Cognitive Load Theory: A Broader View on the Role of Memory in Learning and Education

According to cognitive load theory (CLT), the limitations of working memory (WM) in the learning of new tasks together with its ability to cooperate with an unlimited long-term memory (LTM) for familiar tasks enable human beings to deal effectively with complex problems and acquire highly complex knowledge and skills. With regard to WM, CLT has focused to a large extent on learning task characteristics, and to a lesser extent on learner characteristics to manage WM load and optimize learning through instructional design. With regard to LTM, explanations of human learning and cognition have mainly focused on domain-general skills, instead of domain-specific knowledge held in LTM. The contributions to this special issue provide a broader cognitive load view on the role of memory in learning and education by presenting the historical roots and conceptual development of the concept of WM, as well as the theoretical and practical implications of current debates about WM mechanisms (Cowan 2014), by presenting an updated model of cognitive load in which the physical learning environment is considered a distinct causal factor for WM load (Choi et al. 2014), by an experimental demonstration of the effects of persistent pain on the available WM resources for learning (Smith and Ayres 2014), and by using aspects of evolutionary educational psychology to argue for the primacy of domain-specific knowledge in human cognition (Tricot and Sweller 2014).     

A Reconsideration of Cognitive Load Theory

Cognitive load theory has been very influential in educational psychology during the last decade in providing guidelines for instructional design. Whereas numerous empirical studies have used it as a theoretical framework, a closer analysis reveals some fundamental conceptual problems within the theory. Various generalizations of empirical findings become questionable because the theory allows different and contradicting possibilities to explain some empirical results. The article investigates these theoretical problems by analyzing the conceptual distinctions between different kinds of cognitive load. It emphasizes that reduction of cognitive load can sometimes impair learning rather than enhancing it. Cognitive load theory is reconsidered both from the perspective of Vygotski’s concept of the zone of proximal development and from the perspective of research on implicit learning. Task performance and learning are considered as related, but nevertheless fundamentally different processes. Conclusions are drawn for the further development of the theory as well as for empirical research and instructional practice.

Using Electroencephalography to Measure Cognitive Load

Application of physiological methods, in particular electroencephalography (EEG), offers new and promising approaches to educational psychology research. EEG is identified as a physiological index that can serve as an online, continuous measure of cognitive load detecting subtle fluctuations in instantaneous load, which can help explain effects of instructional interventions when measures of overall cognitive load fail to reflect such differences in cognitive processing. This paper presents a review of seminal literature on the use of continuous EEG to measure cognitive load and describes two case studies on learning from hypertext and multimedia that employed EEG methodology to collect and analyze cognitive load data.     

Bridging Cognitive Load and Self-Regulated Learning Research: A complementary approach to contemporary issues in educational research

The aim of this Introduction to the Special Issue ‘Bridging Cognitive Load and Self-Regulated Learning Research’ is to explore how cognitive load theory, which is particularly relevant for how learners deal with complex information, and self-regulated learning theory, which is particularly relevant for how learners use information to monitor and control their learning, can be combined into one joint research paradigm that is relevant for contemporary and future developments in education. The first two sections introduce cognitive load theory and self-regulated learning theory. The third section discusses the main similarities and differences between the theories, and describes how the cue-utilization framework can be used as the basis for a joint research paradigm. The main idea postulated is that new instructional methods should help learners identify diagnostic cues in available information that provide an accurate indication of where learners stand in relation to criterion task performance. Use of these diagnostic cues when monitoring learning will lead to better regulation of learning activities and of mental resources allocated, and thus to more efficient learning and higher learning outcomes.In the fourth section, the six studies and two commentaries presented in this special issue are positioned within this paradigm. In the fifth and final section, a common research agenda based on the joint CLT-SRL paradigm is sketched and its relevance for future developments is explained. The studies presented in this special issue and the two commentaries, which complete the Special Issue, should be seen as a very first step in executing this research agenda.     

Learners Emphasize Their Intrinsic Load if Asked About It First: Communicative Aspects of Cognitive Load Measurement

Cognitive load measurement is an important aspect of educational research. Current cognitive load surveys differentiate between intrinsic cognitive load (resulting from the complexity of learning materials) and their extraneous cognitive load (which is increased by a demanding design). In two studies, order effects of cognitive load subscales are demonstrated. Asking learners regarding their intrinsic load first increases their responses concerning this type of load, with little effect on extraneous load ratings. This effect can be replicated even when extraneous load is intentionally induced. This finding has important implications for cognitive load research, as the order of surveys appears to bias cognitive load ratings. As most cognitive load research is conducted to find ways of reducing extraneous load, it may be reasonable to carefully consider whether and when intrinsic load items are included in studies. Generally, the results show that study participants seem to emphasize certain demands, similar to a dialogue.     

Differentiating Different Types of Cognitive Load: a Comparison of Different Measures

Recent studies about learning and instruction use cognitive load measurement to pay attention to the human cognitive resources and to the consumption of these resources during the learning process. In order to validate different measures of cognitive load for different cognitive load factors, the present study compares three different methods of objective cognitive load measurement and one subjective method. An experimental three-group design (N = 78) was used, with exposure to seductive details (extraneous cognitive load factor), mental animation tasks (germane cognitive load factor), or the basic learning instruction (control group). Cognitive load was measured by the rhythm method (Park and Brünken 2015), the index of cognitive activity (ICA) (Marshall 2007), and the subjective ratings of mental effort and task difficulty (Paas 1992). Eye-tracking data were used to analyze the attention allocation and as an indicator for cognitive activity. The results show a significantly higher cognitive load for the mental animation group in contrast to the control and the seductive detail group, indicated by rhythm method and subjective ratings, as well as a higher cognitive activity, indicated by eye tracking. Furthermore, the mental animation group shows significantly higher comprehension performance in contrast to the seductive detail group and significantly higher transfer performance in contrast to the control group. The ICA values showed no significant differences in cognitive load. The results provide evidence for the benefits of combining eye-tracking analysis and the results of cognitive load ratings or secondary task performance for a direct and continuous cognitive load assessment and for a differentiating access to the single cognitive load factors.     

Facilitating Flexible Problem Solving: A Cognitive Load Perspective

The development of flexible, transferable problem-solving skills is an important aim of contemporary educational systems. Since processing limitations of our mind represent a major factor influencing any meaningful learning, the acquisition of flexible problem-solving skills needs to be based on known characteristics of our cognitive architecture in order to be effective and efficient. This paper takes a closer look at the processes involved in the acquisition of flexible problem-solving skills within a cognitive load framework. It concludes that (1) cognitive load theory can benefit from putting more emphasis on generalized knowledge structures; (2) there are tradeoffs between generality and power with respect to specific versus generalized knowledge structures; (3) generalized knowledge structures of “medium” generality are essential for flexible expertise; and (4) cognitive load theory could provide a valuable framework for considering essential attributes of flexible expertise.     

Guidelines for Choosing Cognitive Load Measures in Perceptually Rich Environments

Cognitive load measurement is a methodological issue of high importance in all learning settings involving a high perceptual richness, such as virtual and augmented reality. As a result of the growing number of cognitive load measurement methods and surveys, it can be difficult to choose the optimal measurement instrument for learning tasks in perceptually rich environments. Current research suggests that survey-based methods do not necessarily have to be less valid than physiological measures. Furthermore, in several studies, single-item measures of cognitive load have shown a high negative correspondence with learning outcomes. A trend toward a more fine-grained analysis of different components of cognitive load can be observed, but the ability to detect cognitive load depends on selecting an appropriate survey for the specific task. Based on this narrative overview on current developments in cognitive load measurement, recommendations for deciding on a cognitive load measurement method are given.     

Toward a Synthesis of Cognitive Load Theory, Four-Component Instructional Design, and Self-Directed Learning

This article explores the opportunities to apply cognitive load theory and four-component instructional design to self-directed learning. Learning tasks are defined as containing three elements: learners must (a) perform the tasks, (b) assess their task performance, and (c) select future tasks for improving their performance. Principles to manage intrinsic and extraneous load for performing learning tasks, such as simple-to-complex ordering and fading-guidance strategies, are also applicable to assessing performance and selecting tasks. Moreover, principles to increase germane load, such as high variability and self-explanation prompts, are also applicable to assessment and selection. It is concluded that cognitive load theory and four-component instructional design provide a solid basis for a research program on self-directed learning.     

A Cognitive Load Approach to Collaborative Learning: United Brains for Complex Tasks

This article presents a review of research comparing the effectiveness of individual learning environments with collaborative learning environments. In reviewing the literature, it was determined that there is no clear and unequivocal picture of how, when, and why the effectiveness of these two approaches to learning differ, a result which may be due to differing complexities of the learning tasks used in the research and the concomitant load imposed on the learner’s cognitive system. Based upon cognitive load theory, it is argued that learning by an individual becomes less effective and efficient than learning by a group of individuals as task complexity increases. Dividing the processing of information across individuals is useful when the cognitive load is high because it allows information to be divided across a larger reservoir of cognitive capacity. Although such division requires that information be recombined and that processing be coordinated, under high load conditions, these costs are minimal compared to the gain achieved by this division of labor. In contrast, under low load conditions, an individual can adequately carry out the required processing activities, and the costs of recombination and coordination are relatively more substantial. Implications of these ideas for research and practice of collaborative learning are discussed.     

An Evolutionary Upgrade of Cognitive Load Theory: Using the Human Motor System and Collaboration to Support the Learning of Complex Cognitive Tasks

Cognitive load theory is intended to provide instructional strategies derived from experimental, cognitive load effects. Each effect is based on our knowledge of human cognitive architecture, primarily the limited capacity and duration of a human working memory. These limitations are ameliorated by changes in long-term memory associated with learning. Initially, cognitive load theory's view of human cognitive architecture was assumed to apply to all categories of information. Based on Geary’s (Educational Psychologist 43, 179–195 2008; 2011) evolutionary account of educational psychology, this interpretation of human cognitive architecture requires amendment. Working memory limitations may be critical only when acquiring novel information based on culturally important knowledge that we have not specifically evolved to acquire. Cultural knowledge is known as biologically secondary information. Working memory limitations may have reduced significance when acquiring novel information that the human brain specifically has evolved to process, known as biologically primary information. If biologically primary information is less affected by working memory limitations than biologically secondary information, it may be advantageous to use primary information to assist in the acquisition of secondary information. In this article, we suggest that several cognitive load effects rely on biologically primary knowledge being used to facilitate the acquisition of biologically secondary knowledge. We indicate how incorporating an evolutionary view of human cognitive architecture can provide cognitive load researchers with novel perspectives of their findings and discuss some of the practical implications of this view.     

Achieving Optimal Best: Instructional Efficiency and the Use of Cognitive Load Theory in Mathematical Problem Solving

We recently developed the Framework of Achievement Bests to explain the importance of effective functioning, personal growth, and enrichment of well-being experiences. This framework postulates a concept known as optimal achievement best, which stipulates the idea that individuals may, in general, strive to achieve personal outcomes, reflecting their maximum capabilities. Realistic achievement best, in contrast, indicates personal functioning that may show moderate capability without any aspiration, motivation, and/or effort expenditure. Furthermore, our conceptualization indicates the process of optimization, which involves the optimization of achievement of optimal best from realistic best.In this article, we explore the Framework of Achievement Bests by situating it within the context of student motivation. In our discussion of this theoretical orientation, we explore in detail the impact of instructional designs for effective mathematics learning as an optimizer of optimal achievement best. Our focus of examination of instructional designs is based, to a large extent, on cognitive load paradigm, theorized by Sweller and his colleagues. We contend that, in this case, cognitive load imposition plays a central role in the structure of instructional designs for effective learning, which could in turn influence individuals’ achievements of optimal best. This article, conceptual in nature, explores varying efficiencies of different instructional approaches, taking into consideration the potency of cognitive load imposition. Focusing on mathematical problem solving, we discuss the potentials for instructional approaches to influence individuals’ striving of optimal best from realistic best.     

Cognitive Load Theory: Advances in Research on Worked Examples, Animations, and Cognitive Load Measurement

The contributions to this special issue document some recent advances of cognitive load theory, and are based on contributions to the Third International Cognitive Load Theory Conference (2009), Heerlen, The Netherlands. The contributions focus on developments in example-based learning, amongst others on the effects of integrating worked examples in cognitive tutoring systems; specify the effects of transience on cognitive load and why segmentation may help counteract these effects in terms of the role of time in working memory load; and discuss the possibilities offered by electroencephalography (EEG) to provide a continuous and objective measure of cognitive load. This article provides a short introduction to the contributions in this issue.     

Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions

Traditionally, Cognitive Load Theory (CLT) has focused on instructional methods to decrease extraneous cognitive load so that available cognitive resources can be fully devoted to learning. This article strengthens the cognitive base of CLT by linking cognitive processes to the processes used by biological evolution. The article discusses recent developments in CLT related to the current view in instructional design that real-life tasks should be the driving force for complex learning. First, the complexity, or intrinsic cognitive load, of such tasks is often high so that new methods are needed to manage cognitive load. Second, complex learning is a lengthy process requiring learners motivational states and levels of expertise development to be taken into account. Third, this perspective requires more advanced methods to measure expertise and cognitive load so that instruction can be flexibly adapted to individual learners needs. Experimental studies are reviewed to illustrate these recent developments. Guidelines for future research are provided.     

行动研究:利用认知负荷理论调整阅读教学

阅读教学中所用材料给学生带来过高的认知负荷,严重妨碍学生语言习得。教师应利用认知心理学的认知负荷理论,围绕问题,设计不同方案,积极调置教学计划和课堂教学模式。在实施过程中收集数据,并最后对数据分析,得出结论:教师在教学过程中,应保证教学材料具有适当的认知负荷,这样才能使学生的学习达到最佳化。     

样例学习认知负荷理论的研究及其展望

文章回顾了样例学习研究的起源与发展,主要介绍了其理论依据——认知负荷理论,并分别围绕外在认知负荷、内在认知负荷以及相关认知负荷介绍了最新国内外样例学习的设计方法:材料的整合、子目标、不完整样例、错误样例、诱发自我解释问题、多种解题方法比较等,最后在现有研究成果上指出样例学习研究的发展趋势和有待进一步解决的问题。     

认知负荷理论与英语听力练习材料的设计

在语言收听过程中,记忆、尤其是瞬时记忆,是理解的前提。“认知负荷理论”吸收和应用了认知信息加工理论关于注意短时记忆的研究成果,认为学习者的工作记忆容量是有限的,工作记忆的限制性成为了学习的主要障碍;认为通过教学设计尽量减少学习任务中不必要的认知负荷,使工作记忆的容量更多地集中于将要学习的材料中,从而促进学习。文章探讨了在认知负荷理论指导下的英语听力练习材料的设计。     

Cognitive Load and Working Memory in Multimedia Learning: Conceptual and Measurement Issues

This article reviews contemporary research on multimedia learning that uses cognitive load theory as the major theoretical framework. In particular, we address the extent to which working memory has been conceptualized and measured in this research, what kind of subjective measures of cognitive load have been used and whether such measures are combined with other measures of cognitive load, and how results from subjective measures have been related to learning and achievement. The findings show that most of the reviewed studies did not include any clear conceptualization or measurement of working memory, used only general subjective measures containing one or very few items, and did not report findings consistent with the hypothesized relationship between cognitive load and multimedia learning. The findings are discussed in relation to the broader goal of improving research on cognitive load in the context of multimedia learning.     

Making the Black Box of Collaborative Learning Transparent: Combining Process-Oriented and Cognitive Load Approaches

Traditional research on collaborative learning employs a “black box” approach that makes it difficult to gain a deeper understanding of the differential effects of collaborative learning. To make the black box transparent, researchers have studied the process of collaboration, in order to establish which interaction features are likely to make learning more effective and efficient for group members. Although cognitive load theory has been developed in the context of individual learning situations, it may provide a promising new way of looking inside the black box, assuming that students working in groups have more processing capacity than students working individually. The aim of this article is to provide an overview of the process-oriented and cognitive-load approaches to conducting collaborative learning research, to highlight their respective advantages and disadvantages, and to suggest how they can be combined in order to address new research questions.     

基于认知负荷理论的“大学物理”教学

约翰·斯威勒提出的认知负荷理论,主张合理分配认知资源,对有效学习至关重要。如果教师在设计教学时能按照认知负荷理论,尽量减少学习任务中不必要的认知负荷,有助于提高教学的效果。因此,提出基于认知负荷理论的大学物理教学,可以通过物理知识的图表化、优化多媒体教学、融入物理学史等手段,管理内在认知负荷,降低外在认知负荷,增加相关认知负荷,以此减少学生的学习障碍,激发学生的学习兴趣,从而最终达到促进有效学习的目的。