Cognitive Load Theory

Aim/Purpose: This study aims to analyze the cognitive load experienced by secondary school students in Biology within m-learning environments and its impact on learning performance. Background: Cognitive load has become a critical issue that schools need to address to ensure students can excel in their learning without being overwhelmed. While principles for reducing cognitive load have been extensively discussed in previous research, studies focusing on mobile learning (m-learning) for Biology among students in Malaysia remain limited. This study employed Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) to address this gap. By integrating four key principles—segmenting and pretraining, modality, redundancy, and seductive details—into m-learning tasks using the Successive Approximation Model (SAM1), this study aimed to reduce cognitive load and enhance students’ learning performance. Methodology: This study employed a quantitative approach using a randomized pre-test/post-test quasi-experimental design. Students were randomly assigned to either an intervention group (20 students) or a control group (18 students). The study was conducted over four weeks, comprising a three-week intervention period with a one-week interval. Statistical analyses, including independent t-tests, Mann-Whitney U tests, Quade ANCOVA, and Pearson correlation, were used to analyze the quantitative data. Qualitative feedback was analyzed using thematic analysis. Contribution: This study contributes by providing instructional design strategies that incorporate principles for reducing cognitive load in mobile learning for Biology. It also demonstrates how Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) can be effectively integrated. By examining the cognitive load experienced by secondary school students in m-learning environments, the study offers valuable insights for designing and implementing effective instructional strategies. Identifying the factors influencing cognitive load enables educators to develop targeted interventions that enhance learning experiences and optimize performance. Findings: The study indicated that the adoption of mobile learning tasks not only significantly reduced cognitive load but also corresponded to enhanced learning performance. Participants engaging in m-learning experienced lower cognitive load, which was positively associated with superior performance in learning tasks, emphasizing the beneficial impact of mobile learning on cognitive load management and academic achievement. Recommendations for Practitioners: Educators and instructional designers are encouraged to incorporate cognitive load principles into their instructional strategies and learning material design to enhance student performance. Policymakers should consider similar strategies to reduce the cognitive load for students in educational settings to improve learning outcomes. Recommendation for Researchers: Researchers are encouraged to replicate the design elements used in this study when developing mobile or online learning materials to reduce learners’ cognitive load and enhance their performance. They should also consider expanding this research to other topics, subjects, and educational levels to provide further insights and validate the effectiveness of these design elements across different contexts. Impact on Society: The findings of this study have significant implications for society, particularly in addressing mental health and stress issues among the younger generation. By identifying strategies to manage cognitive load and reduce stress in online learning environments, the study provides valuable insights for educators, parents, and policymakers. These strategies can help mitigate the adverse effects of cognitive overload, improve learning experiences, and promote better mental well-being. Additionally, the study’s recommendations can guide the development of more effective and supportive learning environments, contributing to overall societal well-being and academic success. Future Research: Future studies could explore cognitive load beyond the intrinsic and extraneous components focused on in this study, examining additional elements within the frameworks of cognitive load theory and multimedia learning. In addition to using the cognitive load questionnaire, exploring other measurement tools could ensure a more comprehensive understanding of cognitive load. Future research might also consider enriching mobile learning tasks by diversifying subject matter and conducting longitudinal cohort studies. Such studies could provide valuable insights into memory retention over extended periods, aiding in optimizing mobile learning frameworks and enhancing educational experiences.

This study explored the impact of online lectures that were developed using principles of cognitive load theory (CLT) and cognitive theory of multimedia learning (CTML) on health profession students' lecture comprehension, cognitive load, cognitive engagement, and intrinsic motivation in learning. A total of 215 first-year undergraduate students in medical, dentistry, and nutrition programs participated in this pre-post quasi experimental study. The students attended a typical face-to-face lecture on Day-1 of the intervention, followed by a CLT-based online lecture 8 weeks thereafter. Their comprehension of the lecture topics was measured through pre- and post-lecture assessments, and their cognitive load, cognitive engagement, and motivation were measured immediately after each lecture session. The analysis revealed that the CLT-based online lectures promoted the students' comprehension of the lecture content (p < 0.001), self-perceived learning (p < 0.001), engagement toward the learning material, and motivation to learn (p = 0.025). It was also effective at reducing the students' intrinsic and extraneous cognitive loads (p < 0.001). Hence, designing online lectures using CLT and CTML principles could be an effective method to promote students' knowledge and comprehension, cognitive engagement, and learning motivation. However, further research is needed to investigate the applicability and impact of CLT-based online lectures in non-health profession disciplines.

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.

Cognitive load theory (CLT) provides a rich framework to inform instructional design. Despite the applicability of CLT to simulation-based medical training, findings from multimedia learning have not been consistently replicated in this context. This lack of transferability may be related to issues in measuring cognitive load (CL) during simulation. The authors conducted a review of CLT studies across simulation training contexts to assess the validity evidence for different CL measures. PRISMA standards were followed. For 48 studies selected from a search of MEDLINE, EMBASE, PsycInfo, CINAHL, and ERIC databases, information was extracted about study aims, methods, validity evidence of measures, and findings. Studies were categorized on the basis of findings and prevalence of validity evidence collected, and statistical comparisons between measurement types and research domains were pursued. CL during simulation training has been measured in diverse populations including medical trainees, pilots, and university students. Most studies (71%; 34) used self-report measures; others included secondary task performance, physiological indices, and observer ratings. Correlations between CL and learning varied from positive to negative. Overall validity evidence for CL measures was low (mean score 1.55/5). Studies reporting greater validity evidence were more likely to report that high CL impaired learning. The authors found evidence that inconsistent correlations between CL and learning may be related to issues of validity in CL measures. Further research would benefit from rigorous documentation of validity and from triangulating measures of CL. This can better inform CLT instructional design for simulation-based medical training.

This chapter outlines the cognitive architecture used by cognitive load theory, and provides a general indicator of its relevance to instructional design issues associated with multimedia instruction. Cognitive load theory has been one of the theories used to integrate our knowledge of human cognitive structures and instructional design principles. It is concerned with the elements of that theory and its general implications for multimedia learning, specifically words presented in spoken or written form along with pictures or diagrams. The chapter considers categories of knowledge from an evolutionary perspective, followed by an outline of those aspects of human cognitive architecture relevant to instructional concerns, also considered from an evolutionary perspective. The chapter discusses three categories of cognitive load: intrinsic cognitive load, extraneous cognitive load and germane cognitive load. All categories of cognitive load are concerned with the acquisition, storage and use of biologically secondary information.

The APA/APAGS Award for Distinguished Graduate Student in Professional Psychology is awarded on an annual basis by the APA Board of Professional Affairs and the American Psychological Association of Graduate Students (APAGS) to a graduate student who has demonstrated outstanding practice and application of psychology. A qualified candidate must demonstrate exemplary performance in working with an underserved population in an applied setting or have developed an innovative method for delivering health services to an underserved population. When two graduate students are equally qualified, the award may be shared. The 2025 recipients of the APA/APAGS Award for Distinguished Graduate Student in Professional Psychology were selected by the 2024 Board of Professional Affairs. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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.

Despite the overwhelming use of tests and questionnaires, the psychometric models for constructing these instruments are often poorly understood, leading to suboptimal measurement. Measurement Models for Psychological Attributes is a comprehensive and accessible treatment of the common and the less than common measurement models for the social, behavioral, and health sciences. The monograph explains the adequate use of measurement models for test construction, points out their merits and drawbacks, and critically discusses topics that have raised and continue to raise controversy. Because introductory texts on statistics and psychometrics are sufficient to understand its content, the monograph may be used in advanced courses on applied psychometrics and is attractive to both researchers and graduate students in psychology, education, sociology, political science, medicine and marketing, policy research, and opinion research.

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.

There have been many advances over the past four or five decades in understanding brain architecture, and how the process of learning aligns with this architecture. One of the more interesting results has been that of John Sweller [1] and his theory of cognitive load. Sweller identifies the task of learning as effecting change in long-term memory. This long-term memory is, in his view, almost limitless. The problem lies in working, or short-term memory, which has a bottleneck of around five items. Any instructional mode which places too many items into working memory will be, at best, inefficient, and at worse, pointless. It is interesting to note that over the same decades new instructional modes, such as Problem-Based Learning (PBL) have become popular [2]. Sweller, and others such as Paul Kirschner [3], argue that PBL cannot work as advertised, as the student is faced with too high a cognitive load; they can either learn how to solve the problem, or learn the underlying concepts, but not both. This paper outlines the theoretical background to this issue, and presents an intervention undertaken over the last decade in TU DUBLIN to devise new instructional modes which take account of cognitive load problems, whilst maintaining some of the advantages and benefits of PBL. This intervention initially followed the ideas of Louis Bucciarelli [4] of MIT on open design in Engineering education but was later adjusted to take into account the ideas of Kirschner on minimizing cognitive load in developing problem-solving skills.

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.

Enhancing patient education in multiple myeloma - The intersection of cognitive load and socio-emotional adaptation theory.

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.

Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which can involve selective attention to new information that matches existing beliefs. In accordance with principles of least action related to management of cognitive effort, another alternative is to give up trying to understand new information with high cognitive load. In either case, high cognitive load can limit potential for understanding of new information and learning from new information. Cognitive Load Theory (CLT) provides a framework for relating the characteristics of information to human cognitive load. Although CLT has been developed through more than three decades of scientific research, it has not been applied comprehensively to improve the explainability, transparency, interpretability, and shared interpretability (ETISI) of machine learning models and their outputs. Here, in order to illustrate the broad relevance of CLT to ETISI, it is applied to analyze a type of hybrid machine learning called Algebraic Machine Learning (AML). This is the example because AML has characteristics that offer high potential for ETISI. However, application of CLT reveals potential for high cognitive load that can limit ETISI even when AML is used in conjunction with decision trees. Following the AML example, the general relevance of CLT to machine learning ETISI is discussed with the examples of SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and the Contextual Importance and Utility (CIU) method. Overall, it is argued in this Perspective paper that CLT can provide science-based design principles that can contribute to improving the ETISI of all types of machine learning.

This article analyses the field of research on information and communication technology (ICT) in education. It reviews the results of ICT researches carried out in the world. First, the article presents the retrospective of researches on ICT implementation into education. Summarizing them, it reviews main research findings in five areas of ICT implementation: 1) the impact of ICT use on students achievements and attitudes; 2) the impact of ICT use on teachers; 3) the effect of teachers' factors and instructional methods on ICT efficiency; 4) the influence of infrastructure and organizational factors on ICT efficiency; 5) the effect of specific software design features. Then, the paper reviews the main recent and future directions of researches on ICT in education, in the world. Finally, it shortly reviews the most important future directions of ICT research and development in Lithuania. The article concludes that in spite of numerous ICT studies worldwide the national research and experiments have to be carried out in order to implement ICT in an appropriate for Lithuania and efficient way.