Alexander Renkl

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

Learning from Examples: Instructional Principles from the Worked Examples Research:

Worked examples are instructional devices that provide an experts problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom instruction and the broader educational research community. A frame- work for organizing the findings of this research is proposed, leading to instructional design principles. For instance, one instructional design principle suggests that effective examples have highly integrated components. They employ multiple modalities in presentation and emphasize conceptual structure by labeling or segmenting. At the lesson level, effective instruction employs multiple examples for each conceptual problem type, varies example formats within problem type, and employs surface features to signal deep structure. Also, examples should be presented in close proximity to matched practice problems. More- over, learners can be encouraged through direct training or by the structure of the worked example to actively self:explain examples. Worked examples are associated with early stages of skill development, but the design principles are relevant to constructivist research and teaching.

Transitioning from Studying Examples to Solving Problems: Effects of Self-Explanation Prompts and Fading Worked-Out Steps.

Although research has demonstrated that successively fading or successively removing more and more worked-out solution steps as learners transition from relying on examples to independent problem solving reliably fosters performance on near-transfer tasks—relative to example–problem pairs—this effect is not reliable on far-transfer tasks. To address this, the authors combined fading with the introduction of prompts designed to encourage learners to identify the underlying principle illustrated in each worked-out solution step. Across 2 experiments, this combination produced medium to large effects on near and far transfer without requiring additional time on task. Thus, the instructional procedure is highly recommendable because it (a) is relatively straightforward to implement, (b) does not prolong learning time, and (c) fosters both near- and far-transfer performance. Worked-out examples typically consist of a problem formulation, solution steps, and the final answer itself. Research indicates that exposure to worked-out examples is critical when learners are in the initial stages of learning a new cognitive skill in wellstructured domains such as mathematics, physics, and computer programming (Anderson, Fincham, & Douglass, 1997). Moreover, studies performed by Sweller and his colleagues (e.g., Sweller &

Worked-out examples: instructional explanations support learning by self-explanations

Abstract Learning from worked-out examples is very effective for initial skill acquisition, at least when learners actively explain the solution steps in the examples to themselves. However, learning solely on the basis of self-explanations is connected with several restrictions, even when effective self-explaining is trained or elicited. Therefore, a coherent set of principles for integrating instructional explanations into learning via self-explanations was developed, implemented, and tested. An experiment with a control group (without instructional explanations; 20 student teachers) and an experimental group (with instructional explanations; n=28) was conducted. The results showed that the instructional explanations had a positive effect on learning, at least under certain conditions. In addition, deficits in the use of the instructional explanations were identified and possible ways to improve the effectiveness of the instructional explanations were proposed.

From Example Study to Problem Solving: Smooth Transitions Help Learning

Abstract Research has shown that it is effective to combine example study and problem solving in the initial acquisition of cognitive skills. Present methods for combining these learning modes are static, however, and do not support a transition from example study in early stages of skill acquisition to later problem solving. Against this background, the authors proposed a successive integration of problem-solving elements into example study until the learners solved problems on their own (i.e., complete example increasingly more incomplete examples problem to-be-solved). The authors tested the effectiveness of such a fading procedure against the traditional method of using example-problem pairs. In a field experiment and in 2 more controlled laboratory experiments, the authors found that (a) the fading procedure fosters learning, at least when near transfer performance is considered; (b) the number of problem-solving errors during learning plays a role in mediating this effect; and (c) it is more favorable to fade out worked-out solution steps in a backward manner (omitting the last solution steps first) as compared with a forward manner (omitting the first solution steps first).

Toward an Instructionally Oriented Theory of Example-Based Learning.

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (b) phases of skill acquisition, and (c) learning processes. The prescriptive subtheory proposes instructional principles that make full exploitation of the potential of example-based learning possible.

Why Instructional Explanations Often Do Not Work: A Framework for Understanding the Effectiveness of Instructional Explanations

Although explanations are a common means of instruction, research shows that they often do not contribute to learning. To unravel the factors giving rise to the ineffectiveness of instructional explanations, we propose a framework that brings together empirical work on instructional explanations from a variety of research fields, including classroom instruction, tutoring, cooperative learning, cognitive skill acquisition, learning from texts, computer-supported learning, and multimedia learning. In our framework, we identify the distinctive characteristics of instructional explanations, present general guidelines for designing instructional explanations, and describe factors influencing both the generation and use of instructional explanations. It is argued that future research should uncover in more detail the interrelations between the different aspects of providing and using instructional explanations and their specific effects on learning.

The worked-example effect: Not an artefact of lousy control conditions

Recently it has been argued that the worked-example effect, as postulated by Cognitive Load Theory, might only occur when compared to unsupported problem-solving, but not when compared to well-supported or tutored problem-solving as instantiated, for example, in Cognitive Tutors. In two experiments, we compared a standard Cognitive Tutor with a version that was enriched with faded worked examples. In Experiment 1, students in the example condition needed less learning time to acquire a comparable amount of procedural skills and conceptual understanding. In Experiment 2, the efficiency advantage was replicated. In addition, students in the example condition acquired a deeper conceptual understanding. The present findings demonstrate that the worked-example effect is indeed robust and can be found even when compared to well-supported learning by problem-solving.

Learning mathematics from worked-out examples : Analyzing and fostering self-explanations

Recent research has shown that learning from worked-out examples is of major importance for initial skill acquisition in well-structured domains such as mathematics. However, only those learners who actively process the presented examples profit noticeably from this learning mode. Specifically, the learning outcomes depend on how well the learners explain the solution steps presented in the examples to themselves (‘self-explanation effect”). In a series of studies on learning mathematics from examples, learners’ spontaneous self-explanations and instructional means used to encourage self-explanations were investigated. In this research, the following main findings were obtained. Most learners were rather passive with respect to their spontaneous self-explanations. Among the active and successful learners, two subgroups employing different self-explanation styles could be identified. With regard to the instructional means used to induce effective example processing, it turned out that to employ “learning by teaching” in order to stimulate explanation activities was of very limited use. Attempts to directly train for or elicit certain types of self-explanations were more successful. However, even in the latter case, self-explanations had inherent deficits (e.g., proneness to errors). Thus, we sought to design learning arrangements that try to integrate self-explanations with well-timed and well-adapted instructional explanations (e.g., from tutors) in order to enhance students’ problem-solving skills.

Conditions and Effects of Example Elaboration.

The re-analysis is aimed at extending earlier findings on example-based learning and to draw consequences for further research and instructional practice. Based on an earlier experimental study on learning with worked-out examples in the domain of accounting (n=56 students of a vocational school), we re-analysed the effects of an intervention means (elaboration training) on learning behaviour (aspects of example elaboration). In a further step, different ways of dealing with worked-out examples (elaboration profiles) were identified and related to the subsequent learning outcomes and to the learners’ mental effort. We explained the formation of different elaboration profiles by various learner characteristics (prior knowledge, interest and tolerance of ambiguity). It was shown that the elaboration training had a positive effect on the quality of example elaboration. Two ways of learning-effective example elaboration were identified. Subgroups of learners with different elaboration profiles differed in mental effort and in tolerance of ambiguity, but not with respect to prior knowledge and interest. High tolerance of ambiguity concurred with high mental effort and resulted in effective, metacognitively accentuated example elaboration. By integrating cognitive and motivational characteristics in the analysis of elaboration patterns, new insights concerning example-based learning and the role mental effort plays in this context could be won. Consequences for research and instructional practice were drawn.  2001 Elsevier Science Ltd. All rights reserved.