 and 2.5, respectively. Experiments 3–4 used this method to monitor learners’ cognitive performance to determine which instructional design should be used for given levels of expertise. The expertise reversal effect (see Kalyuga, Ayres, Chandler, & Sweller, 2003) occurs when an instructional procedure that is relatively effective for novices becomes ineffective for more knowledgeable learners. A consequence of the effect is that an instructor must be able to accurately estimate the knowledge levels of learners to determine an appropriate instructional design for

Integrity signals

Registry

Crossref · 10.1037/0022-0663.96.3.558

Cites

66 of 66 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1994) · Psychological Review · doi:10.1037/0033-295x.101.2.343 · Cited by 17,559
Review of research in education
Shulman, L. S., Mowry, S., & Berliner, D. C. (1973) · F.E. Peacock eBooks · Cited by 7,754
Cognitive Architecture and Instructional Design
Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998) · Educational Psychology Review · doi:10.1023/a:1022193728205 · Cited by 6,023
Self-citation
The Psychology of Learning and Motivation
(2021) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(21)x0002-3 · Cited by 5,082
Perception in chess
Chase, W. G., & Simon, H. A. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90004-2 · Cited by 4,479
Long-term working memory.
Ericsson, K. A., & Kintsch, W. (1995) · Psychological Review · doi:10.1037/0033-295x.102.2.211 · Cited by 3,498
Cognitive Load Theory and Instructional Design: Recent Developments
Paas, F., Renkl, A., & Sweller, J. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_1 · Cited by 3,041
Self-citation
Cognitive Load Theory and the Format of Instruction
Chandler, P., & Sweller, J. (1991) · Cognition and Instruction · doi:10.1207/s1532690xci0804_2 · Cited by 2,894
Self-citation
Educational measurement, 3rd ed.
Linn, R. L. (1989) · Cited by 2,858

Show all 68 references

Research works printed but not linked: 8

Adams, 1996, Formulating the Rasch model as a mixed coefficients multinomial logit., Vol. 3, 143
10.2307/1170661 · DOI
Baddeley, 1986, Working memory.
Embertson, 1993, Psychometric models for learning and cognitive processes., 125
10.1016/0364-0213(90)90008-K · DOI
10.1016/S0364-0213(80)80008-5 · DOI
Martin, 1995, A Bayesian approach to cognitive assessment., 141
Sweller, 1999, Instructional design.

Sorted by citations

Items 101–200 of 220

A Cognitive Analysis of Students' Activity: An Example in Mathematics
Hérold, J. (2014) · The Australian journal of teacher education · doi:10.14221/ajte.2014v39n1.10 · Cited by 16
Digital Multimedia Perception and Design
(2006) · IGI Global eBooks · doi:10.4018/978-1-59140-860-4 · Cited by 16
Developing Tactical Skills through the Use of Static and Dynamic Soccer Visualizations: An Expert–Nonexpert Differences Investigation
Khacharem, A., Zoudji, B., Kalyuga, S., & Ripoll, H. (2012) · Journal of Applied Sport Psychology · doi:10.1080/10413200.2012.735342 · Cited by 17
Self-citation
Application to learning and teaching
Harris, K. R., Graham, S. W., & Urdan, T. (2012) · American Psychological Association eBooks · Cited by 15
Imagining Instructions: Mental Practice in Highly Cognitive Domains
Ginns, P. (2005) · Australian Journal of Education · doi:10.1177/000494410504900202 · Cited by 15
Rethinking Cognitive Load Theory
Kalyuga, S., & Plass, J. L. (2025) · doi:10.1093/9780190078539.001.0001 · Cited by 18
Self-citation
The Effects of Rapid Assessments and Adaptive Restudy Prompts in Multimedia Learning.
Renkl, A., Skuballa, I. T., Schwonke, R., Harr, N., & Leber, J. (2015) · Educational Technology & Society · Cited by 14
Framework for Instructional Technology: Methods of Implementing Adaptive Training and Education
Durlach, P. J., & Spain, R. D. (2014) · Defense Technical Information Center (DTIC) · doi:10.21236/ada597411 · Cited by 14
Are Inductive Teaching Methods Compatible with Cognitive Load Theory?
Gorbunova, A., van Merriënboer, J. J. G., & Costley, J. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09828-z · Cited by 23
Using a Personal Response System To Map Cognitive Efficiency and Gain Insight into a Proposed Learning Progression in Preparatory Chemistry
Murphy, K. L. (2012) · Journal of Chemical Education · doi:10.1021/ed100859n · Cited by 13
Effectiveness of Combining Worked Examples and Deliberate Practice for High School Geometry
Pachman, M., Sweller, J., & Kalyuga, S. (2014) · Applied Cognitive Psychology · doi:10.1002/acp.3054 · Cited by 12
Self-citation
Scheduling Scaffolding: The Extent and Arrangement of Assistance During Training Impacts Test Performance
Tullis, J. G., Goldstone, R. L., & Hanson, A. J. (2015) · Journal of Motor Behavior · doi:10.1080/00222895.2015.1008686 · Cited by 12
Use of worked‐example videos to support problem‐solving: An analysis of student behavior
Wu, C., DeBoer, J. J., Rhoads, J. F., & Berger, E. (2021) · Computer Applications in Engineering Education · doi:10.1002/cae.22451 · Cited by 13
LEARNER CHARACTERISTIC BASED LEARNING EFFORT CURVE MODE: THE CORE MECHANISM ON DEVELOPING PERSONALIZED ADAPTIVE E- LEARNING PLATFORM
Hsu, P.-S. (2012) · The Turkish Online Journal of Educational Technology · Cited by 11
How Revisions to Mathematical Visuals Affect Cognition
Clinton‐Lisell, V., Cooper, J., Michaelis, J. E., Alibali, M. W., & Nathan, M. J. (2016) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-1005-5.ch010 · Cited by 11
The Effect of an Instruction Designed by Cognitive Load Theory Principles on 7th Grade Students’ Achievement in Algebra Topics and Cognitive Load
Takır, A., & Aksu, M. (2012) · Creative Education · doi:10.4236/ce.2012.32037 · Cited by 11
Adaptive Instruction to Learner Expertise with Bimodal Process-oriented Worked-out Examples
Si, J., Kim, D., & Na, C. (2014) · Educational Technology & Society · Cited by 10
Promoting online learning processes and outcomes via video examples and prompts
Hefter, M. H., & Berthold, K. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2022.2164786 · Cited by 10
Cognitive Load in Adaptive Multimedia Learning
Kalyuga, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-9625-1_15 · Cited by 9
Self-citation
A developmental study on a SPAT design model for mobile learning
Park, S. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9630-x · Cited by 8
Bridging the Gap Between Adaptive Training Research and Army Practice
Schaefer, P. S., & Dyer, J. L. (2012) · Military Psychology · doi:10.1080/08995605.2012.672911 · Cited by 8
The influence of the order and congruency of correct and erroneous worked examples on learning and (meta-)cognitive load
Wesenberg, L., Krieglstein, F., Jansen, S., Rey, G. D., Beege, M., & Schneider, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1032003 · Cited by 9
Effects of background context and signaling on comprehension recall and cognitive load: The perspective of cognitive load theory
Song, M. (2011) · Lincoln (University of Nebraska) · Cited by 7
The Use of Integrative Framework to Support the Development of Competences
Oliveira, M., Andersen, B. S., Pereira, J. M., Seager, W., & Ribeiro, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23834-5_11 · Cited by 7
Wissenserwerb
Renkl, A. (2020) · Springer eBooks · doi:10.1007/978-3-662-61403-7_1 · Cited by 9
Learners’ Cognitive Load When Using Educational Technology
Low, R., Jin, P., & Sweller, J. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-195-9.ch708 · Cited by 7
Self-citation
Expertise reversal effect and sequencing of learning tasks in online English as a second language learning environment
Song, D. (2013) · Interactive Learning Environments · doi:10.1080/10494820.2013.862553 · Cited by 8
Alignment of ACS Inorganic Chemistry Examination Items to the Anchoring Concepts Content Map
Marek, K. A., Raker, J. R., Holme, T. A., & Murphy, K. L. (2018) · Journal of Chemical Education · doi:10.1021/acs.jchemed.8b00241 · Cited by 6
Constructing a coherent problem model to facilitate algebra problem solving in a chemistry context
Ngu, B. H., Yeung, A. S., & Phan, H. P. (2014) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2014.979899 · Cited by 6
Rapid Dynamic Assessment for Learning
Kalyuga, S. (2012) · Springer eBooks · doi:10.1007/978-94-007-4507-0_3 · Cited by 6
Self-citation
Cognitive Load Theory
Garvey, A. M., Angulo, J. A. G., & Esteban, L. P. (2017) · Review of Business and Legal Sciences · doi:10.26537/rebules.v0i28.1024 · Cited by 6
Technology and Effective Assessment Systems
Baker, E. L. (2005) · Yearbook of the National Society for the Study of Education · doi:10.1111/j.1744-7984.2005.00037.x · Cited by 6
Graphic organizers’ optimized design through segmenting and signaling principle: Based on generative learning theory
Qin, T., & Wu, C. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105416 · Cited by 5
Chunking feedback in instructor-learner interaction facilities long-term learning transfer: Behavioral and fNIRS hyperscanning studies
Yi, Z. H. U., & Hu, Y. (2024) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2024.00555 · Cited by 7
Sequential effects of high and low guidance on children's early science learning
Matlen, B. J., & Klahr, D. (2010) · International Conference of Learning Sciences · Cited by 5
Worked Examples
Sweller, J., & Ayres, P. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199841332.013.17 · Cited by 5
Self-citation
Using Virtual Labs in an Inquiry Context
Hovardas, T., Xenofontos, N. A., & Zacharia, Z. C. (2017) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-2528-8.ch003 · Cited by 5
Towards Individualized Instruction with Technology-Enabled Tools and Methods
Chung, G. K. W. K., Delacruz, G. C., Dionne, G. B., Baker, E. L., Lee, J. R., & Osmundson, E. (2007) · American Educational Research Association Annual Meeting · Cited by 5
The assimilation of complex accounting concepts using the Cognitive Load Theory as a framework
Garvey, A. M., Esteban, L. P., & Angulo, J. A. G. (2017) · EDUCADE - Revista de Educación en Contabilidad Finanzas y Administración de Empresas · doi:10.12795/educade.2017.i08.04 · Cited by 4
Research Methods in Multimedia Learning
Jarodzka, H. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108894333.006 · Cited by 5
An Efficient Approach to Slicing Learning Video to Improve Learning Effectiveness by Considering Learner Prior Knowledge
Lee, C.-I., & Tsai, C.-L. (2018) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/87018 · Cited by 4
Examining Preservice Teachers' Reasoning and Decision Making in Three Case-Based Approaches
Çevik, Y. D. (2011) · Iowa State University Digital Repository (Iowa State University) · doi:10.31274/etd-180810-3078 · Cited by 4
Measuring Noncommissioned Officer Knowledge and Experience to Enable Tailored Training
Schaefer, P. S., Blankenbeckler, P. N., & Brogdon, C. J. (2011) · doi:10.21236/ada552941 · Cited by 4
Defining Tailored Training Approaches for Army Institutional Training
Schaefer, P. S., & Dyer, J. L. (2013) · Defense Technical Information Center (DTIC) · doi:10.21236/ada578565 · Cited by 4
Evolutionary perspective on human cognitive architecture: Implications for adaptive instruction Commentary on the Special Issue on adaptivity
Kalyuga, S. (2025) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102300 · Cited by 4
Self-citation
Encouraging Metacognitive Reflection through Prompts in a Computer-Based Learning Platform: Failure to Find a Benefit in a Large-Scale Randomized Trial
Zengilowski, A., Schuetze, B. A., Siedahmed, A., Yan, V. X., & Heffernan, N. T. (2025) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3698205.3729547 · Cited by 3
The predictive effects of AI anxiety on 21st-century skills and lifelong learning tendencies: a study of pre-service teachers in Northern Cyprus
Cantaş, Ç., Celep, C., & BATUR, Ö. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1810689 · Cited by 4
Contemporary Approaches to Assessment of Engineering Competencies for Diverse Learners
Douglas, K. A., Neumann, K., & Oliveri, M. E. (2023) · Routledge eBooks · doi:10.4324/9781003287483-37 · Cited by 3
Reconceptualizing Cognitive Load Theory
Kalyuga, S., & Plass, J. L. (2025) · Oxford University Press eBooks · doi:10.1093/9780190078539.003.0007 · Cited by 3
Self-citation
Learning to use electronic outlining via observational learning: Effects on students' argumentative writing performance
de Smet, M. J. R., Brand‐Gruwel, S., & Kirschner, P. A. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12825 · Cited by 5
Towards a programming environment that adaptively suggests examples and corresponding puzzles based on programmer skill
Harms, K. J. (2014) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2014.6883047 · Cited by 3
How to Build Bridges between Intelligent Tutoring System Subfields of Research
Pavlik, P. I., & Toth, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13437-1_11 · Cited by 3
The Use of Learning Study in Designing Examples for Teaching Physics
Guo, J., Yang, L.-Y., & Ding, Y. (2017) · Research in Science Education · doi:10.1007/s11165-017-9635-1 · Cited by 3
Learning Strategies Design to Accommodate Learning Styles, Initial Knowledge and Reduce The Differences of Scientific Reasoning and Argumentation Performance
Naj’iyah, A., Viyanti, & Suyatna, A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1788/1/012031 · Cited by 3
The effects of concept mapping experience, feedback timing, and motivation on students’ learning outcomes
Sunday, O. J., Adesope, O., Dai, S., & Carbonneau, K. J. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102201 · Cited by 6
A New Process Phase Diagnostic Technique
Hsu, P.-S., & Chang, T.-J. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-539-1.ch009 · Cited by 2
Identification and Analysis of Item Environment Effects on Organic Chemistry Multiple-Choice Exams
Michels, O. R., Schreurs, D. G., Nedungadi, S., Pentecost, T. C., Kreke, P. J., Raker, J. R., & Murphy, K. L. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00921 · Cited by 2
Criticisms and Desired Additional Modifications
Kalyuga, S., & Plass, J. L. (2025) · Oxford University Press eBooks · doi:10.1093/9780190078539.003.0004 · Cited by 1
Self-citation
A Dynamic User-Friendly Interactive Interface for Adaptive e-Learning: A Real-Time and Non-interference Diagnostic Technique
Chang, T.-J., Hsu, P.-S., Wu, M., & Chuang, C.-P. (2008) · International Symposium on Knowledge Acquisition and Modeling (KAM) · doi:10.1109/kam.2008.59 · Cited by 2
To use or not to use, that is the question: On students’ encounters with a library of examples
Greensfeld, H., & Nevo, E. (2017) · Cogent Social Sciences · doi:10.1080/23311886.2017.1323381 · Cited by 2
Cognitive Readiness for Solving Equations
Chung, G. K. W. K., & Delacruz, G. C. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7579-8_7 · Cited by 2
Impact of alternative designs of multimedia elaboration on learning outcomes: Towards personalized learning of Software Engineering
Chikasha, S., Petegem, W. V., & Valcke, M. (2022) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon52537.2022.9766676 · Cited by 3
Problemorientiertes Online-Lernen im Biologieunterricht: Fähigkeitsselbstkonzept, mentale Anstrengung und Vorwissen als Prädiktoren für Wissenserwerbsprozesse zwischen Instruktion und Konstruktion
Zumbach, J., Moser, S., Unterbruner, U., & Pfligersdorffer, G. (2014) · Zeitschrift für Didaktik der Naturwissenschaften · doi:10.1007/s40573-014-0004-6 · Cited by 2
A Dynamic, Real Time and Non-interference Quantitative Measurement Technique on Diagnosing Instructional Condition for Adaptive E-learning
Hsu, P.-S., Chang, T.-J., Chuang, C.-P., & Wu, M. (2008) · IEEE Asia-Pacific Services Computing Conference (APSCC) · doi:10.1109/apscc.2008.108 · Cited by 2
When Less is More in Cognitive Diagnosis
Kalyuga, S. (2005) · eScholarship (California Digital Library) · Cited by 2
Self-citation
An exploration of concise redundancy in online multimedia learning
Wu, Y.-F. (2011) · Scholarship & Creative Works - Digital UNC a service of University Libraries (University of Northern Colorado) · Cited by 2
The Effectiveness Of Comparing Correct and Incorrect Examples for Learning About Decimal Magnitude
Durkin, K. L. (2009) · Cited by 2
The Effects of Animations and Multimedia Messages on Public Engagement in Precision Medicine: Assessment of Moderation and Mediation
Peng, W., Occa, A., & Morgan, S. E. (2024) · Journal of Health Communication · doi:10.1080/10810730.2024.2313994 · Cited by 1
Basics of Cognitive Load Theory (CLT)
Kalyuga, S., & Plass, J. L. (2025) · Oxford University Press eBooks · doi:10.1093/9780190078539.003.0002 · Cited by 1
Self-citation
Modifications Made to CLT to Date
Kalyuga, S., & Plass, J. L. (2025) · Oxford University Press eBooks · doi:10.1093/9780190078539.003.0003 · Cited by 1
Self-citation
Effects of task complexity and pre-training on problem solving: A cognitive load perspective
Zeitlhofer, I., Zumbach, J., & Schwartz, N. (2026) · European Journal of Psychology of Education · doi:10.1007/s10212-026-01176-0 · Cited by 1
Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique
Hsu, P.-S., & Chang, T.-J. (2011) · International Journal of Distance Education Technologies · doi:10.4018/jdet.2011070103 · Cited by 1
The Effects of Feedback During Exploration Depend on Prior Knowledge.
Fyfe, E. R., & Rittle‐Johnson, B. (2012) · eScholarship (California Digital Library) · Cited by 1
Effect of a Representative Sample of Internally Calibrated Mental Effort and Polytomously Scored Data on Representing Cognitive Efficiency
Nedungadi, S., Michels, O. R., Kreke, P. J., Raker, J. R., & Murphy, K. L. (2022) · Journal of Chemical Education · doi:10.1021/acs.jchemed.1c00973 · Cited by 1
On the Cognitive Load of Online Learners With Multi-Level Data Mining
Liu, L., Zhao, B., & Rao, Y. (2022) · International Journal of Information and Communication Technology Education · doi:10.4018/ijicte.314225 · Cited by 1
El tratamiento del suicidio de deportistas de artes marciales en los medios digitales (The treatment of suicide of martial arts athletes in digital media)
Camilo, J. A. D. O., Moraes, R. D. D. M. D., do NASCIMENTO, J. S., & Galán, C. H. (2023) · Retos · doi:10.47197/retos.v49.98425 · Cited by 1
An Online Learning and Problem Solving (OLPS) EEG Database for Mental Workload Assessment and Its Initial Benchmark Classification Performance
Sadhukhan, M. K., Ahirwal, M. K., & Saritha, S. K. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-3526-6_21 · Cited by 1
Introduction
Kalyuga, S., & Plass, J. L. (2025) · Oxford University Press eBooks · doi:10.1093/9780190078539.003.0001 · Cited by 2
Self-citation
Knowledge and Working Memory Effects on Learning from Visual Displays
Kalyuga, S. (2013) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-62396-235-720251007 · Cited by 1
Self-citation
Fuzzy Models for Implementation of the Decision-Making Module in Networked Didactic Prototypes
Méndez, B. E. P., González-Calleros, J. M., García, C. A. R., & Guerrero-García, J. (2020) · Computación y Sistemas · doi:10.13053/cys-24-2-3378 · Cited by 1
Basic Principles of Multimedia Learning
Mayer, R. E., & Fiorella, L. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108894333.014 · Cited by 1
A final project submitted to the Faculty of Claremont Graduate University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology
Saw, A. T. (2011) · Cited by 1
Imagination Effect
Ginns, P. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_140 · Cited by 1
Worked examples : effect of modified and conventional worked examples on assembly time, accuracy, and efficiency in a manufacturing environment
Dill, J. B. (2006) · USF Scholarship Repository (University of San Francisco)
RAPID DYNAMIC ASSESSMENT OF EXPERTISE: A COMPARISON OF PERFORMANCE AND MENTAL EFFICIENCY MEASURES IN ACCORDANCE WITH COGNITIVE LOAD THEORY
Johnson, D. L. (2007) · University of Minnesota Digital Conservancy (University of Minnesota)
Can You Teach in a Normal Way? Examining Chinese and US Curricula's Approach to Teaching Fraction Divisions
Feil, Y. C. (2010) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Utilizing Learning Promotion Potential (LPP) framework to facilitate the application of higher-order cognitive skills
Odiemo, L. O. (2017) · Electronic Journal of Research in Educational Psychology · doi:10.25115/ejrep.v7i19.1334
The Cerebral Hemodynamics of Cognitive Load: Learning Anatomy with Static and Dynamic Digital Images.
Loftus, J. J. (2014) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/28026
Academic Performance and Engagement of Educational Opportunity Program Students in a College Algebra Extended Class at the State University of New York, New Platz
Pindling, C. (2006) · doi:10.32597/dissertations/636/
An investigation into the effects of presentation modality and visual representation on recall and cognitive load: learning aspects of electroencephalogram (EEG) net application
Barber, L. (2011) · e-space (Manchester Metropolitan University)
Evaluating the benefits of worked examples in a constraint-based tutor.
Najar, A. S. (2014) · UC Research Repository (University of Canterbury) · doi:10.26021/2261
Learnable Interfaces--Leveraging Navigation by Design.
Swanson, K. (2012) · ResearchWorks at the University of Washington (University of Washington)
Training For Decision Making In Complex Environments: Instructional Methods And Individual Differences
Ray, J. M. (2013) · Journal of International Crisis and Risk Communication Research
A Path Analysis of Expertise and Cognitive Load Factors on Performance
RyuJeeheon (2009) · 교과교육학연구 · doi:10.24231/rici.2009.13.4.851
Use Of Graphics In Multimedia Instructional Materials: Research Based Design Guidelines
Wiebe, E. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--235 · Cited by 1
Students Facing Examples: Making the Unseen Visible Learning in an Example-Rich Environment (ERE) - An Emotional Perspective
Greensfeld, H., & Nevo, E. (2017) · Athens Journal of Education · doi:10.30958/aje.4-1-2
The Effect of Pedagogical Agent's Nonverbal Communications on Affective Perception and Cognitive Efficiency
RyuJeeheon (2014) · The Korean Journal of Educational Methodology Studies · doi:10.17927/tkjems.2014.26.1.115
An exploration into the effects of L1 explanatory notes on L2 oral production: liberation of the mind or shackles of dependency?
Carter, K. (2012) · Mspace (University of Manitoba)
Adaptive Facilitation Skills for Army Instructors
Toumbeva, T. H., Ratwani, K. L., Miller, L. C., Diedrich, F. J., Flanagan, S., Murphy, J. O., & Oster, E. (2018)
Playing to Win: Applying Cognitive Theory and Gamification to Augmented Reality for Enhanced Mathematical Outcomes in Underrepresented Student Populations
Brown, T. M. (2018) · VTechWorks (Virginia Tech)

Authors: Kalyuga, S., & Sweller, J.
Affiliations: UNSW Sydney (AU)
ORCID: Kalyuga, Slava
Sweller, John
Venue: Journal of Educational Psychology
Publisher: American Psychological Association (APA)
Published: 2004-09
Volume: 96
Issue: 3
Pages: 558-568
ISSN: 0022-0663 (ISSN-L)
1939-2176
DOI: 10.1037/0022-0663.96.3.558
OpenAlex: W2078428840
Cited by: 219
References: 68
Citations by year: 2026: 5 · 2025: 15 · 2024: 5 · 2023: 6 · 2022: 7 · 2021: 9 · 2020: 6 · 2019: 4 · 2018: 9 · 2017: 12 · 2016: 7 · 2015: 7 · 2014: 17 · 2013: 12 · 2012: 18
FWCI: 12.81
Type: journal article
Language: en
Open access: green
handle.unsw.edu.au
Licence: cc-by-nc-nd
Locations: Journal of Educational Psychology (publishedVersion)
UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Keywords: expertise reversal effect, cognitive load, instructional design, algebra instruction, geometry teaching, high school students

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Measuring Knowledge to Optimize Cognitive Load Factors During Instruction.

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks