Measuring Cognitive Load: Are There More Valid Alternatives to Likert Rating Scales?
Open accessAbstract
Cognitive load researchers have used varying subjective techniques based on rating scales to quantify experienced cognitive load. Although it is generally assumed that subjects can introspect on their cognitive processes and have no difficulty in assigning numerical values to the imposed cognitive load, little is known about how visual characteristics of the rating scales influence the validity of the cognitive load measure. In this study we look at validity of four subjective rating scales (within groups) differing in visual appearance by participants rating perceived difficulty and invested mental effort in response to working on simple and complex weekday problems. We used two numerical scales (the nine-point Likert scale most often used in Cognitive load theory research and a Visual Analogue Scale ranging between 0–100%) and two pictorial scales (a scale consisting of emoticons ranging from a relaxed blue-colored face to a stressed red-colored face and an “embodied” scale picturing nine depicted weights from 1–9 kg). Results suggest that numerical scales better reflect cognitive processes underlying complex problem solving while pictorial scales Underlying simple problem solving. This study adds to the discussion on the challenges to quantify cognitive load through various measurement methods and whether subtleties in measurements could influence research findings.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Numerical scales are the common way to measure constructs like cognitive load.
“Such numerical scales are the common way to measure constructs like cognitive load (Ouwehand et al., 2021).”
From Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load (2023) · cites this paper as Ouwehand et al., 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,588 · see citing works
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615 · see citing works
The episodic buffer: a new component of working memory?
Baddeley, A. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01538-2 · Cited by 7,359 · cited 1× · see citing works
“…rating scales, visualization, problem solving INTRODUCTION Cognitive load theory (CLT) centralizes the characteristics of human cognitive architecture, and especially the limitations of working memory in time and capacity (Baddeley, 1992, 2000), as a prerequisite for the optimization of learning.”
BackgroundCognitive 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 · see citing works
Self-citationBarsalou, L. W. (2008) · Annual Review of Psychology · doi:10.1146/annurev.psych.59.103006.093639 · Cited by 5,936 · cited 7× · see citing works
“Since the nineties a lot of evidence has been gathered, suggesting that for a substantial part, our cognition is tightly bound to how we perceive and interact in the world (Barsalou, 2008).”
BackgroundInfluentialBaddeley, A. (1992) · Science · doi:10.1126/science.1736359 · Cited by 5,258 · see citing works
Cognitive Load Measurement as a Means to Advance Cognitive Load Theory
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_8 · Cited by 2,527 · see citing works
Self-citationCognitive Architecture and Instructional Design: 20 Years Later
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019) · Educational Psychology Review · doi:10.1007/s10648-019-09465-5 · Cited by 2,337 · see citing works
Self-citationCognitive Load Theory and Complex Learning: Recent Developments and Future Directions
van Merriënboer, J. J. G., & Sweller, J. (2005) · Educational Psychology Review · doi:10.1007/s10648-005-3951-0 · Cited by 2,206 · see citing works
Paas, F. G. W. C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.4.429 · Cited by 2,150 · see citing works
Self-citationElement Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load
Sweller, J. (2010) · Educational Psychology Review · doi:10.1007/s10648-010-9128-5 · Cited by 2,020 · see citing works
cocor: A Comprehensive Solution for the Statistical Comparison of Correlations
Diedenhofen, B., & Musch, J. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121945 · Cited by 1,919 · see citing works
Coding qualitative data: a synthesis guiding the novice
Linneberg, M. S., & Korsgaard, S. (2019) · Qualitative Research Journal · doi:10.1108/qrj-12-2018-0012 · Cited by 1,170 · see citing works
Toward a rational and mechanistic account of mental effort
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031526 · Cited by 1,077 · see citing works
Alghadir, A., Anwer, S., Iqbal, A., & Iqbal, Z. (2018) · Journal of Pain Research · doi:10.2147/jpr.s158847 · Cited by 783 · see citing works
Skinner, B. F. (1963) · American Psychologist · doi:10.1037/h0045185 · Cited by 675 · see citing works
DeLeeuw, K. E., & Mayer, R. E. (2008) · Journal of Educational Psychology · doi:10.1037/0022-0663.100.1.223 · Cited by 558 · see citing works
Self-Assessment of Knowledge: A Cognitive Learning or Affective Measure?
Sitzmann, T., Ely, K., Brown, K. G., & Bauer, K. N. (2010) · Academy of Management Learning and Education · doi:10.5465/amle.9.2.zqr169 · Cited by 514 · see citing works
Learning, reward, and decision making
O'Doherty, J. P., Cockburn, J., & Pauli, W. M. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-010416-044216 · Cited by 475 · see citing works
Staddon, J. E. R., & Cerutti, D. T. (2002) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145124 · Cited by 384 · see citing works
Using subjective measures to detect variations of intrinsic cognitive load within problems
Ayres, P. (2006) · Learning and Instruction · doi:10.1016/j.learninstruc.2006.09.001 · Cited by 381 · see citing works
Sung, Y., & Wu, J.-S. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1041-8 · Cited by 375 · see citing works
On staying grounded and avoiding quixotic dead ends
Barsalou, L. W. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1028-3 · Cited by 331 · see citing works
Embodied Cognition and Mirror Neurons: A Critical Assessment
Caramazza, A., Anzellotti, S., Strnad, L., & Lingnau, A. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-013950 · Cited by 247 · see citing works
Artificial Intelligence in Cyber Physical Systems
Radanliev, P., Roure, D. D., Nurse, J. R. C., Nicolescu, R., Huth, M., Cannady, S., & Montalvo, R. M. (2020) · AI & Society · doi:10.1007/s00146-020-01049-0 · Cited by 218 · see citing works
Mighty metaphors: Behavioral and ERP evidence that power shifts attention on a vertical dimension
Zanolie, K., van Dantzig, S., Boot, I., Wijnen, J. G., Schubert, T. W., Giessner, S. R., & Pecher, D. (2011) · Brain and Cognition · doi:10.1016/j.bandc.2011.10.006 · Cited by 210 · see citing works
Schmeck, A., Opfermann, M., van Gog, T., PAAS, F. G. W. C., & Leutner, D. (2014) · Instructional Science · doi:10.1007/s11251-014-9328-3 · Cited by 207 · see citing works
Self-citationSweller, J., Ayres, P., & Kalyuga, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8126-4_6 · Cited by 203 · see citing works
Reliability and validity of the faces and word descriptor scales to measure procedural pain
Keck, J. F., Gerkensmeyer, J. E., Joyce, B., & Schade, J. G. (1996) · Journal of Pediatric Nursing · doi:10.1016/s0882-5963(96)80081-9 · Cited by 190 · see citing works
Some cognitive processes and their consequences for the organisation and presentation of information
Sweller, J. (1993) · Australian Journal of Psychology · doi:10.1080/00049539308259112 · Cited by 157 · see citing works
Development and Validation of a Pictorial Nausea Rating Scale for Children
Baxter, A. L., Watcha, M. F., Baxter, W., Leong, T. L., & Wyatt, M. M. (2011) · PEDIATRICS · doi:10.1542/peds.2010-1410 · Cited by 151 · see citing works
Timing and Frequency of Mental Effort Measurement: Evidence in Favour of Repeated Measures
van Gog, T., Kirschner, F., Kester, L., & Paas, F. (2012) · Applied Cognitive Psychology · doi:10.1002/acp.2883 · Cited by 130 · see citing works
Self-citationValidity of Cognitive Load Measures in Simulation-Based Training
Naismith, L. M., & Cavalcanti, R. B. (2015) · Academic Medicine · doi:10.1097/acm.0000000000000893 · Cited by 106 · see citing works
Zheng, R. Z. (2018) · Cited by 106 · see citing works
Working Memory, Executive Control, and Aging
Braver, T. S., & West, R. L. (2015) · Routledge eBooks · doi:10.4324/9780203837665.ch7 · Cited by 80 · see citing works
Rhodes, M. G. (2015) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199336746.013.4 · Cited by 55 · see citing works
Validity Evidence for the Child Occupational Self Assessment
Kramer, J. M., Kielhofner, G., & Smith, E. V. (2010) · American Journal of Occupational Therapy · doi:10.5014/ajot.2010.08142 · Cited by 54 · see citing works
Should self-regulated learning be integrated with cognitive load theory? A commentary
Sweller, J., & Paas, F. (2017) · Learning and Instruction · doi:10.1016/j.learninstruc.2017.05.005 · Cited by 54 · see citing works
Self-citationA Review of B. F. Skinner’s ‘Reinforcement Theory of Motivation’
Gordan, M. (2014) · INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION METHODOLOGY · doi:10.24297/ijrem.v5i3.3892 · Cited by 49 · see citing works
Judgments of learning: Methods, data, and theory.
Rhodes, M. G. (2016) · Cited by 46 · see citing works
Assessing the validity of the cognitive load scale in a problem-based learning setting
Hadie, S. N. H., & Yusoff, M. S. B. (2016) · Journal of Taibah University Medical Sciences · doi:10.1016/j.jtumed.2016.04.001 · Cited by 44 · see citing works
Effects of Gestures on Older Adults' Learning from Video‐based Models
Ouwehand, K., van Gog, T., & PAAS, F. G. W. C. (2014) · Applied Cognitive Psychology · doi:10.1002/acp.3097 · Cited by 36 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Survey on Measuring Cognitive Workload in Human-Computer Interaction
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304 · see citing works
“A careful evaluation of the actual measurement is inevitable, as external stressors, e.g., the social-evaluative threat of performing well in a study, may impact biomarkers [49].”
Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
Rong, Y., Leemann, T., Nguyen, T.-T., Fiedler, L., Qian, P., Unhelkar, V., Seidel, T., Kasneci, G., & Kasneci, E. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331846 · Cited by 278 · see citing works
Krieglstein, F., Beege, M., Rey, G. D., Sanchez-Stockhammer, C., & Schneider, S. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09738-0 · Cited by 139 · see citing works
Krieglstein, F., Beege, M., Rey, G. D., Ginns, P., Krell, M., & Schneider, S. (2022) · Educational Psychology Review · doi:10.1007/s10648-022-09683-4 · Cited by 125 · see citing works
Creative cognition: A multidisciplinary and integrative framework of creative thinking
Pinkow, F. (2022) · Creativity and Innovation Management · doi:10.1111/caim.12541 · Cited by 52 · see citing works
Guidelines for Choosing Cognitive Load Measures in Perceptually Rich Environments
Skulmowski, A. (2022) · Mind Brain and Education · doi:10.1111/mbe.12342 · Cited by 43 · see citing works
Comparing Likert and visual analogue scales in ecological momentary assessment
Haslbeck, J. M. B., Martínez, A. J., Roefs, A., Fried, E. I., Lemmens, L. H. J. M., Groot, E. L., & Edelsbrunner, P. A. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02706-2 · Cited by 31 · see citing works
Zhang, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0314088 · Cited by 28 · see citing works
Maeneja, R., Silva, C. R., Ferreira, I. S., & Abreu, A. M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1258262 · Cited by 29 · see citing works
Ibrahim, R. K., Marar, Y. A. A., Salman, M., Jehad, S., Hamza, M. G., Abouelnasr, A. S., Abdelaliem, S. M. F., Alahmedi, S. H., & Hendy, A. S. (2025) · BMC Nursing · doi:10.1186/s12912-025-03655-z · Cited by 24 · see citing works
In-Ear SpO₂ for Classification of Cognitive Workload
Davies, H. J., Williams, I., Hammour, G., Yarici, M. C., Stacey, M. J., Seemungal, B. M., & Mandic, D. P. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3196841 · Cited by 24 · see citing works
A Synthetic Review of Cognitive Load in Distance Interpreting: Toward an Explanatory Model
Zhu, X., & Aryadoust, V. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.899718 · Cited by 32 · see citing works
Safari, M., Shalbaf, R., Bagherzadeh, S., & Shalbaf, A. (2024) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2024.2386325 · Cited by 19 · see citing works
Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments
Anders, C., Moontaha, S., Real, S., & Bert Arnrich, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03738-7 · Cited by 20 · see citing works
Measuring cognitive load in multitasking using mobile fNIRS
Boere, K., Anderson, F., Hecker, K. G., & Krigolson, O. E. (2024) · Neuroimage Reports · doi:10.1016/j.ynirp.2024.100228 · Cited by 15 · see citing works
A continuum from predictive to online feedback in visuomotor interception
Márquez, I., Lemus, L., & Treviño, M. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16628 · Cited by 15 · see citing works
Liu, A., Duffy, M. C., Tse, S., Zucker, M., McMillan, H. J., Weldon, P., Quet, J., & Long, M. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2189540 · Cited by 15 · see citing works
Manz, S., Schmalz, T., Ernst, M., Köhler, T. M., Gonzalez-Vargas, J. E., & Dosen, S. S. (2024) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2024.106250 · Cited by 13 · see citing works
Video Tutorials in the Traditional Classroom: The Effects on Different Types of Cognitive Load
Fan, E., Bower, M., & Siemon, J. (2024) · Technology Knowledge and Learning · doi:10.1007/s10758-024-09754-1 · Cited by 13 · see citing works
Schnaubert, L., & Schneider, S. (2022) · Frontiers in Education · doi:10.3389/feduc.2021.648319 · Cited by 14 · see citing works
Guo, J., Zhao, Y., Zhang, W., Lu, K., Feng, X., & Xia, T. (2025) · Behavioral Sciences · doi:10.3390/bs15050673 · Cited by 14 · see citing works
Mahfoudh, H. B., & Zoudji, B. (2022) · Perceptual and Motor Skills · doi:10.1177/00315125221076448 · Cited by 11 · see citing works
McMinn, D., Grant, T., DeFord-Watts, L., Porkess, V., Lens, M., Rapier, C., Joe, W., Becker, T. A., & Bender, W. R. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf023 · Cited by 10 · see citing works
Dodd, N., Cohn, M., & Zellou, G. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1204211 · Cited by 9 · see citing works
Pollock, K., MacKay, J. R. D., Hearns, S., Morton, C. M., & Pollock, P. J. (2024) · Simulation in Healthcare The Journal of the Society for Simulation in Healthcare · doi:10.1097/sih.0000000000000771 · Cited by 8 · see citing works
Letteri, I., & Vittorini, P. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00492-8 · Cited by 9 · see citing works
Brain functional training: a perspective article
Silva‐Grigoletto, M. E. D., Monteiro, M. R. P., Aragão-Santos, J. C., Vasconcelos, A. B. S., Marcos-Pardo, P. J., & Fortes, L. D. S. (2024) · Frontiers in Aging · doi:10.3389/fragi.2024.1368878 · Cited by 7 · see citing works
Procedural Learning in Mixed Reality: Assessing Cognitive Load and Performance
Mugisha, G., & Arguel, A. (2025) · Education Sciences · doi:10.3390/educsci15030339 · Cited by 6 · see citing works
Balancing teacher-led and student-led learning in science: the importance of cognitive load
Cairns, D. (2025) · Large-scale Assessments in Education · doi:10.1186/s40536-025-00263-w · Cited by 10 · see citing works
Ortega, E. M., Castéra, J., Cheneval-Armand, H., & Brandt‐Pomares, P. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1223656 · Cited by 5 · see citing works
Auditory Temporal Hints in AR Piano Tutoring
Lange-Nawka, D. C., Wünsche, B. C., & Thompson, S. E. R. (2023) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz61134.2023.10343724 · Cited by 5 · see citing works
Embodied Measurement: Tangible Interactions to Enhance the Validity of Self-Report Measures
Uhl, J. C., Regal, G., Koesten, L. M., Oppermann, M., Murtinger, M., & Tscheligi, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714055 · Cited by 4 · see citing works
Fan, E., Bower, M., & Siemon, J. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105487 · Cited by 3 · see citing works
Franchine, M. E., Zimerer, C., Rohor, P. E. M., Loureiro, M., Machado, F., de Mello, R. C., & Frizera, A. (2025) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2025.04.011 · Cited by 4 · see citing works
Comparing Visualizations to Help a Teacher Effectively Monitor Students in a VR Classroom
Rahman, Y., Kulshreshth, A. K., & Borst, C. W. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00059 · Cited by 4 · see citing works
Visualizing the Black-Box: Likert Scales and the Creation of Theory-Method-Case Packages
Bargheer, S. (2025) · The American Sociologist · doi:10.1007/s12108-024-09641-3 · Cited by 4 · see citing works
Geberkidan, E. W., Michael, K., & Abraham, S. T. (2024) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2024.2392854 · Cited by 3 · see citing works
CALM: Multimodal Cognitive Load Assessment Framework via Engineered and Explainable Features
Myrick, C., Ghosh, I., Jayarajah, K., & Roy, N. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00067 · Cited by 3 · see citing works
Wang, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1618121 · Cited by 6 · see citing works
Miró, À., Alier, M., Chen, O., Serrano, J. M., & Castro-Alonso, J. C. (2025) · Journal of Curriculum Studies · doi:10.1080/00220272.2025.2585442 · Cited by 3 · see citing works
Verma, B., & Chatterji, N. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-1107-3.ch004 · Cited by 3 · see citing works
Cognitive load scale in learning formal definition of limit: A rasch model approach
Oktaviyanthi, R., Agus, R. N., Garcia, M., & Lertdechapat, K. (2024) · Infinity Journal · doi:10.22460/infinity.v13i1.p99-118 · Cited by 6 · see citing works
A Comparative Study of Short-Term Social Media Use with Face-to-Face Interaction in Adolescence
Mendonça, I., Coelho, F., Rando, B., & Abreu, A. M. (2025) · Children · doi:10.3390/children12040460 · Cited by 7 · see citing works
Evaluating a novel clinical reasoning tool for physical therapy students: A pilot study
Kargela, M., Woodhouse, L. J., Durning, S. J., & Jensen, G. M. (2024) · The Clinical Teacher · doi:10.1111/tct.13843 · Cited by 2 · see citing works
A working memory reversal effect on design principles? Evidence on pauses in video among children
Pinelli, M., & Cojean, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13545-1 · Cited by 2 · see citing works
Rohor, P. E. M., Frizera, A., Zimerer, C., Franchine, M. E., Loureiro, M., Machado, F. S. V., de Mello, R. C., & Carletti, L. (2025) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2025.08.028 · Cited by 2 · see citing works
Wyssenbach, T., Kaufmann, K., & Schwaninger, A. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2546054 · Cited by 2 · see citing works
Liao, H.-H., Liu, T., & Lin, Y.-C. (2026) · Instructional Science · doi:10.1007/s11251-026-09794-3 · Cited by 2 · see citing works
Hyland, T., Seery, N., & Buckley, J. (2024) · European Journal of Engineering Education · doi:10.1080/03043797.2024.2405860 · Cited by 2 · see citing works
Lu, Y. (2025) · International Journal of Education and Social Development · doi:10.54097/s8r0xj17 · Cited by 2 · see citing works
Playing for a Healthy Life: Integrating Mobile Exergames in Physical Education
Orgaz, P. S., Baeza, M. A., & Navia, J. A. (2025) · Behavioral Sciences · doi:10.3390/bs15020229 · Cited by 2 · see citing works
CoAffinity: A Multimodal Dataset for Cognitive Load and Affect Assessment in Remote Collaboration
Gunasekaran, T. S., Gupta, K., Pai, Y. S., Bai, H., & Billinghurst, M. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3562559 · Cited by 2 · see citing works
Using Culture for Customers Expectations Analysis in a Luxury Hotel
Sidorčuka, I., & Kurusina, A. (2025) · Eurasian studies in business and economics · doi:10.1007/978-3-031-84319-8_8 · Cited by 1 · see citing works
Martín‐Fuentes, I., Fernandez‐Gamez, B., Vidal‐Almela, S., Caro‐Rus, A., Solis‐Urra, P., Sánchez‐Aranda, L., Fernández‐Ortega, J., Sanchez‐Martinez, J., Coca‐Pulido, A., Olvera‐Rojas, M., et al. (2025) · Brain and Behavior · doi:10.1002/brb3.70636 · Cited by 1 · see citing works
Kelley, B., McMahan, R. P., Wickens, C. D., Clegg, B. A., & Ortega, F. R. (2025) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3756884.3765992 · Cited by 1 · see citing works
A new framework about exercise, functionality and health: Brain Functional Training
Monteiro, M. R. P., Fortes, L. D. S., Santo, M. D., & Silva‐Grigoletto, M. E. D. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106685 · Cited by 1 · see citing works
Yang, E., Ryu, J., Choi, H., & Kim, I. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1805065 · Cited by 1 · see citing works
Enhancing digital reading for dyslexic learners: an AI-based approach to cognitive load reduction
Amjad, A., Amirhosseini, M. H., & Kalabi, F. (2026) · Journal of Enabling Technologies · doi:10.1108/jet-08-2025-0060 · Cited by 1 · see citing works
Colliot, T., & Amadieu, F. (2026) · Discourse Processes · doi:10.1080/0163853x.2026.2678175 · Cited by 1 · see citing works
Hedrick, M. S., Braumuller, C., Gunning, S., Faulkner, K. F., & Peborgh, P. V. (2023) · Building Acoustics · doi:10.1177/1351010x231217139 · Cited by 1 · see citing works
Concurrent predictive and prospective strategies in a simple visuomotor taskPreprint
Márquez, I., & Treviño, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.15.594355 · Cited by 1 · see citing works
Moontaha, S., Cavalier, C., Esser, B., Jordan, A., Goebel, I., Anders, C., Mimi, A., Krüger, B., Surges, R., & Bert Arnrich, A. (2025) · Scientific Data · doi:10.1038/s41597-025-06328-3 · Cited by 1 · see citing works
Classifying Cognitive Load in Digital Textbook Reading Using Electroencephalogram Signals
Wittke, L., Hachem, P. E., Chen, Y., Ollier, J., Cheng, B., Gashi, S., & Zhang, X. (2025) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet66371.2025.11045966
Secondary Mathematics Complexity Decomposed and Fitted Through Five Reliable Variables
Miró, À., Alier, M., Serrano, J. M., Benavides, A., & Catasús, P. (2025) · Lecture notes in educational technology · doi:10.1007/978-981-96-5658-5_93
Modeling Intrinsic Load: Can Item Difficulty Be a Reasonable Proxy?
Gorgun, G., Cai, M., & Epp, C. D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98462-4_33
Moreno-Arevalo, B. S., Zimerer, C., Salamanca, M. J., de Mello, R. C., & Frizera, A. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-93646-3_9
Surwanti, D., Loyens, S. M. M., Burke, M., Hikmah, I., Gemilang, A. V., & Rikers, R. M. J. P. (2025) · Education Sciences · doi:10.3390/educsci15101392 · Cited by 2 · see citing works
Improving post-pandemic online learning experiences for minority students: An exploratory study
Cramer, E. M., Kwitonda, J. C., Sun, W., & Hazzazi, N. (2025) · Learning Environments Research · doi:10.1007/s10984-025-09562-z
Asyhari, A., & Islamia, I. (2025) · Tadris Jurnal Keguruan dan Ilmu Tarbiyah · doi:10.24042/tadris.v10i1.24313
CS Teaching Assistant Perceptions on LLM-Generated Faded Worked Examples for Feedback Training
Gonzaga, J. T., Vassar, A., & Jiang, Y. (2026) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3770762.3772582
Peer aesthetic and social perceptions of anterior open bite in children
Faria, E., de Freitas, K. M. S., Cotrin, P., Valarelli, F. P., Gurgel, J. D. A., & Pinzan, C. R. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-43275-4
Turkish adaptation and psychometric evaluation of cognitive load scale for middle school students
Özcan, Z. Ç., & Öner, A. T. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106583 · Cited by 1 · see citing works
Smith, C., Rafferty, K., Sharma, V., & Rainey, E. (2026) · SN Computer Science · doi:10.1007/s42979-026-04894-w
da Fonseca, G. D. R. B. C., Tipoe, G. L., & Ganotice, F. A. (2026) · Anatomical Sciences Education · doi:10.1002/ase.70267
Scholl, D., & Küth, S. (2026) · Teaching and Teacher Education · doi:10.1016/j.tate.2026.105600
Sun, X., Wei, S., Pan, T., Wang, Y., Bosch, J. A., Echizen, I., Ali, A. E., & Sugawara, S. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3806190
Hybrid Hands-On and Simulation-Based Learning to Teach Velocity and Acceleration in Year 9 STEMPreprint
Lam, B., & Collins, J. (2026) · Research Square · doi:10.21203/rs.3.rs-9668054/v1
The subtlety of impossible intersections during virtual locomotion
Powell, P., Mell, J., & McMahan, R. P. (2026) · Frontiers in Virtual Reality · doi:10.3389/frvir.2026.1754369
Rais, M. C., Duymaz, R., Rähder, C., Pointinger, F., Kühnlenz, B., & Kühnlenz, K. (2026) · at - Automatisierungstechnik · doi:10.1515/auto-2025-0120
Wąsik, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29656-6_11
Comparative usability analysis of mixed-method teaching interfaces in educational settings
Rahman, Y., & Kulshreshth, A. K. (2026) · Frontiers in Virtual Reality · doi:10.3389/frvir.2026.1907167
Pădurean, V.-A., Riegel, K., Barbre, G., Blake, M., Denny, P. C., Gotovos, A., Leinonen, J., MacNeil, S., Prather, J. E., & Singla, A. K. (2026) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3765964.3811639
EEG Components as an Implicit Channel for Perceptual Similarity Evaluation
Mitrevska, T., Chen, B., & Mayer, S. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3831398
Zhu, Z., Zhu, C., Zhang, X., Shi, M., Tang, T., & Yao, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2718629
Exploring Cognitive Load Markers in Construction Fatality Reports: An LLM-Based Approach
Adjei, E. K., Jin, Z.-Y., & Gambatese, J. A. (2026) · Construction Research Congress 2026 · doi:10.1061/9780784486993.067
Measuring cognitive load using non-invasive techniques: a systematic review
Gittins, E., Sahoo, D., Jenkinson-Garner, M., & Ahmad, M. I. (2026) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-026-00482-7
Kim, D., & Ryoo, D. (2026) · Technology Pedagogy and Education · doi:10.1080/1475939x.2026.2745998
Opoku, K., & Domfe, G. (2026) · Journal of Social and Economic Development · doi:10.1007/s40847-026-00519-3
- Authors
- Ouwehand, K., Kroef, A. V. D., Wong, J., & Paas, F.
- Affiliations
- Erasmus University Rotterdam (NL)
- Delft University of Technology (NL)
- Corresponding author
- Ouwehand, Kim
- Venue
- Frontiers in Education
- Publisher
- Frontiers Media SA
- Published
- 2021-09-20
- Volume
- 6
- ISSN
- 2504-284X (ISSN-L)
- OpenAlex
- W3199729108
- Cited by
- 90
- References
- 41
- Citations by year
- 2026: 24 · 2025: 32 · 2024: 15 · 2023: 11 · 2022: 8
- FWCI
- 8.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Education (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Erasmus Universiteit Rotterdam
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- cognitive load, subjective rating scales, Likert scale, visual analogue scale, pictorial scale, scale validity, perceived difficulty, cognitive effort, problem complexity, cognitive load assessment
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
