Cognitive Load Theory: How Many Types of Load Does It Really Need?

Introduction: Patient handoffs remain a significant patient safety challenge. Researchers have used cognitive load theory (CLT) to unpack the complexity of health professions workplace learning tasks including handoffs. 1 When the cognitive load of a handoff exceeds the working memory capacity of the trainee, learning and performance suffer. CLT focuses on the development of instructional strategies that match intrinsic load (IL) to the developmental stage of the learner, decrease extraneous load (EL), and optimize germane load (GL). CLT strategies have traditionally focused on modifying the complexity of the task, the knowledge of the learner, and/or the design of the instruction. CLT researchers have given little attention to how the learner’s emotions influence cognitive load. 2 Yet, we know that emotion modulates learning through numerous cognitive processes including motivation, attention, working memory, and long-term memory. 3 To address this gap, this study explores how emotion influenced the IL, EL, and GL imposed upon the working memory of trainees performing patient handoffs. Methods: From January to March 2019, we administered a cross-sectional survey to 1,807 residents and fellows from a large 24-hospital health system in the United States. Participants completed the survey after performing a handoff. The survey included questions about features of the learner, task, and instructional environment. IL, EL, and GL were measured with the 16-item Cognitive Load Inventory for Handoffs. 4 Emotions were assessed with an 8-item scale based on the circumplex model of affect. 5 The authors used factor analysis to identify the core dimensions of emotion and then explored the relationship between emotion and cognitive load with 3 types of analyses. First, bivariate correlational analyses examined the relationship between the emotion factors and learner, task, and environment features known to predict cognitive load. Second, we conducted univariate regression analyses to investigate whether emotion factor scores predicted IL, EL, and GL. Third, multivariate regression determined whether the addition of the emotion factors to models including these previously established predictors of cognitive load types increased the variance explained (Δ R2). Results: Six hundred and ninety-three (38.7%) of 1,807 residents and fellows completed a survey. Exploratory and confirmatory factor analysis identified 2 dimensions of emotion representing positive activation (labeled invigoration) and positive deactivation (labeled tranquility). The correlational analyses revealed that higher invigoration was associated with higher levels of training and use of a standardized verbal protocol, while lower levels of invigoration were associated with interruptions, difficulty obtaining essential information, and fatigue. Higher tranquility was associated with higher levels of training and lower levels of tranquility with interruptions, difficulty obtaining information, fatigue, longer handoffs, and greater number of patients. In univariate regression analyses, higher levels of invigoration, tranquility, and their interaction were independently associated with lower intrinsic load and extraneous load. The interaction of invigoration and tranquility predicted lower germane load. Finally, in the multivariate models of predictors for each type of cognitive load, the addition of the emotion term increased the amount of variance explained from 0.10 to 0.16 for IL, 0.15 to 0.27 for EL, and 0.11 to 0.14 for GL. Discussion: Emotion significantly influenced the cognitive load experienced by trainees while performing in vivo handoffs. Like studies in the simulation context, the structure of emotion during patient handoffs consisted of 2 factors. Higher invigoration/tranquility predicted lower IL, El, and GL. Traditionally, CLT researchers assumed that emotion primarily affected EL. Our results suggest a much broader impact of emotion. Significance: The study models how emotion and cognitive load can be measured during a workplace learning activity. These results suggest that enhancing invigoration and tranquility will reduce cognitive load and improve learning and performance. This highlights the importance of developing instructional strategies that help trainees regulate their emotions.

Background:Advancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.Objective:To develop and collect validity evidence for a revised self-report inventory that measures cognitive load types during a handoff.Methods:Based on prior published work, input from experts in cognitive load theory and handoffs, and a think-aloud exercise with residents, a revised Cognitive Load Inventory for Handoffs was developed. The Cognitive Load Inventory for Handoffs has items for intrinsic, extraneous, and germane load. Students who were second- and sixth-year students recruited from a Dutch medical school participated in four simulated handoffs (two simple and two complex cases). At the end of each handoff, study participants completed the Cognitive Load Inventory for Handoffs, Paas’ Cognitive Load Scale, and one global rating item for intrinsic load, extraneous load, and germane load, respectively. Factor and correlational analyses were performed to collect evidence for validity.Results:Confirmatory factor analysis yielded a single factor that combined intrinsic and germane loads. The extraneous load items performed poorly and were removed from the model. The score from the combined intrinsic and germane load items associated, as predicted by cognitive load theory, with a commonly used measure of overall cognitive load (Pearson’s r = 0.83, p < 0.001), case complexity (beta = 0.74, p < 0.001), level of experience (beta = −0.96, p < 0.001), and handoff accuracy (r = −0.34, p < 0.001).Conclusion:These results offer encouragement that intrinsic load during handoffs may be measured via a self-report measure. Additional work is required to develop an adequate measure of extraneous load.

Online courses are becoming ubiquitous and increasingly tend to use authentic learning tasks as the driving force for teaching and learning. Nevertheless, designing online courses that incorporate real– world tasks is more challenging as these problems require more cognitive processes (van Merriënboer Sluijsmans, 2009). This phenomenon can be explained by Cognitive Load Theory (CLT) introduced by Sweller (1994). CLT distinguishes three types of cognitive load: intrinsic, extraneous and germane load. The level of intrinsic load is assumed to be determined by the level of element interactivity. An element can be a definition, concept, formula and procedure that needs to be or has been learned. Extraneous load is mainly imposed by instructional procedures that are suboptimal, whereas germane load refers to the learners’ working memory resources available to deal with the complexity of the task or learning material (Sweller, 2010). Accordingly, the experienced cognitive load is mainly dependent of students’ prior knowledge. Nevertheless, cognitive load can also be determined by students’ motivation (Feldon, Franco, Chao, Peugh, Maahs-Fladung, 2018; Verhoeven, Schnotz, Paas, 2009). As a consequence, when designing an online course for complex tasks, it is important to understand how the different types of cognitive load are affected by students’ cognitive and motivational characteristics. Therefore, in the current study, a high and low complex task was developed relating to the learning and teaching of geometry. The complexity of the task was manipulated by increasing the element interactivity for the high complex task (Sweller, 2010). In the low complex task one element was questioned each time, and consequently students had to apply a single rule, formula or procedure. By contrast, the high complex task was based on a real-life context (e.g., teaching geometry), and had higher element interactivity. Subsequently, the high complex task required learners to engage in a series of cognitive activities such as analysing, decision making, implementing and evaluating, while holding several procedures and rules in mind. Accordingly, we expected the high complex task to induce more cognitive load. The same amount of support containing the same content, was provided during both tasks. Consequently, in this context, students could take initiative in diagnosing their learning needs by identifying appropriate support. Since students could consult different amounts of support, this self-directed learning strategy could also influence the perceived cognitive load (van Merriënboer Sluijsmans, 2009). Accordingly, the amount of consulted support was also taken into account during the analyses. The aim of the study was twofold. First, as a manipulation check of task complexity, we investigated differences in the experienced cognitive load while solving a high and low complex task. Secondly, we examined whether students’ cognitive and motivational characteristics influence the different types of perceived cognitive load, when taking into account the amount of consulted support for both the high and low complex task. A multivariate approach was chosen to assess the degree of interplay that may exist among students’ cognitive, motivational characteristics, consultation of support and the different types of perceived cognitive load. By conducting this study, we wanted to gain insight into whether the cognitive, motivational characteristics and consultation of support influence the perceived cognitive load differently for a high and low complex task.

Transaction analysis, the process of discerning which accounts are impacted (either increased or decreased) by a given transaction, is a fundamental accounting principle. While proficiency in this concept is vital for introductory accounting students, it poses a significant challenge for many. We addressed this hurdle using Excel and Excel VBA to develop a fully automated Journal Entry Learning Object (JELO) spreadsheet designed to aid first-year accounting students in grasping the intricacies of analyzing business transactions. Grounded in cognitive load theory (CLT), our spreadsheet based JELO provides a user-friendly interface that helps students develop their skills without becoming overwhelmed. Cognitive load theory, pioneered by John Sweller, suggests that learning is optimized when instructional materials reduce unnecessary cognitive load, allowing students to allocate their cognitive resources efficiently toward understanding and mastering the task at hand. CLT distinguishes between three types of cognitive load: intrinsic, extraneous, and germane (Sweller, Ayres, &amp; Kalyuga, 2011). Intrinsic cognitive load refers to the inherent difficulty associated with a specific instructional topic (Sweller, J. 2010). In transaction analysis, the intrinsic load is naturally high due to the complexity of understanding the impact of transactions on various accounts. To manage this, JELO has three tiers of transaction questions. Students are allowed to progress to more complex transactions only after demonstrating proficiency with the lower levels. Extraneous cognitive load is generated by inefficient presentation of information to learners (Paas &amp; Sweller, 2014). JELO minimizes extraneous load through its intuitive and interactive design. By providing clear instructions, a logical interface, and eliminating unnecessary information, JELO ensures that students can focus on the essential aspects of transaction analysis without being distracted by irrelevant information. Germane cognitive load refers to the mental effort required to create schemas and integrate new information into existing knowledge structures (Sweller et al., 2011). JELO enhances germane load by offering scaffolded and immediate feedback. This feedback is designed to reinforce correct responses and guide students through their errors with hints and explanations, facilitating deeper cognitive processing and better schema construction. Our goal was to make skill acquisition enjoyable and, more realistically, to reduce the pain of learning transaction analysis. The JELO offers an alternative to traditional “drill and practice” methods by providing an easy-to-use, interactive learning tool that dynamically adjusts the difficulty of its questions to match the student's skill level. This adaptive learning approach ensures that students are continually challenged just enough to promote learning without becoming overwhelmed. Additionally, JELO's feedback mechanism aligns with CLT principles by providing immediate positive reinforcement and corrective guidance. By doing so, it helps students build confidence and competence in transaction analysis, leading to a more effective and engaging learning experience. By incorporating principles of cognitive load theory, JELO ensures that students are neither under-challenged nor overwhelmed. This careful balancing of students’ overall cognitive load facilitates a more effective learning environment, fostering both proficiency and satisfaction in mastering transaction analysis.

Working memory is used to process information of our mental representations and new knowledge. In learning English language, students are faced with various kinds of material and instruction that relate to the development of their knowledge and skills. However, extraneous information may overload their cognitive load and inhibit their knowledge and performances. Therefore, the theory of cognitive load effects provides a solution towards the overloading of cognitive capacity by introducing cognitive load effects. Cognitive load theory itself deals with the instructional design methods that use the limited cognitive capacity of learners. There are three sources or types of cognitive load: the intrinsic load, extraneous load, and germane load. By analyzing an English textbook for class VIII secondary level in Indonesia designed by the Ministry of Education and Culture for distance learning in the Covid-19 pandemic era, this paper points out several cognitive load principles that create a better schema construction and automation that contribute to the decreased cognitive load.

Background: The use of virtual patients (VPs), due to their high complexity and/or inappropriate sequencing with other instructional methods, might cause a high cognitive load, which hampers learning.Aim: To investigate the efficiency of instructional methods that involved three different applications of VPs combined with lectures.Method: From two consecutive batches, 171 out of 183 students have participated in lecture and VPs sessions. One group received a lecture session followed by a collaborative VPs learning activity (collaborative deductive). The other two groups received a lecture session and an independent VP learning activity, which either followed the lecture session (independent deductive) or preceded it (independent inductive). All groups were administrated written knowledge acquisition and retention tests as well as transfer tests using two new VPs. All participants completed a cognitive load questionnaire, which measured intrinsic, extraneous and germane load. Mixed effect analysis of cognitive load and efficiency using the R statistical program was performed.Results: The highest intrinsic and extraneous load was found in the independent inductive group, while the lowest intrinsic and extraneous load was seen in the collaborative deductive group. Furthermore, comparisons showed a significantly higher efficiency, that is, higher performance in combination with lower cognitive load, for the collaborative deductive group than for the other two groups.Conclusion: Collaborative use of VPs after a lecture is the most efficient instructional method, of those tested, as it leads to better learning and transfer combined with lower cognitive load, when compared with independent use of VPs, either before or after the lecture.

Purpose of the Article. This study aims to substantiate the principles of interface design based on cognitive load theory, with a focus on balancing the reduction of excessive perceptual complexity and providing users with control capabilities in situations of uncertainty or conflicting outcomes. Research Methods. The methodological foundation is cognitive load theory, which distinguishes between intrinsic, extraneous, and germane load. The study employs comparative analysis of scientific publications, international accessibility standards (particularly WCAG 2.2), and modern software solutions oriented toward inclusive and adaptive design. Scientific Novelty. For the first time, practices for reducing cognitive load in web application interfaces are systematized from the perspective of combining extraneous load minimization with the integration of user control tools. Recommendations are proposed for designers and developers regarding the implementation of inclusive interface adaptation mechanisms, including tools based on artificial intelligence. Conclusions. Influence on intrinsic cognitive load is primarily achievable through the optimization of system messages, button texts, and hints, whereas user-generated content remains beyond the designer’s control. Excessive cognitive load can be reduced by adhering to standards for font size, line spacing, contrast, and clear visual structure. At the same time, parameters such as font choice or color scheme yield contradictory results and largely depend on users’ individual cognitive characteristics and prior experience. The optimal approach involves implementing personalization and adaptability mechanisms, allowing each user to set a comfortable level of complexity and find a balance between simplicity and functionality.

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).

Cognitive load theory has been a major influence for the field of educational psychology. One of the main guidelines of the theory is that extraneous cognitive load should be reduced to leave sufficient cognitive resources for the actual learning to take place. In recent years, research regarding various design factors, in particular from the field of digital and online learning, have challenged this assumption. Interactive learning media, immersion, disfluency, realism, and redundant elements constitute five major challenges, since these design factors have been shown to induce task-irrelevant cognitive load, i.e., extraneous load, while still promoting motivation and learning. However, currently there is no unified approach to integrate such effects into cognitive load theory. By including aspects of constructive alignment, an approach aimed at fostering deep forms of learning in order to achieve specific learning outcomes, we devise a strategy to balance cognitive load in digital learning. Most importantly, we suggest considering both the positive and negative effects on cognitive load that certain design factors of digital learning can cause. In addition, a number of research results highlight that some types of positive effects of digital learning can only be detected using a suitable assessment method. This strategy of aligning cognitive load with desired learning outcomes will be useful for formulating theory-guided and empirically testable hypotheses, but can be particularly helpful for practitioners to embrace emerging technologies while minimizing potential extraneous drawbacks.

ABSTRACTMany online videos feature an instructor on the screen to improve learners' engagement; however, the influence of this design on learners' cognitive load is underexplored. This study investigates the effects of instructor presence on learners' processing of information using both subjective and psychophysiological measures of cognitive load. Sixty university students watched a statistics instructional video either with or without instructor presence, while the spontaneous electrical activity of their brain was recorded using electroencephalography (EEG). At the conclusion of the video, they also self‐reported overall load, intrinsic load, extraneous load, and germane load they experienced during the video. Learning from the video was assessed via tests of retention and transfer. Results suggested the instructor‐present video improved learners' ability to transfer information and was associated with a lower self‐reported intrinsic and extraneous load. Event‐related changes in theta band activity also indicated lower cognitive load with instructor‐present video.

To a first approximation, human memory is divided into two parts, short-term and long-term. Cognitive Load Theory (CLT) attempts to minimize the short-term memory load while maximizing the memory available for transferring knowledge from short-term to long-term memory. According to CLT there are three types of load, intrinsic, extraneous, and germane. Our implementation of CLT components into our electronic homework system, JExam, attempts to minimize intrinsic and extraneous load by breaking down multistep problems into smaller, individual steps for the students. Using the static fading approach over several questions, students weave all the steps together to solve the entire problem. Using Item Response Theory (IRT) abilities, we compared the performance of students utilizing the CLT static fading approach to that of students without the CLT components. On seven out of eight topics, students exposed to CLT component homework questions scored higher on subsequent test questions related to that topic than those not exposed to CLT. In four out of the eight cases, students exposed to CLT based homework problems improved their chances of correctly answering related test questions by greater than 10%. Though there exists a large time commitment in producing CLT based homework problems, this study suggests that the reward, in terms of student performance, is well worth the investment.

In undergraduate education, theoretical learning remains foundational; nonetheless, the increasing use of technology to deliver abstract content poses new cognitive challenges. Slide-heavy presentations, dense digital materials, and online lecture formats often increase students' mental load. Framed within Cognitive Load Theory (CLT), this study investigates how undergraduate students experience and respond to the cognitive demands of lecture-based theoretical courses, specifically the ones with technological learning materials. CLT's framework, encompassing intrinsic, extraneous, and germane load, offers a valuable lens to explore the mental effort required for understanding theory-intensive content. This research adopts a qualitative approach, combining semi-structured interviews with previous researches of lecture materials and course outlines. Undergraduate students across multiple disciplines at a large public university will be invited to reflect on their experiences and engagement with digital lecture slides, recorded lectures, and other instructional technologies used in theory-heavy courses, particularly how they perceive cognitive challenges and the strategies they use to manage them. Document analysis will further illuminate how instructional design elements may contribute to or alleviate cognitive load. By centering student experiences, this study aims to uncover how technological design choices either support or hinder students' ability to process theoretical content. Findings are expected to inform more cognitively effective uses of educational technology in higher education, helping instructors reduce unnecessary load while promoting deeper engagement with abstract material.

The COVID-19 pandemic caused an unprecedented and highly threatening, constrained, and confusing social and educational environment, we decided to expand the traditional focus of the extraneous load in Cognitive Load Theory (CLT) acknowledging the psychological environment in which learning occurs. We therefore adapted and implemented principles of the CLT to reduce extraneous load for our students by facilitating their educational activities. Given previous empirical support for the principles of CLT, it was expected that the adoption of these principles might enable our students to cultivate attitudes and skills across multiple domains such as online learning and presentation technologies, implementing and maintaining a “classroom atmosphere” in a virtual environment, participating in discussions among large online groups of students, facilitating group work, providing virtual office hours for students, and proactively planning for upcoming semesters.

Cognitive Load Measurement, Worked-Out Modeling, and Simulation

This study investigates the effectiveness of applying Sweller’s (1988) cognitive load theory (CLT) principles to improve the learning of derivative concepts among first-year university students. The research employed a quasi-experimental pre-/post-test control group design to evaluate the impact of CLT-based instructional strategies–worked examples, visual scaffolds, and structured problem-solving–on students’ achievement and perceived cognitive load. A total of 60 students were randomly assigned to either an experimental group, receiving CLT-based instruction, or a control group, receiving traditional teaching. Data were collected using achievement tests and the cognitive load scale, assessing intrinsic, extraneous, and germane load components. Quantitative data were analyzed using independent samples t-tests and two-way and mixed-design ANOVA, with effect sizes (Cohen’s d, partial η²) reported to determine the magnitude of observed effects. Results indicated that the CLT-based instruction led to significantly higher achievement scores, reduced extraneous cognitive load, and increased germane processing compared to traditional methods. The findings support the theoretical validity of CLT and its practical applicability for mathematics instruction, suggesting that structured visual and example-based learning can optimize students’ cognitive resources and promote deeper understanding of calculus concepts.