Fred Paas

Cognitive Architecture and Instructional Design

Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it.

Cognitive Load Theory and Instructional Design: Recent Developments

Cognitive load theory (CLT) originated in the 1980s and underwent substantial development and expansion in the 1990s by researchers from around the globe. As the articles in this special issue demonstrate, it is a major theory providing a framework for investigations into cognitive processes and instructional design. By simultaneously considering the structure of information and the cognitive architecture that allows learners to process that information, cognitive load theorists have been able to generate a unique variety of new and sometimes counterintuitive instructional designs and procedures. The genesis of this special issue emerged from an international symposium on CLT that was organized at the 2001 Biannual Conference of the European Association for Research on Learning and Instruction, Fribourg, Switzerland. Most of the articles that follow are based on contributions to that symposium and discuss the most recent work carried out within the cognitive load framework. Before summarizing those articles, we provide a brief outline of CLT. Although the information that learners must process varies on many dimensions, the extent to which relevant elements interact is a critical feature. Information varies on a continuum from low to high in element interactivity. Each element of low-element interactivity material can be understood and learned individually without consideration of any other elements. Learning what the usual 12 function keys effect in a photo-editing program provides an example. Element interactivity is low because each item can be understood and learned without reference to any other items. In contrast, learning how to edit a photo on a computer provides an example of high-element interactivity. Changing the color tones, darkness, and contrast of the picture cannot be considered independently because they interact. The elements of high-element interactivity material can be learned individually, but they cannot be understood until all of the elements and their interactions are processed simultaneously. As a consequence, high-element interactivity material is difficult to understand. Element interactivity is the driver of our first category of cognitive load. That category is called intrinsic cognitive load because demands on working memory capacity imposed by element interactivity are intrinsic to the material being learned. Different materials differ in their levels of element interactivity and thus intrinsic cognitive load, and they cannot be altered by instructional manipulations; only a simpler learning task that omits some interacting elements can be chosen to reduce this type of load. The omission of essential, interacting elements will compromise sophisticated understanding but may be unavoidable with very complex, high-element interactivity tasks. Subsequent additions of omitted elements will permit understanding to occur. Simultaneous processing of all essential elements must occur eventually despite the high-intrinsic cognitive load because it is only then that understanding commences. One may argue that this aspect of the structure of information has driven the evolution of human cognitive architecture. An architecture is required that can handle high-element interactivity material. Human cognitive architecture met this requirement by its combination of working and long-term EDUCATIONAL PSYCHOLOGIST, 38(1), 1–4 Copyright

Cognitive load measurement as a means to advance cognitive load theory

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 peoples 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.

Training Strategies for Attaining Transfer of Problem-Solving Skill in Statistics: A Cognitive-Load Approach.

In statistical problems, the differential effects on training performance, transfer performance, and cognitive load were studied for 3 computer-based training strategies. The conventionaI, worked, and completion conditions emphasized, respectively, the solving of conventional problems, the study of worked-out problems, and the completion of partly worked-out problems. The relation between practice-problem the and transfer was expected to be mediated by cognitive load. It was hypothesized that practice with conventional problems would require more time and more effort during training and result in lower and more effort-demanding transfer performance than practice with worked-out or partly worked-out problems

Variability of worked examples and transfer of geometrical problem-solving skills : a cognitive-load approach

Four computer-based training strategies for geometrical problem solving in the domain of computer numerically controlled machinery programming were studied with regard to their effects on training performance, transfer performance, and cognitive load. A low- and a high-variability conventional condition, in which conventional practice problems had to be solved (followed by worked examples), were compared with a low- and a high-variability worked condition, in which worked examples had to be studied. Results showed that students who studied worked examples gained most from high-variability examples, invested less time and mental effort in practice, and attained better and less effort-demanding transfer performance than students who first attempted to solve conventional problems and then studied work examples.

The Efficiency of Instructional Conditions: An Approach to Combine Mental Effort and Performance Measures

This article reports on a calculational approach for combining measures of mental workload and task performance that allows one to obtain information on the relative efficiency of instructional conditions. The method is based on the standardization of raw scores for mental effort and task performance to z scores, which are displayed in a cross of axes. Relative condition efficiency is calculated as the perpendicular distance to the line that is assumed to represent an efficiency of zero. We conclude that the method for calculating and representing relative condition efficiency discussed here can be a valuable addition to research on the training and performance of complex cognitive tasks.

Measurement of cognitive load in instructional research.

The results of two of our recent empirical studies were considered to assess the usefulness of subjective ratings and cardiovascular measures of mental effort in instructional research. Based on its reliability and sensitivity, the subjective rating-scale technique met the requirements to be useful in instructional research whereas the cardiovascular technique did not. It was concluded that the usefulness of both measurement techniques in instructional research needs to be investigated further.

Instructional Efficiency: Revisiting the Original Construct in Educational Research

This article revisits Paas and Van Merriënboers (1993) measure of instructional efficiency, which can be applied by educational researchers to compare the effects of different instructional conditions on learning. This measure relied on performance and mental effort on the test, and as such gave an indication of the quality of learning outcomes. The acquisition of more (less) efficient cognitive schemata is indicated by combinations of high (low) performance and low (high) mental effort. This instructional efficiency measure has become widely adopted, but in an adapted form that incorporates mental effort invested in the learning phase instead of the test phase. This article demonstrates that the adaptation has important consequences for the construct of instructional efficiency and for the type of conclusions that can be drawn. Examples are given to illustrate the various implications of different combinations of mental effort and performance measures in the light of more contemporary developments in educational research.

Redirecting Learners' Attention during Training: Effects on Cognitive Load, Transfer Test Performance and Training Efficiency.

Cognitive load theory provides guidelines for improving the training of complex cognitive skills and their transfer to new situations. One guideline states that extraneous cognitive load that is irrelevant to the construction of cognitive schemata should be minimised. Experiment 1 (N=26) compares completion problems, conventional problems, and a learner-controlled condition in which learners may choose between problem formats. Completion problems decrease cognitive load during training and have a zero or positive effect on transfer performance. A second guideline states that germane cognitive load that is directly relevant to schema construction should be optimised. In Experiment 2 (N=69) practice schedules of either high or low contextual interference are compared (HCI and LCI). HCI increases cognitive load during training and shows a trend towards higher transfer performance. Experiment 3 (N=87) combines both guidelines in a factorial experiment with the factors problem format (completion vs. conventional) and contextual interference (HCI vs. LCI). It is hypothesised that redirecting attention from extraneous to germane processes will improve training efficiency, i.e. positively affect the balance between cognitive load during training and transfer test performance. In support of this hypothesis, it is found that the completion-HCI group shows highest training efficiency. But transfer test performance for this group is disappointing. The results are discussed in relation to the operationalisation of HCI in combination with completion problems.

A Motivational Perspective on the Relation between Mental Effort and Performance: Optimizing Learner Involvement in Instruction

Motivation can be identified as a dimension that determines learning success and causes the high dropout rate among online learners, especially in complex e-learning environments. It is argued that these learning environments represensent a new challenge to cognitive load researchers to investigate the motivational effects of instructional conditions and help instructional designers to predict which instructional configurations will maximize learning and transfer. Consistent with the efficiency perspective introduced by Paas and Van Merriënboer (1993), an alternative motivational perspective of the relation between mental effort and performance is presented. We propose a procedure to compute and visualize the differential effects of instructional conditions on learner motivation, and illustrate this procedure on the basis of an existing data set. Theoretical and practical implications of the motivational perspective are discussed.