Development of an instrument for measuring different types of cognitive load
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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).
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If the sum of extraneous, intrinsic, and germane load exceeds the student's available resources, cognitive overload occurs, hindering the processing of new information.
“Thus, if the sum of these factors exceeds the student's available resources, cognitive overload occurs, hindering the processing of new information (Leppink et al., 2013).”
From Systematic Review of the Effectiveness of Neurodidactic Spaced Learning Strategies in Long-Term Memory (2026) · cites this paper as Leppink et al., 2013
The paper’s Abstract identifies intrinsic, extraneous, and germane load and says extraneous load should be minimized to allow learning-related activity. It does not state that the sum of the three loads crossing a resource threshold causes cognitive overload or hinders processing of new information. (LLM, conf=0.84)
Experimental — accuracy not yet measuredLeppink et al. found the three factors of their cognitive load questionnaire to be robust even when items were adopted to fit different domains.
“Even if they had to adopt the items to fit the different domains, they found the three factors to be robust.”
From Development and Validation of Two Instruments Measuring Intrinsic, Extraneous, and Germane Cognitive Load (2017) · cites this paper as Leppink et al., 2013
The Leppink et al. (2013) questionnaire was not tested in separate experiments to verify whether it could explicitly differentiate between types of cognitive load.
“However, no separate experiments were performed to verify whether the questionnaire could explicitly differentiate between types of cognitive load, which would be an important criterion for assessing validity.”
From Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load (2023) · cites this paper as Leppink et al. (2013)
The GCL scale in Leppink et al. had problems, specifically a lack of correlation with learning performance.
“However, because of problems with the GCL scale (lack of correlation with learning performance), one GCL-related item was added in a further study to explicitly capture the effort required to cope with cognitive load.”
From Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load (2023) · cites this paper as Leppink et al. (2013)
PCA is less stringent in terms of assumptions than EFA.
“a principal component analysis (PCA) was preferred over an exploratory factor analysis (EFA) as it is less stringent in terms of assumptions (e.g., Leppink et al., 2013).”
From Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load (2023) · cites this paper as Leppink et al., 2013
Previous cognitive load questionnaires relied on a three-factor model.
“Since the previous PCA resulted in a three-component solution and previous cognitive load questionnaires also relied on a three-factor model (Dönmez et al., 2022;Klepsch et al., 2017;Leppink et al., 2013), the a priori hypothesis explicitly assumed that the observed ratings of the items loaded on three latent variables (factors).”
From Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load (2023) · cites this paper as Leppink et al., 2013
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(2019) · Cambridge University Press eBooks · doi:10.1017/9781108654555 · Cited by 130 · see citing works
Johnson, A. M., Özoğul, G., & Martin Reisslein, B. (2014) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12078 · Cited by 125 · see citing works
Subgoals Help Students Solve Parsons Problems
Morrison, B. B., Margulieux, L. E., Ericson, B. J., & Guzdial, M. (2016) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2839509.2844617 · Cited by 126 · see citing works
“Intrinsic load is a combination of the innate difficulty of the material being learned as well as the learner's characteristics [15].”
BackgroundKrieglstein, 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
“Following the generally accepted definition of the ICL (e.g., Leppink et al., 2013), the domain-specific prior knowledge should correlate negatively with this cognitive load type.”
BackgroundMethodInfluentialUsing mobile devices to enhance inquiry-based learning processes
Becker, S., Klein, P., Gößling, A., & Kühn, J. (2020) · Learning and Instruction · doi:10.1016/j.learninstruc.2020.101350 · Cited by 148 · see citing works
Thrive or overload? The effect of task complexity on novices’ simulation‐based learning
Haji, F. A., Cheung, J. J. H., Woods, N. N., Regehr, G., de Ribaupierre, S., & Dubrowski, A. (2016) · Medical Education · doi:10.1111/medu.13086 · Cited by 124 · see citing works
Solving parsons problems versus fixing and writing code
Ericson, B. J., Margulieux, L. E., & Rick, J. (2017) · Proceedings of the 17th Koli Calling International Conference on Computing Education Research · doi:10.1145/3141880.3141895 · Cited by 121 · see citing works
Afify, M. K. (2020) · Turkish Online Journal of Distance Education · doi:10.17718/tojde.803360 · Cited by 118 · see citing works
“For building the scale, various previous studies which studied measuring the cognitive load have been explored (Josephsen, 2018; Kruger, & Doherty, 2016; Leppink, et. al., 2013, 2014; Paas. et. al.”
MethodMeasuring cognitive load in the presence of educational video: Towards a multimodal methodology
Kruger, J., & Doherty, S. (2016) · Australasian Journal of Educational Technology · doi:10.14742/ajet.3084 · Cited by 115 · see citing works
“Here we propose the use of an instrument based on the items developed and validated by Leppink et al. (2013; 2014) to distinguish between intrinsic and extraneous load while also looking at possible correlations between online psychophysiological measures of cognitive load (eye tracking and EEG)…”
MethodUsing cognitive theory to facilitate medical education
Qiao, Y. Q., Shen, J., Liang, X., Ding, S., Chen, F. Y., Shao, L., Zheng, Q., & Ran, Z. H. (2014) · BMC Medical Education · doi:10.1186/1472-6920-14-79 · Cited by 111 · see citing works
“[23].”
Orrú, G., & Longo, L. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-14273-5_3 · Cited by 128 · see citing works
“Multi-subjective rating scales of cognitive load types from [32] in the scale 0 to 10 (0 = not at all, 10 = completely).”
InfluentialSewell, J. L., Maggio, L. A., ten Cate, O., van Gog, T., Young, J. Q., & O’Sullivan, P. (2018) · Medical Teacher · doi:10.1080/0142159x.2018.1505034 · Cited by 110 · see citing works
“An ideal learning task is one in which intrinsic is matched to the level of the learner, germane load is optimized, and extraneous load is minimized (Leppink et al. 2013; Young and Sewell 2015).”
BackgroundMethodCognitive load theory: Practical implications and an important challenge
Leppink, J. (2017) · Journal of Taibah University Medical Sciences · doi:10.1016/j.jtumed.2017.05.003 · Cited by 120 · see citing works
“If the goal of an exercise is to have students learn how to address specific stimuli (e.g., phone ringing, colleagues passing by, patient files) in a workplace environment, cognitive load due to dealing with these stimuli is ICL.”
BackgroundInfluentialSelf-citationDinçer, S., & Doğanay, A. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.04.005 · Cited by 112 · see citing works
Measuring Cognitive Load in Embodied Learning Settings
Skulmowski, A., & Rey, G. D. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01191 · Cited by 108 · see citing works
“Recently, cognitive load surveys measuring the three distinct load types were presented (e.g., Eysink et al., 2009; Leppink et al., 2013).”
BackgroundMethodInfluentialAndersen, M. S., & Makransky, G. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12478 · Cited by 108 · see citing works
“Scale Leppink et al., 2013 Adapted version used in Study 1”
BackgroundMethodResultInfluentialValidity 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
Getting the point: Tracing worked examples enhances learning
Hu, F., Ginns, P., & Bobis, J. (2014) · Learning and Instruction · doi:10.1016/j.learninstruc.2014.10.002 · Cited by 106 · see citing works
Effects of interactivity in E-textbooks on 7th graders science learning and cognitive load
Weng, C., Otanga, S., Weng, A., & Cox, J. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.02.008 · Cited by 103 · see citing works
Ready student one: Exploring the predictors of student learning in virtual reality
Madden, J., Pandita, S., Schuldt, J. P., Kim, B., Won, A. S., & Holmes, N. G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229788 · Cited by 104 · see citing works
“We note, however, that many of the common methods have several limitations [87], such as that different methods may or may not be able to distinguish different types of cognitive load [28, 84, 87, 88].”
MethodLin, H. C.-S., Yu, S., Sun, J. C., & Jong, M. S. (2019) · Interactive Learning Environments · doi:10.1080/10494820.2019.1624579 · Cited by 101 · see citing works
“We adopted the 10-item cognitive load scale which had developed by Leppink, Paas, Van der Vleuten, Van Gog, and Van Merriënboer (2013).”
MethodMeasuring cognitive load in introductory CS: adaptation of an instrument
Morrison, B. B., Dorn, B., & Guzdial, M. (2014) · Proceedings of the tenth annual conference on International computing education research · doi:10.1145/2632320.2632348 · Cited by 147 · see citing works
Schmidt, M., Benzing, V., Wallman-Jones, A., Mavilidi, M. F., Lubans, D. R., & PAAS, F. G. W. C. (2018) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2018.12.017 · Cited by 108 · see citing works
Self-citationMeasurement of cognitive load in multimedia learning: a comparison of different objective measures
Korbach, A., Brünken, R., & Park, B. (2017) · Instructional Science · doi:10.1007/s11251-017-9413-5 · Cited by 98 · see citing works
“Therefore, one item might not be a valid measure of a specific cognitive load factor, whereas self-reports in general could be suitable not only to rate the total cognitive load but also to differentiate between the cognitive load factors (Leppink et al. 2013, 2014).”
BackgroundTwelve tips for medical curriculum design from a cognitive load theory perspective
Leppink, J., & Duvivier, R. (2016) · Medical Teacher · doi:10.3109/0142159x.2015.1132829 · Cited by 96 · see citing works
“Some recent studies have provided psychometric measures of different types of cognitive load (Leppink et al. 2013, 2014).”
BackgroundSelf-citationHochberg, K., Becker, S., Louis, M., Klein, P., & Kühn, J. (2020) · Journal of Science Education and Technology · doi:10.1007/s10956-020-09816-w · Cited by 93 · see citing works
Krell, M. (2017) · Cogent Education · doi:10.1080/2331186x.2017.1280256 · Cited by 91 · see citing works
“Despite this concern, Leppink et al. (2013) proposed an instrument to separately measure contentrelated (intrinsic load), instruction-related (extraneous load) and process-related (germane load) sources of CL (cf. Paas & Van Merriënboer, 1994).”
BackgroundMethodInfluentialCognitive Load Theory in Computing Education Research: A Review
Duran, R., Zavgorodniaia, A., & Sorva, J. (2022) · ACM Transactions on Computing Education · doi:10.1145/3483843 · Cited by 92 · see citing works
“Of those, CER has largely relied on the Paas single-question instrument [74] and Morrison and colleagues’ [69] programming-domain adaptation of Leppink and colleagues’ [50] ten-item questionnaire.”
BackgroundMethodGlogger‐Frey, I., Gaus, K., & Renkl, A. (2016) · Learning and Instruction · doi:10.1016/j.learninstruc.2016.11.002 · Cited by 88 · see citing works
Schneider, S., Krieglstein, F., Beege, M., & Rey, G. D. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104350 · Cited by 93 · see citing works
Interactive digital storytelling: bringing cultural heritage in a classroom
Rizvić, S., Bošković, D., Okanović, V., Šljivo, S., & Zukić, M. (2019) · Journal of Computers in Education · doi:10.1007/s40692-018-0128-7 · Cited by 87 · see citing works
Classroom complexity affects student teachers’ behavior in a VR classroom
Huang, Y., Richter, E., Kleickmann, T., Wiepke, A., & Richter, D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104100 · Cited by 91 · see citing works
“Cognitive load has typically been measured with subjective ratings, physiological measures and performance in the secondary task (e.g., Anmarkrud et al., 2019; Leppink et al., 2013; Paas et al., 2003).”
BackgroundWe need more replication research – A case for test-retest reliability
Leppink, J., & Pérez-Fuster, P. (2017) · Perspectives on Medical Education · doi:10.1007/s40037-017-0347-z · Cited by 86 · see citing works
“For example, when interested in the test-retest reliability of a questionnaire of six or more items on cognitive load experienced in a learning period that just finished [32, 37, 38], an interval of ten to fifteen minutes may do.”
BackgroundSelf-citationMakransky, G., Terkildsen, T. S., & Mayer, R. E. (2019) · Learning and Instruction · doi:10.1016/j.learninstruc.2018.12.001 · Cited by 82 · see citing works
Shi, Y., Du, J., & Worthy, D. A. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103367 · Cited by 82 · see citing works
Cognitive load experienced by nurses in simulation-based learning experiences: An integrative review
Rogers, B., & Franklin, A. E. (2021) · Nurse Education Today · doi:10.1016/j.nedt.2021.104815 · Cited by 80 · see citing works
The effects of video lecture viewing strategies on cognitive load
Costley, J., Fanguy, M., Lange, C. H., & Baldwin, M. (2020) · Journal of Computing in Higher Education · doi:10.1007/s12528-020-09254-y · Cited by 79 · see citing works
Liao, S., Kruger, J., & Doherty, S. (2020) · The Journal of Specialised Translation · doi:10.26034/cm.jostrans.2020.549 · Cited by 78 · see citing works
“Given that intrinsic cognitive load cannot be manipulated by instructional design (Leppink et al. 2013), a vital principle in learning instruction is therefore to reduce extraneous cognitive load so as to free up more cognitive resources, for instance, by optimising the instructional design of the stimuli.”
BackgroundOtt, N., Brünken, R., Vogel, M., & Malone, S. (2018) · Learning and Instruction · doi:10.1016/j.learninstruc.2018.04.010 · Cited by 78 · see citing works
Poquet, O., Lim, L.-A., Mirriahi, N., & Dawson, S. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170376 · Cited by 77 · see citing works
“The interventions in these studies tap into germane load – an element of cognitive load assisting with comprehension, and commonly measured through a validated survey instrument [14].”
BackgroundMaking an Effort Versus Experiencing Load
Klepsch, M., & Seufert, T. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.645284 · Cited by 79 · see citing works
Xie, H., Wang, F., Hao, Y., Chen, J., An, J., Wang, Y., & Liu, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183884 · Cited by 79 · see citing works
Curzon, P., Bell, T., Waite, J., & Dorling, M. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108654555.018 · Cited by 80 · see citing works
The Differential Effects of Comprehensive Feedback Forms in the Second Language Writing Class
López, M. B., Steendam, E. V., Speelman, D., & Buyse, K. (2018) · Language Learning · doi:10.1111/lang.12295 · Cited by 85 · see citing works
Margulieux, L. E., Ketenci, T., & Decker, A. (2019) · Computer Science Education · doi:10.1080/08993408.2018.1562145 · Cited by 75 · see citing works
Liu, T., Lin, Y.-C., & PAAS, F. G. W. C. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.10.019 · Cited by 75 · see citing works
Self-citationSeufert, T. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09574-6 · Cited by 73 · see citing works
Measuring cognitive load in augmented reality with physiological methods: A systematic review
Suzuki, Y., Wild, F., & Eileen Scanlon, A. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12882 · Cited by 74 · see citing works
“Frequently used cognitive load scales include the NASA Task Load Index (NASA-TLX) (Hart & Staveland, 1988), the Paas Subjecting Rating Scale (Paas, 1992) and Leppink's Cognitive Load Scale (Leppink et al., 2013).”
Faber, T. J. E., Dankbaar, M. E. W., van den Broek, W. W., Bruinink, L. J., Hogeveen, M., & van Merriënboer, J. J. G. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05698-3 · Cited by 73 · see citing works
“Apart from the previously mentioned 10-item scale by Lep-pink et al. [16], the 8-item questionnaire by Klepsch and Seufert [85] and the 15-item scale developed by Kriegl-stein et al. [86] appear promising instruments that distinguish between active and passive mental load.”
MethodA systematic map of research characteristics in studies on augmented reality and cognitive load
Buchner, J., Buntins, K., & Kerres, M. (2021) · Computers and Education Open · doi:10.1016/j.caeo.2021.100036 · Cited by 73 · see citing works
Jiang, D., & Zhang, L. J. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103841 · Cited by 76 · see citing works
Berssanette, J. H., & de Francisco, A. C. (2021) · IEEE Transactions on Education · doi:10.1109/te.2021.3127215 · Cited by 71 · see citing works
- Authors
- Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G.
- Affiliations
- Maastricht University (NL)
- University of Wollongong (AU)
- Corresponding author
- Leppink, Jimmie
- Venue
- Behavior Research Methods
- Published
- 2013-04-10
- Volume
- 45
- Issue
- 4
- Pages
- 1058-1072
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W1993729533
- Cited by
- 1,066
- References
- 45
- Citations by year
- 2026: 168 · 2025: 136 · 2024: 130 · 2023: 105 · 2022: 88 · 2021: 84 · 2020: 72 · 2019: 98 · 2018: 49 · 2017: 41 · 2016: 38 · 2015: 31 · 2014: 20 · 2013: 5
- FWCI
- 42.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Figshare (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- 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 › Education › Evaluation of Teaching Practices
- Keywords
- cognitive load, intrinsic cognitive load, extraneous load, germane load, instructional design, statistics instruction, university students, factor structure
- MeSH terms
- Adult, Analysis of Variance, Cognition, Female, Humans, Learning, Male, Models, Psychological, Surveys and Questionnaires, Reproducibility of Results, Linear Models, Behavioral Research, Young Adult
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