Cognitive Load Theory Research Research Articles

Discussion of the special issue on cognitive load theory.

The present Special Issue contains 9 papers exploring novel cognitive load theory research questions. To provide a discussion of the 9 articles in this Special Issue of the Journal and to highlight the strengths and weaknesses of the papers and indicate opportunities for future studies. Briefly summarizes cognitive load theory in its current version followed by the discussion of the 9 individual papers.

Measuring Cognitive Load: Are There More Valid Alternatives to Likert Rating Scales?

Cognitive load researchers have used varying subjective techniques based on rating scales to quantify experienced cognitive load. Although it is generally assumed that subjects can introspect on their cognitive processes and have no difficulty in assigning numerical values to the imposed cognitive load, little is known about how visual characteristics of the rating scales influence the validity of the cognitive load measure. In this study we look at validity of four subjective rating scales (within groups) differing in visual appearance by participants rating perceived difficulty and invested mental effort in response to working on simple and complex weekday problems. We used two numerical scales (the nine-point Likert scale most often used in Cognitive load theory research and a Visual Analogue Scale ranging between 0–100%) and two pictorial scales (a scale consisting of emoticons ranging from a relaxed blue-colored face to a stressed red-colored face and an “embodied” scale picturing nine depicted weights from 1–9 kg). Results suggest that numerical scales better reflect cognitive processes underlying complex problem solving while pictorial scales Underlying simple problem solving. This study adds to the discussion on the challenges to quantify cognitive load through various measurement methods and whether subtleties in measurements could influence research findings.

The construct of cognitive load in interpreting and its measurement

ABSTRACTThis article attempts to contribute to the study of cognitive load in interpreting by presenting an in-depth discussion of the construct and its measurement. Borrowing from mental workload and Cognitive Load Theory research, cognitive load in interpreting is defined as the portion of an interpreter’s limited cognitive capacity devoted to performing an interpreting task in a certain environment. The article then presents a methodological discussion on how to measure cognitive load, focusing on the major categories of cognitive load measures and a series of selection criteria. Considering that existing studies only focus on simultaneous interpreting, the article introduces some techniques that are potentially useful for measuring cognitive load in consecutive interpreting, including the NASA Task Load Index (NASA-TLX), pen recording, and eye tracking.

Managing One's Own Cognitive Load when Evidence of Split Attention is Present

SummaryThere is an increasing expectation in tertiary education that students take control of their own learning, experience independence and manage their own cognition. This research sought to investigate techniques for university students to manage their own cognitive load. This paper presents two experiments conducted with postgraduate university students enrolled in an educational psychology subject in an Australian university. A total of 86 students participated in Experiment 1 and 85 in Experiment 2. The results of both experiments show that it is possible to instruct students on how to self‐manage split attention. Furthermore, the findings from Experiment 2 show that students can transfer skills of split‐attention management when provided with new instructional materials. The implications for this unique direction of cognitive load theory research are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Contemporary cognitive load theory research: The good, the bad and the ugly

This paper reviews the 16 contributions of the special issue entitled Current Research in Cognitive Load Theory. Each paper is briefly summarized and some critical comments made. The overall collection is then discussed in terms of the positive contributions they make to the field of learning and instruction, and cognitive load theory in particular (the good), as well as problematical issues such as unresolved explanations and conflicting results (the bad) and the special case of measuring cognitive load (the ugly).

The Scientific Value of Cognitive Load Theory: A Research Agenda Based on the Structuralist View of Theories

In this paper, two methodological perspectives are used to elaborate on the value of cognitive load theory (CLT) as a scientific theory. According to the more traditional critical rationalism of Karl Popper, CLT cannot be considered a scientific theory because some of its fundamental assumptions cannot be tested empirically and are thus not falsifiable. According to the structuralist view of theories introduced by Joseph D. Sneed, a theory may be considered scientific even if it comprises nontestable fundamental assumptions. Rather, the scientific value of a theory results from the holistic empirical content of the overall theory net built around fundamental assumptions and from the successful applications of this theory net to explain and predict empirical findings. This latter view is helpful to explicate some implicit methodological assumptions of CLT research and to avoid the potential circularity of CLT’s fundamental assumptions. Additionally, the structuralist view of theories can be directly used to derive a research agenda for the future development of CLT.

Research on cognitive load theory and its design implications for e-learning

This introduction to the special issue provides a context for the contributing articles. for readers who are not familiar with cognitive load theory (CLT), it provides a very brief description of assumptions regarding memory systems and learning processes, different types of cognitive load (intrinsic, extraneous, and germane), and design implications. Whereas traditional CLT research focused on instructional methods to decrease extraneous cognitive load that is not directly relevant for learning, contributions to this special issue represent wider perspectives that reflect new developments in CLT. These articles have been organized into three categories: (a) methods to decrease intrinsic cognitive load, and deal with high-element interactivity materials, (b) methods to increase germane cognitive load that is directly relevant for learning, and (c) methods to deal with differences in learner's individual levels of expertise and expertise development. To conclude, design implications for (adaptive) e-learning are discussed.

Cognitive Load Theory: Instructional Implications of the Interaction between Information Structures and Cognitive Architecture

Within the cognitive load theory research community it has become customary to report theoretical and empirical progress at international conference symposia and in special issues of journals (e.g., Educational Psychologist 2003; Learning and Instruction 2002). The continuation of this custom at the 10th European Conference for Research on Learning and Instruction, 2003, in Padova, Italy, has materialized in this special issue of Instructional Science on the instructional implications of the interaction between information structures and cognitive architecture. Since the 1990s this interaction has begun to emerge as an explicit field of study for instructional designers and researchers. In this introduction, we describe the basics of cognitive load theory, sketch the origins of the instructional implications, introduce the articles accepted for this special issue as a representative sample of current research in this area, and discuss the overall results in the context of the theory. It is generally accepted that performance degrades at the cognitive load extremes of either excessively low load (underload) or excessively high load (overload) – see e.g., Teigen (1994). Under conditions of both underload and overload, learners may cease to learn. So, whereas learning situations with low processing demands will benefit from practice conditions that increase the load and challenge the learner, learning situations with an extremely high load will benefit from practice conditions that reduce the load to more manageable levels (Wulf and Shea 2002). Cognitive load theory (CLT; Paas, Renkl and Sweller 2003; Sweller 1988, 1999) is mainly concerned with the learning of complex cognitive tasks, where learners are often overwhelmed by the number of information elements and their interactions that need to be processed simultaneously before meaningful learning can commence. Instructional control of this (too) high load, in order to attain meaningful learning in complex cognitive domains, has

Cognitive Load Theory as a Tool for Expertise Development

This special issue is dedicated to recentdevelopments within cognitive load theory (CLT)and identifies some instructional implicationsof the interaction between informationstructures and cognitive architecture. Thepresent article discusses the different studiesin this special issue. An important conclusionis that recent CLT research recognizes thelearners' level of expertise as an importantfactor mediating the relation between cognitivearchitecture, information structures, andlearning outcomes. In this context, we discussthe role of expertise in CLT research and therole of CLT in expertise research. Based onthis discussion, the use of CLT as a tool todevelop expertise is identified as a verypromising approach.