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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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“…consisting of two items measuring ICL, three for ECL and two (plus one, if applicable) to measure GCL. Krieglstein et al. (2022) conducted a meta-analysis of studies that had applied differentiated cognitive load scales (Eysink et al., 2009; Klepsch et al., 2017; Leppink et al., 2013, 2014).”
Zukić, M., Đapo, N., & Husremović, D. (2016) · Universal Journal of Psychology · doi:10.13189/ujp.2016.040505 · Cited by 14
“Finally, as Leppink, et al [15] state, the instrument can be adapted to measure CL while teaching other scientific disciplines, not just statistics.”
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“Dependent variables pertaining to cognitive load were measured by way of a student self-report survey with agreement-style response options anchored at the extremes: 0 = “I do not agree at all” and 10 = “I completely agree” (based on an instrument designed by Leppink et al., 2013).”
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“Conversely, the LLM-based scaffolding streamlined the learning process by offering on-demand clari fi cation, thus allowing students to allocate their cognitive resources more effectively to the learning task itself (Leppink et al., 2013).”
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“Although we do not measure cognitive load directly, we draw on established CLT-informed studies to conceptually align each type of cognitive load with core constructs in our design (Leppink et al. 2013; F. G. W. C. Paas and van Merriënboer 1994).”
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“This study modi fi ed the cognitive load questionnaires of Cierniak et al. (2009) and Leppink et al. (2013) for the usage of textbooks.”
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Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique
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BackgroundA Conceptual Review on Integration of Cognitive Load Theory and Human-Computer Interaction
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“It is considered that the subject ’ s prior knowledge determine the ICL [10].”
BackgroundDo research findings on schema-based instruction translate to the classroom?
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Why do cognitive prompts hurt learning in older adults?
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“…of the material and the learner’s knowledge, and EL is the (undesirable) load placed on working memory due to aspects, such as unclear or lengthy instructions while, on the other hand, GL is related to processes such as schema formation which are beneficial for learning (Leppink et al., 2013).”
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“…evaluations have not been evaluated in English (e.g., Klepsch et al., 2017; Krell, 2015), are specific to only one instructional context (e.g., see Leppink et al., 2013, 2014; Miller et al., 2021), or have not attempted to establish measurement invariance between different groups of learners…”
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“In recent years, a variety of cognitive load scales have been developed that have individual focal points and have been shown to cover different aspects of cognitive load (e.g., [27–29]).”
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“The 10 question-based cognitive load from Leppink et.al [28] were analyzed for intrinsic(Q 1,2,3), extrinsic(Q 4,5,6) and ger-mane(Q 7,8,9,10) load using the parametric one way ANOVA and asymptomatic Kruskal-Wallis test based on the fulfillment of the conditions for parametric data.”
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“However, the initial version of the 10-item CLS demonstrates adequate goodness of fit indices (χ2= 36.89, p=0.25; RMSEA=0.04) and internal consistency reliability (Cronbach's α > 0.80) (Hadie & Yusoff, 2016; Leppink et al., 2013).”
BackgroundInfluentialInfluence of Instructional Design to Manage Intrinsic Cognitive Load on Learning Effectiveness
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“At the same time, the multidimensional cognitive load scale developed by Leppink et al. (2013) was employed to gauge the cognitive loads perceived by the learners, assess the effectiveness of the multidimensional indicator, and thereby verify the results of our theoretical analysis.”
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“For the measurement of cognitive load a validated instrument for the measurement of intrinsic, extraneous and germane load in complex knowledge domains was used originally developed by Leppink et al. (2013) [22].”
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“In 2013, Leppink et al. presented a questionnaire to measure the three widely accepted cognitive load types separately: intrinsic, extraneous and germane.”
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“Leppink et al [57][58] and Cierniak et al [20] measures are used as they have specific questions to measure extraneous load and NASA-Task Load Index [47] are chosen as it is developed specifically for UID.”
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“Leppink et al. (2013) connect intrinsic load to the complexity of information, concepts, and definitions.”
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“For instance, one of the items in Leppink et al. ’ s instrument is “ The activity covered concepts and definitions that I perceived as very complex, ” and this statement is one of three in the instrument used to isolate perceived levels of intrinsic load (Leppink et al. 2013, p. 1070).”
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Küçük, S., Turan, Z., Özkan, Ç. N., Taş, Y. F., & Gürsoy, T. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2024.2408582 · Cited by 7
“The cognitive load scale, developed by Leppink et al. (2013) and adapted by Liao et al. (2019) to the middle school sample, was used to assess the cognitive load levels of students during collaborative AR activities.”
The Impact of Flipped Learning and Digital Laboratory in Basic Electronics Coursework
Portillo, F., Soler‐Ortiz, M., Sánchez-Cruzado, C., García, R. M., & Novas, N. (2024) · Computer Applications in Engineering Education · doi:10.1002/cae.22810 · Cited by 8
“Cognitive overload occurs when the volume or complexity of information a person must deal with exceeds their cognitive processing capacity [14].”
Liu, X., & Zhang, Y. (2024) · British Journal of Educational Psychology · doi:10.1111/bjep.12729 · Cited by 10
Lin, Y., Chien, S., & Hou, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13243-4 · Cited by 13
Kolil, V. K., Prasanna, P. S., & Achuthan, K. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13799-9 · Cited by 8
“Cognitive load was evaluated using a 10-item instrument with 10-point Likert-style questions (1: meaning not at all the case and 10: meaning completely the case), adapted from the literature (Leppink et al., 2013 ) (supplementary document, Table S3).”
Cognitive Impact of Fatigue in Forensic Neuropsychology Context
Schultz, I. Z., Sepehry, A. A., & Greer, S. C. (2018) · Psychological Injury and Law · doi:10.1007/s12207-018-9324-z · Cited by 10
The effects of example-free instruction and worked examples on problem-solving
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What explains children’s digital word reading performance in L2?
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“This 6-item 9-point bipolar scale was adapted from the extraneous cognitive load subscale in the Cognitive Load Index (CLI, Leppink et al., 2013).”
Zhang, S., Ding, S., Cui, W., Li, X., Wei, J. J., & Wu, Y. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293950 · Cited by 8
Feng, T.-T. (2023) · Education and Information Technologies : Official Journal of the IFIP technical committee on Education · doi:10.1007/s10639-023-12386-0 · Cited by 9
“According to researchers, there are currently no generally accepted or widely used means of measuring cognitive load, but researchers propose the development of such tools (Anmarkrud et al., 2019; Leppink et al., 2013; Thor-vald & Lindblom, 2014; Vainshtein et al., 2019).”
Exploring gesture frequencies and images in multimedia environments with pedagogical agents
Davis, R. O., Lee, Y., Vincent, J., & Wan, L. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13053 · Cited by 9
Human Cognitive Architecture Through the Lens of Cognitive Load Theory
Leppink, J., & Hanham, J. (2018) · Springer eBooks · doi:10.1007/978-981-13-2808-4_2 · Cited by 8
Self-citationShi, Y., Yang, H., Yang, Z., Liu, W., & Yang, H. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-51968-1_16 · Cited by 8
Shi, Y., Du, J., Zhu, Q., & Liu, X. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482858.010 · Cited by 8
The influence of affective decorative pictures on learning statistics online
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Der Einsatz von Virtual-Reality Lernszenarien für Softskills-Trainings
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“Auch der Effekt des „Expertise Reversal“ aus der Theorie der kognitiven Belastung könnte hier zutreffen, d.h. die Lernaufgaben wurden als zu einfach erlebt, wodurch sich der Einsatz von kognitiven Ressourcen nicht lohnte (Leppink et al. 2013).”
BackgroundHaden, P. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108654555.007 · Cited by 10
Theoretical Aspects of Multimodal Cognitive Load Measures
Chen, F., Zhou, J. J., Wang, Y., Yu, K., Arshad, S. Z., Khawaji, A., & Conway, D. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-31700-7_3 · Cited by 11
Cognitive Load Theory: Implications for Nursing Education and Research
Chen, R., Dore, K., Grierson, L. E. M., Hatala, R., & Norman, G. (2014) · Canadian Journal of Nursing Research · doi:10.1177/084456211404600204 · Cited by 8
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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