Performance of a cognitive load inventory during simulated handoffs: Evidence for validity

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.

Cognitive load theory has been traditionally described as involving three separate and additive types of load. Germane load is considered as a learning-relevant load complementing extraneous and intrinsic load. This article argues that, in its traditional treatment, germane load is essentially indistinguishable from intrinsic load, and therefore this concept may be redundant. Contrary to extraneous and intrinsic load, germane cognitive load was added to the cognitive load framework based on theoretical considerations rather than on specific empirical results that could not be explained without this concept. The design of corresponding learning activities always required methods and techniques external to the theory. The article suggests that the dual intrinsic/extraneous framework is sufficient and non-redundant and makes boundaries of the theory transparent. The idea of germane load might have an independent role within this framework if (as recently suggested by John Sweller) it is redefined as referring to the actual working memory resources devoted to dealing with intrinsic rather than 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, & 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 & 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.

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.

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.

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.

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.

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.

Cognitive Load Measurement, Worked-Out Modeling, and Simulation

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.

COVID necessitated the shift to virtual resident instruction. The challenge of learning via virtual modalities has the potential to increase cognitive load. It is important for educators to reduce cognitive load to optimize learning, yet there are few available tools to measure cognitive load. The objective of this study is to identify and provide validity evidence following Messicks' framework for an instrument to evaluate cognitive load in virtual emergency medicine didactic sessions. This study followed Messicks' framework for validity including content, response process, internal structure, and relationship to other variables. Content validity evidence included: (1) engagement of reference librarian and literature review of existing instruments; (2)engagement of experts in cognitive load, and relevant stakeholders to review the literature and choose an instrument appropriate to measure cognitive load in EM didactic presentations. Response process validity was gathered using the format and anchors of instruments with previous validity evidence and piloting amongst the author group. A lecture was provided by one faculty to four residency programs via ZoomTM. Afterwards, residents completed the cognitive load instrument. Descriptive statistics were collected; Cronbach's alpha assessed internal consistency of the instrument; and correlation for relationship to other variables (quality of lecture). The 10-item Leppink Cognitive Load instrument was selected with attention to content and response process validity evidence. Internal structure of the instrument was good (Cronbach's alpha=0.80). Subscales performed well-intrinsic load (α=0.96, excellent), extrinsic load (α=0.89, good), and germane load (α=0.97, excellent). Five of the items were correlated with overall quality of lecture (p<0.05). The 10-item Cognitive Load instrument demonstrated good validity evidence to measure cognitive load and the subdomains of intrinsic, extraneous, and germane load. This instrument can be used to provide feedback to presenters to improve the cognitive load of their presentations.

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.

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).

In this article, we discuss cognitive load measurement techniques with regard to their contribution to cognitive load theory (CLT). CLT is concerned with the design of instructional methods that efficiently use people's limited cognitive processing capacity to apply acquired knowledge and skills to new situations (i.e., transfer). CLT is based on a cognitive architecture that consists of a limited working memory with partly independent processing units for visual and auditory information, which interacts with an unlimited long-term memory. These structures and functions of human cognitive architecture have been used to design a variety of novel efficient instructional methods. The associated research has shown that measures of cognitive load can reveal important information for CLT that is not necessarily reflected by traditional performance-based measures. Particularly, the combination of performance and cognitive load measures has been identified to constitute a reliable estimate of the mental efficiency of instructional methods. The discussion of previously used cognitive load measurement techniques and their role in the advancement of CLT is followed by a discussion of aspects of CLT that may benefit by measurement of cognitive load. Within the cognitive load framework, we also discuss some promising new techniques.