Cognitive Load Theory Questionnaire

Abstract

The Cognitive Load Theory Questionnaire (CLT Questionnaire), developed by Felix Krieglstein, Maik Beege, Günter Daniel Rey, Christina Sanchez-Stockhammer, and Sascha Schneider (2023), is a psychometrically validated self-report instrument designed to differentiate and quantify the three canonical dimensions of cognitive load: intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL). Rooted firmly in evolutionary educational psychology and Sweller’s Cognitive Load Theory (CLT; Sweller, 2020), the scale addresses long-standing methodological and psychometric vulnerabilities found in earlier multidimensional inventories. Comprising 15 items evenly distributed across three subscales (5 items per dimension), respondents evaluate targeted instructional materials and learning procedures using a 9-point Likert-type scale ranging from 1 (not at all applicable) to 9 (fully applicable). The development and psychometric evaluation of the instrument adhered to rigorous scale development guidelines (e.g., Carpenter, 2018; DeVellis & Thorpe, 2021). Evaluated across university students and adult learning cohorts in Germany, the instrument demonstrated exceptional structural validity and internal consistency. Principal Component Analysis (PCA) demonstrated that a three-component solution accounted for 74.3% of the total variance following the systematic refinement of problematic items. Confirmatory Factor Analysis (CFA) affirmed structural integrity with satisfactory goodness-of-fit indices (CFI = .92, GFI = .92, SRMR = .07, RMSEA = .10), exhibiting salient factor loadings across all latent dimensions (ICL and ECL loadings: .73 to .93; GCL loadings: .35 to .91). Reliability analyses yielded high composite internal consistency estimates using McDonald’s omega (ω_ICL = .93, ω_ECL = .93, ω_GCL = .80). Convergent validity was established against the multidimensional cognitive load instrument of Klepsch et al. (2017), evidencing strong alignment for ECL, moderate alignment for ICL, and distinct conceptual differentiation for GCL. The instrument provides instructional designers, cognitive scientists, and educational technologists with a robust diagnostic metric for optimizing learning media in line with the limitations and affordances of human cognitive architecture.

Keywords

Cognitive Load Theory, Intrinsic Cognitive Load, Extraneous Cognitive Load, Germane Cognitive Load, Instructional Design, Educational Psychology, Working Memory Architecture, Psychometrics, Factor Analysis, Learning Media Evaluation

Authors

The Cognitive Load Theory Questionnaire was conceptualized, developed, and empirically validated by an interdisciplinary team of researchers specializing in educational technology, cognitive media psychology, and applied linguistics:

• Felix Krieglstein (Corresponding Author) — Faculty of Humanities, Institute for Media Research, Psychology of Learning with Digital Media, Chemnitz University of Technology, Chemnitz, Germany. ORCID: 0000-0002-1324-3816. Email: [email protected]
• Maik Beege — Department of Psychology, Digital Media in Education, University of Education Freiburg, Freiburg, Germany.
• Günter Daniel Rey — Faculty of Humanities, Institute for Media Research, Psychology of Learning with Digital Media, Chemnitz University of Technology, Chemnitz, Germany.
• Christina Sanchez-Stockhammer — Faculty of Humanities, Institute for English and American Studies, English and Digital Linguistics, Chemnitz University of Technology, Chemnitz, Germany.
• Sascha Schneider — Faculty of Arts and Social Sciences, Institute of Education, Educational Technology, University of Zurich, Zurich, Switzerland.

Purpose

The central aim of the Cognitive Load Theory Questionnaire is to deliver an empirically sound, theoretically aligned diagnostic measurement of the three cognitive load facets operating within working memory during academic and digital instructional interventions. Cognitive Load Theory posits that human learning is constrained by the severely restricted capacity and duration of working memory when dealing with novel, biologically secondary information (Sweller, 2020). When instructional formats impose processing demands that exceed this cognitive threshold, learning failure inevitably ensues. Consequently, instructional designers, experimental psychologists, and educational practitioners require precise psychometric tools capable of isolating the sources of cognitive burden.

Historically, educational psychology relied heavily upon unidimensional mental effort metrics, most notably the classic subjective rating scale developed by Paas (1992). While useful as an omnibus index of overall perceived cognitive strain, unidimensional scales fail to differentiate whether observed cognitive expenditure stems from the innate difficulty of the learning material, poorly executed multimedia design, or constructive schema acquisition. Subsequent multidimensional measures (e.g., Leppink et al., 2013; Klepsch et al., 2017) attempted to capture this tripartite distinction; however, they frequently encountered psychometric dilemmas, including unstable factor structures, problematic cross-loadings, conflation between perceived difficulty and cognitive load, and ambiguous item interpretations regarding germane processing. Krieglstein and colleagues (2023) developed the CLT Questionnaire to remediate these structural limitations by following stringent psychometric instrument development criteria (e.g., Carpenter, 2018; DeVellis & Thorpe, 2021).

In empirical research contexts, the instrument serves as an indispensable manipulation check for laboratory experiments and field trials investigating instructional principles (such as the split-attention effect, modality effect, signaling effect, and redundancy effect). By administering the scale immediately post-intervention, researchers can ascertain whether experimental manipulations successfully mitigated extraneous load without artificially depressing germane processing or confounding intrinsic complexity. In applied educational and human-computer interaction (HCI) settings, the questionnaire provides curriculum developers, e-learning software engineers, and interactive media designers with granular diagnostic feedback. Specifically, it enables practitioners to isolate whether learner disengagement or poor performance originates from inadequate prerequisite knowledge (high ICL), counter-intuitive interface architecture and confusing instructional design (high ECL), or deficient metacognitive investment in learning and schema consolidation (low GCL).

Psychological Construct

The Cognitive Load Theory Questionnaire assesses cognitive processing demands through a multidimensional construct rooted in the architecture of human memory. Human cognition relies on an essentially unlimited long-term memory store where knowledge is organized into cognitive schemata, contrasted against a conscious working memory characterized by extreme limitations in simultaneous processing capacity (roughly 4 ± 1 information elements) and temporal duration when processing unorganized information. Within this cognitive processing framework, total cognitive load consists of three functionally distinct, additive dimensions:

1. Intrinsic Cognitive Load (ICL)

Intrinsic cognitive load represents the innate processing demands imposed by the essential nature of the learning task itself, independent of instructional presentation. The primary determinant of ICL is element interactivity: the number of distinct informational elements that must be processed simultaneously in working memory to achieve conceptual comprehension. When elements can be learned serially in isolation (such as memorizing vocabulary translations), element interactivity is low, resulting in modest ICL. Conversely, when individual elements cannot be understood without concurrently apprehending their functional interrelations (such as mathematical proofs, physiological cascades, or thermodynamic laws), element interactivity is high, generating heavy intrinsic load. Crucially, ICL is a relational construct defined by the interaction between task complexity and learner expertise; domain experts who have consolidated complex elements into automated schema nodes experience markedly lower ICL than novices encountering the material for the first time. The CLT Questionnaire operationalizes ICL through 5 items assessing perceived conceptual difficulty, structural complexity, and the requirement for prior knowledge to render the information coherent.

2. Extraneous Cognitive Load (ECL)

Extraneous cognitive load represents non-essential mental processing induced by suboptimal instructional design, inefficient communication, poor layout, or superfluous multimedia elements. Unlike ICL, which is inherent to the instructional objective, ECL introduces cognitive interference that expends finite working memory capacity without contributing to schema acquisition or automation. Classic manifestations of extraneous load include the split-attention effect (requiring learners to mentally integrate disparate, physically separated text and graphic elements), redundant multimodal narration, cluttered user interfaces, or ambiguous navigational pathways. Because working memory capacity is strictly bounded, high levels of ECL deplete resources that could otherwise be allocated to understanding complex content or actively building mental models. The CLT Questionnaire captures ECL via 5 items explicitly gauging instructional design flaws, such as difficulty discerning structural overviews, hindrances in connecting disparate information units, awkward material layouts, impediments in finding relevant information, and attention disruption attributable directly to design properties.

3. Germane Cognitive Load (GCL)

The conceptualization and psychometric capture of germane cognitive load have undergone substantial evolution within educational psychology. Originally conceptualized by Sweller and colleagues as an independent, third form of cognitive load that represents dedicated mental processing devoted to schema construction and automation, more recent theoretical revisions (e.g., Sweller et al., 2011; Sweller, 2020) have redefined GCL not as an independent source of load per se, but as the productive redirection of working memory resources toward schema acquisition. Under this refined view, total cognitive capacity is distributed between intrinsic demands (essential processing) and extraneous demands (ineffective processing); germane processing represents the actual working memory resources actively and productively committed to processing the intrinsic load. In the CLT Questionnaire, Krieglstein et al. (2023) operationalize GCL as active cognitive investment, generative learning effort, schema expansion, and perceived knowledge transferability. The 5 items measuring GCL query the learner’s active cognitive reflection, deliberate exertion of mental effort to achieve deep comprehension, self-appraised attainment of comprehensive insight, successful structural integration of novel content with pre-existing knowledge schemas, and the perceived readiness to deploy newly acquired knowledge dynamically.

Theoretical Framework

The psychometric architecture of the CLT Questionnaire is grounded in Cognitive Load Theory (Sweller, 1988, 2010, 2020; Paas et al., 2003, 2010), an instructional theory derived from principles of evolutionary biology and human cognitive architecture. Sweller categorizes human knowledge into biologically primary knowledge (which humans have evolved to acquire effortlessly and unconsciously, such as learning a native spoken language, basic facial recognition, or social cueing) and biologically secondary knowledge (cultural, academic, and scientific knowledge that humans have not evolved to acquire automatically, such as reading, mathematics, scientific reasoning, and formal logic). Secondary knowledge mandates deliberate, conscious instructional mechanisms and is mediated entirely through the structural constraints of the human cognitive system.

Foundational Cognitive Architecture

The standard model of human cognitive architecture central to CLT comprises two interacting memory systems:

• Long-Term Memory (LTM): A virtually limitless, permanent repository of automated and semi-automated schemata. Schemata categorize multiple informational elements into a single conceptual unit, fundamentally bypassing processing bottlenecks. Expertise consists predominantly of the depth, breadth, and automaticity of these schemata.
• Working Memory (WM): The conscious processor of incoming sensory input and retrieved memory representations. As demonstrated by seminal cognitive research (e.g., Miller, 1956; Baddeley, 1992; Cowan, 2001), working memory is severely restricted in both capacity (able to hold approximately 3 to 5 discrete elements simultaneously) and duration (novel information degrades within 20 to 30 seconds unless maintained through rehearsal).

Crucially, these working memory constraints apply strictly to novel information acquired from the environment. When retrieving organized schemata from long-term memory, working memory treats complex schemata as single functional units, thereby enabling experts to manipulate immense amounts of domain-specific data without experiencing cognitive overload. This architectural mechanism is known as the borrowing and reorganizing principle paired with the automation principle.

The Additive Model of Cognitive Load

Historically, CLT conceptualized working memory capacity as an additive equation:

Total Cognitive Load = Intrinsic Cognitive Load + Extraneous Cognitive Load + Germane Cognitive Load

If the sum of these processing components exceeds an individual’s total available working memory threshold ($C_{WM}$), cognitive overload occurs ($ICL + ECL + GCL > C_{WM}$), precipitating a catastrophic decrement in learning efficacy, retention, and transfer performance. The overarching mandate of instructional design is therefore threefold: (1) accurately calibrate ICL to match the current developmental or expertise level of the learner, (2) ruthlessly minimize or eliminate ECL through optimized visual-spatial, auditory, and structural instructional formatting, and (3) liberate maximum cognitive bandwidth for GCL, thereby facilitating active schema induction, contextual elaboration, and cognitive consolidation.

In developing their instrument, Krieglstein et al. (2023) synthesized these structural principles with Mayer’s Cognitive Theory of Multimedia Learning (CTML; Mayer, 2014, 2021). Mayer delineates triarchic cognitive processing consisting of extraneous processing (equivalent to ECL), essential processing (equivalent to ICL), and generative processing (equivalent to GCL). By designing questionnaire items that tightly match these definitions, the authors operationalized cognitive load not as an ambiguous emotional state of frustration or exhaustion, but as a direct reflection of structural working memory allocation during multimedia learning events.

Validity

The validity of the CLT Questionnaire was established across diverse German-speaking adult and university student cohorts through multi-step psychometric evaluations conforming to modern testing standards.

Content and Construct Validity

To ensure high content validity, the item development phase incorporated comprehensive literature reviews of preceding cognitive load measurement batteries (such as Paas, 1992; Cierniak et al., 2009; Leppink et al., 2013; Klepsch et al., 2017) alongside formal theoretical definitions established by cognitive load scholars (Sweller, 2020). Item pools underwent expert review by cognitive and instructional psychologists to refine syntactic clarity, remove semantic ambiguities, ensure direct applicability across diverse instructional modalities (e.g., printed text, digital instructional animations, hypermedia, video lectures), and eliminate construct contamination (e.g., conflating intrinsic difficulty with emotional stress or demotivation). Initial empirical testing evaluated an expanded pool of candidate items, leading to the identification and removal of two psychometrically deficient items that exhibited problematic cross-loadings or poor conceptual fit, resulting in a finalized, parsimonious 15-item structure.

Convergent Validity

Convergent validity was evaluated by computing Pearson correlation coefficients ($r$) between the subscales of the CLT Questionnaire and the widely utilized multidimensional cognitive load inventory developed by Klepsch, Schmitz, and Seufert (2017). The observed correlation matrix demonstrated robust convergence with established benchmarks (e.g., Carlson & Herdman, 2012):

• Extraneous Cognitive Load: The ECL subscale manifested strong, statistically significant convergent validity when correlated against the corresponding Klepsch et al. extraneous load items. This confirms that items capturing design-induced hindrances, interface opacity, and fragmented learning content align tightly across independent psychometric operationalizations.
• Intrinsic Cognitive Load: The ICL subscale demonstrated moderate-to-high, statistically significant positive correlations with the Klepsch et al. intrinsic load scale. The magnitude of this association confirmed that both scales successfully target task-inherent complexity, element interactivity, and prerequisite knowledge gaps, while Krieglstein et al.’s formulation achieved enhanced conceptual clarity by eliminating ambiguity regarding content length versus content density.
• Germane Cognitive Load: The GCL subscale demonstrated a lower, albeit statistically significant, correlation with the Klepsch et al. germane dimension. Krieglstein et al. (2023) noted that this lower correlation is theoretically expected and desirable, reflecting long-standing psychometric criticisms of earlier GCL measures. While previous instruments frequently conflated germane load with generic concentration or perceived learning success, the CLT Questionnaire isolates active cognitive elaboration, knowledge integration, and application readiness, establishing distinct construct refinement rather than redundant replication.

Discriminant Validity

Discriminant validity across the three latent dimensions of the CLT Questionnaire was confirmed through factor-analytic intercorrelations. Although ICL and ECL assess cognitive demands, their inter-factor correlations remained within an acceptable range, demonstrating that task complexity and presentation format operate as structurally independent dimensions. Similarly, GCL maintained negative-to-orthogonal correlations with ECL, demonstrating that design-induced extraneous load actively suppresses generative schema formation, exactly as predicted by Cognitive Load Theory.

Reliability

The reliability of the Cognitive Load Theory Questionnaire was systematically assessed through modern internal consistency estimations, utilizing McDonald’s model-based composite reliability coefficient omega (ω) alongside conventional Cronbach’s alpha (α). McDonald’s omega is widely recognized by contemporary psychometricians (e.g., Crutzen & Peters, 2017) as superior to Cronbach’s alpha because it does not depend on the unrealistic assumption of essential tau-equivalence (equal factor loadings across all items within a subscale).

Across the validated samples comprising adult participants and German university undergraduates engaged in digital learning interventions, the subscales demonstrated robust internal consistency:

• Intrinsic Cognitive Load Subscale: The 5-item ICL subscale demonstrated exceptional composite reliability, yielding an omega coefficient of ω_ICL = .93. This high reliability indicates minimal measurement error and confirms that items probing difficulty, informational complexity, and reliance on prior knowledge converge reliably onto a unified latent construct.
• Extraneous Cognitive Load Subscale: The 5-item ECL subscale likewise demonstrated outstanding internal reliability, yielding an omega coefficient of ω_ECL = .93. The individual items assess instructional layout, informational linkage, structural clarity, navigational velocity, and attentional focus, demonstrating exceptional internal cohesion when evaluating varied digital media designs.
• Germane Cognitive Load Subscale: The 5-item GCL subscale demonstrated good internal consistency, with an omega coefficient of ω_GCL = .80. Given the broad nature of generative cognitive processing—encompassing active reflection, mental exertion, schema construction, and transfer readiness—an omega value of .80 represents a theoretically balanced psychometric threshold, avoiding the item redundancy commonly indicated by omega estimates exceeding .95.

Item-total correlations across all three subscales consistently exceeded standard psychometric cutoff values ($r_{it} > .40$), confirming that each individual item contributes meaningfully to its designated theoretical dimension.

Factor Analysis

The structural integrity and latent dimensionality of the 15-item CLT Questionnaire were investigated using a sequential exploratory and confirmatory factor-analytic strategy.

Principal Component Analysis (PCA)

Prior to confirmatory testing, dimensionality was evaluated using Principal Component Analysis with oblique rotation (promax/oblimin), reflecting the theoretical assumption that the three cognitive load dimensions are correlated under real-world learning conditions. Following the identification and removal of two problematic items from the initial candidate item pool due to insufficient primary loadings (< .40) or unacceptable cross-loadings (> .30 on secondary components), the final 15-item battery resolved cleanly into three discrete, theoretically coherent components.

These three components collectively accounted for an impressive 74.3% of the total cumulative variance in participants’ responses. The scree test, eigenvalue inspection (Kaiser-Guttman criterion with eigenvalues substantially exceeding 1.0), and parallel analysis consistently supported the retention of three latent factors corresponding strictly to Intrinsic Cognitive Load, Extraneous Cognitive Load, and Germane Cognitive Load.

Confirmatory Factor Analysis (CFA)

To rigorously test the fit of the hypothesized three-factor measurement model, Confirmatory Factor Analysis was executed using maximum likelihood estimation. The hypothesized structural model specified 15 observed indicators loading onto three correlated latent traits ($ICL$, $ECL$, and $GCL$), with error terms constrained to be uncorrelated.

The empirical data exhibited acceptable to good overall model fit according to standard structural equation modeling criteria (e.g., Hu & Bentler, 1999):

• Comparative Fit Index (CFI): .92 (exceeding the standard .90 threshold for acceptable fit in newly developed multidimensional scales).
• Goodness-of-Fit Index (GFI): .92.
• Standardized Root Mean Square Residual (SRMR): .07 (satisfying the stringent ≤ .08 threshold for acceptable model fit).
• Root Mean Square Error of Approximation (RMSEA): .10.

Analysis of standardized factor loadings revealed that all 15 items loaded significantly ($p < .001$) onto their designated latent factors:

• ICL Dimension: Standardized factor loadings were uniformly high, ranging from .73 to .93, indicating that the indicators measure intrinsic complexity with minimal residual error.
• ECL Dimension: Standardized factor loadings similarly exhibited high magnitudes, ranging from .73 to .93, confirming that items successfully operationalize presentation and design hindrances.
• GCL Dimension: Factor loadings displayed greater dispersion, ranging from .35 to .91. Notably, Item 11 (“I actively reflected upon the learning content”; loading = .41) and Item 13 (“I made an effort to understand the learning content”; loading = .35) demonstrated comparatively lower factor saturations. However, Krieglstein and colleagues explicitly retained these two items to protect the theoretical convergence and content completeness of the germane construct, ensuring that active mental effort and conscious reflection remain represented in the scale architecture.

Instrument / Measurement Tool

The Cognitive Load Theory Questionnaire is a standardized, self-report psychometric instrument designed for post-task administration following an instructional learning episode.

• Test Type: Multi-dimensional self-report rating scale / diagnostic survey inventory.
• Format: Paper-and-pencil or computer-administered digital assessment.
• Item Count: 15 items total, organized into three balanced subscales:
  • Intrinsic Cognitive Load (ICL): 5 items (Items 1 to 5)
  • Extraneous Cognitive Load (ECL): 5 items (Items 6 to 10)
  • Germane Cognitive Load (GCL): 5 items (Items 11, 13, 15, 16, 17)
• Response Scale: Participants are asked to evaluate referenced learning material using a Likert-type rating scale ranging from 1 (not at all applicable) to 9 (fully applicable).
• Target Population: Adults and university students (aged 18 years and older) participating in educational, laboratory, or vocational instructional interventions.
• Administration Time: Approximately 3 to 5 minutes. The instrument is intentionally brief to minimize memory decay when administered immediately following learning sessions.
• Scoring Rules:
  • No reverse-scored items exist within the scale; all items are coded in a positive conceptual direction relative to their targeted subscale.
  • Subscale Scores: Calculated by averaging the numerical responses (ranging from 1 to 9) of the items corresponding to each respective dimension:
    • $\text{Score}_{ICL} = \frac{\text{Item } 1 + \text{Item } 2 + \text{Item } 3 + \text{Item } 4 + \text{Item } 5}{5}$
    • $\text{Score}_{ECL} = \frac{\text{Item } 6 + \text{Item } 7 + \text{Item } 8 + \text{Item } 9 + \text{Item } 10}{5}$
    • $\text{Score}_{GCL} = \frac{\text{Item } 11 + \text{Item } 13 + \text{Item } 15 + \text{Item } 16 + \text{Item } 17}{5}$
  • Total Score: Computing an omnibus composite score across all 15 items is generally discouraged from a psychometric standpoint, as the three dimensions represent qualitatively distinct constructs that behave differently in response to instructional interventions. Reporting individual subscale mean scores preserves critical diagnostic nuance.

Permissions & Fee and Test Year

• Publication Year: 2023
• Original Authors: Felix Krieglstein, Maik Beege, Günter Daniel Rey, Christina Sanchez-Stockhammer, and Sascha Schneider.
• Copyright & Open Access Licensing: The Cognitive Load Theory Questionnaire was published in Educational Psychology Review under an open-access Creative Commons Attribution 4.0 International License (CC BY 4.0).
• Usage Permissions: The instrument may be reproduced, distributed, adapted, and utilized freely for academic, non-commercial research, institutional evaluation, and teaching purposes without requiring formal permission from the authors or publisher, provided appropriate academic attribution is granted to the original 2023 publication.
• Commercial Use: Permitted under the terms of CC BY 4.0, provided that complete citation and source attribution are maintained and modifications are clearly designated.
• Fee: None. The scale is completely free of charge.

References

• Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359
• Carlson, K. D., & Herdman, A. O. (2012). Understanding the impact of convergent validity on research results. Organizational Research Methods, 15(1), 17–32. https://doi.org/10.1177/1094428110392383
• Carpenter, S. (2018). Ten steps in scale development and reporting: A guide for researchers. Communication Methods and Measures, 12(1), 25–44. https://doi.org/10.1080/19312458.2017.1396583
• Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, 25(2), 315–324. https://doi.org/10.1016/j.chb.2008.12.020
• Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. https://doi.org/10.1017/s0140525x01003922
• Crutzen, R., & Peters, G.-J. Y. (2017). Scale quality: Alpha is an inadequate estimate of internal consistency, and it is time to move on. The European Health Psychologist, 19(6), 252–259. https://doi.org/10.31234/osf.io/hfsng
• DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). SAGE Publications.
• Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in Psychology, 8, 1997. https://doi.org/10.3389/fpsyg.2017.01997
• Krieglstein, F., Beege, M., Rey, G. D., Sanchez-Stockhammer, C., & Schneider, S. (2023). Development and validation of a theory-based questionnaire to measure different types of cognitive load. Educational Psychology Review, 35(1), 37. https://doi.org/10.1007/s10648-023-09738-0
• Leppink, J., Paas, F., Van der Vleuten, C. P., Van Gog, T., & Der Neer, J. J. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1
• Mayer, R. E. (Ed.). (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369
• Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781316941355
• Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
• Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434. https://doi.org/10.1037/0022-0663.84.4.429
• Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1
• Paas, F., Van Gog, T., & Sweller, J. (2010). Cognitive load theory: New conceptualizations, specifications, and measure. Educational Psychology Review, 22(2), 115–121. https://doi.org/10.1007/s10648-010-9133-8
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
• Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
• Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16. https://doi.org/10.1007/s11423-019-09701-3
• Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer Science & Business Media. https://doi.org/10.1007/978-1-4419-8126-4

Items of the Scale