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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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“These can be measured using domain-specific knowledge tests administered before, immediately after, and at delayed-retention intervals, alongside validated measures of cognitive load such as the Leppink et al. (2013) instrument, which distinguishes intrinsic, extraneous, and germane load.”
The Effects of Agent Type and Feedback Style on Self-Directed Learning: A Mixed-Methods Study
Han, X., Cao, J., Wang, Z., & Luo, H. (2026) · Behavioral Sciences · doi:10.3390/bs16071069 · Cited by 1
“Cognitive load was adapted from the extraneous cognitive load subscale (Leppink et al., 2013) consisting of four items assessing the additional cognitive effort required to process feedback.”
Tang, L., Wu, C. S., Ma, L., Wang, L., Han, J., & Jiang, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1858297
“The scale was adapted from the multidimensional cognitive load questionnaires proposed by Klepsch et al. (2017) and Leppink et al. (2013).”
Confounding Components: Problems with Cognitive Load Surveys in Introductory Computer Science
Watkins, A., Settle, A., & Miller, C. S. (2026) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3803400.3809387
“The MCLS is widely used in CS education research [15] and was adapted from the LCLS [12] for introductory CS contexts.”
InfluentialHung, C., Liu, S., Wu, S., & Liu, F. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09759-7
“…progress ( r = .196, explaining approximately 8.5% of variance when considering all cognitive load factors collectively) is directionally consistent with cognitive load theory predictions [20, 21, 31], while reflecting real-world educational complexity where multiple factors influence outcomes.”
InfluentialZiba, M., Ranjbar, M. J., Mortazavi, S., & Gilbert, S. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30086-7_36
Wąsik, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29656-6_11
Guslinda, Suarman, Sumarno, & Putra, Z. H. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026761
Hu, Y., Zhang, H., & Wu, B. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10673-4
Scano, A. (2026) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2026.1838867
“Second, attention and cognitive load can be quantified using established paradigms such as dual-task interference (Pashler, 1994), EEG techniques (Mastropietro et al., 2023), or validated psychometric instruments (Leppink et al., 2013).”
Végh, L., Takáč, O., Czakóová, K., & Demeter, D. (2026) · Education Sciences · doi:10.3390/educsci16071135
Cognitive Apprenticeship AI Agent: A Pilot Study of Feedback Timing in Programming Learning
Lin, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32115-2_46 · Cited by 1
Tsai, C.-Y., & Yen, W.-H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32115-2_12
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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