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Development of an instrument for measuring different types of cognitive load
Open accessAbstract
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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“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
Experimental — accuracy not yet measuredIn the citing paper
“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
In the citing paper
“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)
In the citing paper
“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)
In the citing paper
“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
In the citing paper
“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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Citations
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“Cognitive load was evaluated using a questionnaire adapted from [19], examining the mental effort required by the learning method.”
Wang, Y., Cao, Y., & Gong, S.-Y. (2025) · Journal of educational computing research · doi:10.1177/07356331251410001 · Cited by 1
“Cognitive load was measured using a self-reported scale developed by Leppink et al. (2013) and revised by Xiong et al. (2018).”
Managing learning demands through mobile AI: A cognitive load perspective on ChatGPT use in Nepal
Sharma, S., & Khadka, A. (2026) · Mobile Media & Communication · doi:10.1177/20501579251407518 · Cited by 2
Interleaved practice in physics benefits from collaboration
Danzglock, M., Berger, R., & Hänze, M. (2026) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102307 · Cited by 1
Gao, W., Chen, P., Hu, S., Liu, Y., & Xian, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-35279-x · Cited by 3
Elmalı, F., & Citak, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2622591 · Cited by 1
“Cognitive-load management was not directly measured using child-appropriate indicators; no validated scales or objective proxies (e.g., latency/error logs) were collected (Andersen & Makransky, 2021; Leppink et al., 2013; Paas et al., 2016).”
Siam, M., AlBakri, O., Jeyaseelan, L., Atiomo, W., & Ezimokhai, M. (2026) · Cureus · doi:10.7759/cureus.104590 · Cited by 1
“GL represents productive cognitive effort directed toward schema construction and integration into long-term memory [9].”
Gan, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2637834 · Cited by 1
Using Sweller’s cognitive load theory to improve learning of derivative concepts
Canhasi-Kasemi, E., Orhani, S., & Temaj, I. (2026) · European Journal of Science and Mathematics Education · doi:10.30935/scimath/18060 · Cited by 1
Yang, E., Ryu, J., Choi, H., & Kim, I. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1805065 · Cited by 1
“This creates a measurement challenge for CLT-and SRL-oriented research: although self-report instruments remain useful, they are typically administered retrospectively and thus provide relatively coarse summaries of perceived effort (Leppink et al., 2013; Ouwehand et al., 2021).”
Abas, B., & Qureshi, S. K. (2026) · Journal of Organizational Effectiveness People and Performance · doi:10.1108/joepp-07-2025-0625 · Cited by 1
Ringdorfer, R., Dallinger, D., Kappe, C. O., & Spitzer, P. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/18508 · Cited by 1
“We acknowledge that this adaptation means our items are not directly comparable to the original Leppink et al. (2013) instrument, and we do not claim to measure cognitive load as a validated construct.”
InfluentialDavis, R. O., Lee, Y. L., Vincent, J., & Lee, J. H. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14050074 · Cited by 1
“After listening to the video podcast, participants completed a cognitive load questionnaire adapted from Leppink et al. (2013) to measure intrinsic (3 items), extraneous (3 items), and germane cognitive load (4 items).”
Liu, B., Wang, J., Yue, Z., Ma, J., & Zhang, J. (2026) · Behavioral Sciences · doi:10.3390/bs16050716 · Cited by 1
“For each design condition, participants rated cognitive load using the questionnaire by Leppink et al. (2013), which measures three dimensions: Intrinsic Cognitive Load (ICL; α = 0.914; e.g., “This learning material covers topics that are very complex.”)”
InfluentialMaslov, I., Poelmans, S., & Aysolmaz, B. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-16234-2_6 · Cited by 1
Xu, C., Niu, G., & Sun, X. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00594-w · Cited by 1
“Cognitive load scale To measure different types of cognitive load during IVR learning, we adapted the Multidimensional Cognitive Load Scale for Virtual Environments (MCLSVE) (Andersen & Makransky, 2020) from Leppink et al. (2013) (Andersen & Makransky, 2020).”
Sun, F., Liu, G., Liao, J., & Yang, Y. (2026) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-026-09483-w · Cited by 1
Wu, S., Peng, G., Li, S., Cameron, D., Zhang, J. I.-X., Zhang, J. I.-X., & Zhang, Q. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103846 · Cited by 3
Sun, Y., Wang, X., & Wei, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1835027 · Cited by 1
“Following the approach described by Leppink et al. (2013) and adapted for performance-based learning tasks, two educational psychology experts independently classified each NASA-TLX dimension according to its primary theoretical alignment: mental demand and temporal demand were mapped to intrinsic…”
Generative AI-Driven Virtual Reality for Health Consultation Training in School Nursing
Geng, X., Eto, M., & Yamada, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31421-5_4 · Cited by 1
Arshad, A., Lone, A., Arickswamy, L., Hassan, K., Alnaim, A. A., & Alfarhan, M. F. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1883053 · Cited by 2
Shan, G., Rivera, M., Kumar, S., & Anand, P. (2026) · Journal of Management Information Systems · doi:10.1080/07421222.2026.2692282 · Cited by 1
“Cognitive load theory provides a structured lens to examine the mental effort involved in such tasks [49, 62].”
Opinion Acquisition: An Experiment on Numeric, Linguistic and Color Coded Rating Scale Comparison
Pilipczuk, O., & Cariowa, G. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-48429-7_3 · Cited by 1
Metadata
- 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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