Managing Cognitive Load in Verbal and Pictorial Representations

Chapter VI describes specific evidence-based methods for managing cognitive load in verbal and pictorial information representations. According to the major forms of memory storage, there are verbal and pictorial representational modes, whereas according to major forms of sensory input, there are auditory and visual information modalities. The chapter will consider sources of cognitive load involving different modes and modalities of multimedia information presentations. When learners process text and visuals that could not be understood in isolation, the process of integrating verbal and pictorial representations is required for comprehension. When text and pictures are not appropriately located close to each other or not synchronized in time, integrating these referring sources of information may increase working memory load and inhibit learning. Instructional design techniques dealing with such split attention situations may enhance learning. Reducing split-attention in paper-based and on-screen text and graphics was one of the first and most commonly mentioned applications of cognitive load theory. Using dualmode presentations that involve different processing channels of human cognitive system is an alternative approach to dealing with split attention situations. This chapter discusses means for coordinating verbal and pictorial sources of information in space and time, eliminating redundant components of presentations, segmenting instructional presentations in units that could be processed with less cognitive load, and other techniques. The chapter also describes interactions between instructional efficiency of different formats of multimedia presentations and levels of learner expertise in specific task domains.

Evaluation of Cognitive Load

Availability of valid and usable measures of cognitive load involved in learning is essential for providing support for cognitive load-based explanations of the effects predicted and described in cognitive load theory as well as for general evaluation of learning conditions. Besides, the evaluation of cognitive load may provide another indicator of levels of learner expertise in addition to performance scores. As mentioned before, due to the available schematic knowledge base, more knowledgeable learners are expected to perform their tasks with lower mental effort than novices. Even though simple subjective rating scales remain the most often used measures of cognitive load imposed by instructional materials, new more sophisticated techniques are being developed, especially in multimodal environments associated with performance of complex cognitive tasks. This chapter provides a brief overview of traditional, as well as some novel methods for measuring and evaluating cognitive load. Some recently developed approaches to using these measures in estimating instructional efficiency of learning environments are also discussed.

Human Cognitive Processes

One of the major components of our cognitive architecture, working memory, becomes overloaded if more than a few chunks of information are processed simultaneously. For example, we all experience this cognitive overload when trying to keep in memory an unfamiliar telephone number or add two four-digit numbers in the absence of a pen and paper. Similar in nature processing limitations of working memory represent a major factor influencing the effectiveness of human learning and performance, particularly in complex environments that require concurrent performance of multiple tasks. The learner prior domain-specific knowledge structures and associated levels of expertise are considered as means of reducing these limitations and guiding high-level knowledge-based cognitive activities. One of the most important results of studies in human cognition is that the available knowledge is a single most significant learner cognitive characteristic that influences learning and cognitive performance. Understanding the key role of long-term memory knowledge base in our cognition is important to the successful management of cognitive load in multimedia learning.

The Expertise Reversal Effect

Cognitive studies of expertise that were reviewed in Chapter I indicated that prior knowledge is the most important 1earner characteristic that influences learning processes. Recently, it has been established that learning procedures and techniques that are beneficial for learners with low levels of prior knowledge may become relatively inefficient for more knowledgeable learners due to cognitive activities that consume additional working memory resources. This expertise reversal effect could be related to aptitude-treatment interactions (interactions between learning outcomes of different instructional treatments and student aptitudes) that were actively investigated in 1960-70s. The learner level of prior knowledge or level of expertise is the aptitude of interest in this case. The effect is explained by the cognitive overload that more knowledgeable learners may experience due to processing redundant for these learners instructional components (as compared to information without redundancy). As a consequence, instructional outcomes of different multimedia learning formats and procedures are always relative to levels of learner task-specific expertise. This chapter describes cognitive processes that cause expertise reversal effect and major instructional implications of this effect. The chapter provides a review of empirical evidence obtained in the original longitudinal studies of the effect, the expertise reversal for methods of enhancing essential cognitive load, and expertise reversal phenomena when learning from textual and hypertextual materials. The chapter also describes relations between the expertise reversal effect and studies of Aptitude-Treatment Interactions. Additional empirical evidence for the effect in other areas will be described in the following chapters in Section 2 of the book.

Managing Cognitive Load in Interactive Multimedia

Most sophisticated multimedia learning environments include various interactivity features. Interactive multimedia learning environments respond dynamically to learner specific actions. Such environments support active, learner-engaged forms of learning that are expected to promote deep cognitive processes and result in active construction and acquisition of new knowledge. Spector, Christensen, Sioutine, and McCormack (2001) noted that interactivity is the most critical feature of technology-enhanced learning environments. They summarized the relevant conclusions addressed in the research literature in this area as follows: 1. “Doing goes hand-in-hand with learning: learners learn what they do. 2. As learning environments provide more and more opportunities for active learner participation, they tend to promote learning; too many opportunities for interaction, however, can lead to confusion and disorientation. 3. Cognitive engagement with the subject material is vital for learning. 4. Opportunities for reflection generally improve learning. 5. Informative feedback is a necessary part of meaningful cognitive engagement; advanced learners may be able to generate their own feedback (a metacognitive skill)” (p. 522).

Assessment of Task-Specific Expertise

Main implication of the expertise reversal effect is the need to tailor instructional techniques and procedures to changing levels of learner expertise in a specific task domain. In order to design adaptive procedures capable of tailoring instruction in real-time, it is necessary to have online measures of learner expertise. Such measures should be rapid enough to be used in real time. At the same time, they need to have sufficient diagnostic power to detect different levels of task-specific expertise. One of the previously mentioned reasons for low practical applicability of the results of studies in Aptitude-Treatment Interactions were inadequate aptitude measures. Most of the assessment methods used in those studies were psychometric instruments designed for selection purposes (e.g., large batteries of aptitude tests based on artificially simplified tasks administered mostly in laboratory conditions). Another suggested reason was unsuitability of those methods for dynamic, real-time applications while learners proceeded through a single learning session. This chapter describes a rapid diagnostic approach to the assessment of learner task-specific expertise that has been intentionally designed for rapid online application in adaptive learning environments. The method was developed using an analogy to experimental procedures applied in classical studies of chess expertise mentioned in Chapter I. In those studies, realistic board configurations were briefly presented for subsequent replications. With the described diagnostic approach, learners are briefly presented with a problem situation and required to indicate their first solution step in this problem situation or to rapidly verify suggested steps at various stages of a problem solution procedure.

Adaptive Procedures for Efficient Learning

The rapid diagnostic approach to evaluating levels of learner task-specific expertise was introduced in Chapter IV and used in several studies that were subsequently described throughout this book. The rapid diagnostic techniques (first-step method and rapid verification technique) were instrumental in investigating some instances of the expertise reversal effect and in optimizing levels of cognitive load in faded worked example procedures (Section II and Chapter XI). This chapter describes some specific adaptive procedures based on rapid diagnostic methods for evaluating ongoing levels of learner task specific expertise. Two specific approaches to the design of adaptive instruction are considered, adaptive procedures based on rapid measures of performance and adaptive procedures based on combined measures of performance and cognitive load (efficiency measures). The expertise reversal effect established interactions between learner levels of task-specific expertise and effectiveness of different instructional methods. The major instructional implication of this effect is the need to tailor instructional methods and procedures to dynamically changing levels of learner expertise in a specific class of tasks within a domain. The rapid diagnostic approach was successfully used for real-time evaluation of levels of learner task-specific expertise in adaptive online tutorials in the domains of linear algebra equations (Kalyuga & Sweller, 2004; 2005) and vector addition motion problems in kinematics (Kalyuga, 2006) for high school students. Both first step diagnostic method and rapid verification technique were applied in adaptive procedures. According to the rapid assessment-based tailoring approach, these tutorials provided dynamic selection of levels of instructional guidance that were optimal for learners with different levels of expertise based on real-time online measures of these levels. The general designs of those studies were similar. In learner-adapted groups, at the beginning of training sessions, each student was provided with an appropriate level of instructional guidance according to the outcome of the initial rapid pretest. Then during the session, depending on the outcomes of the ongoing rapid tests, the student was allowed to proceed to the next learning stage or was required to repeat the same stage and then take the rapid test again. At each subsequent stage, a lower level of guidance was provided to learners (e.g., worked-out components of solution procedures were gradually omitted and progressively replaced with problem solving steps), and a higher level of the rapid diagnostic tasks was used at the end of the stage. In control non-adapted groups, learners either studied all tasks that were included in the corresponding stages of the training session of their yoked participants, or were required to study the whole set of tasks available in the tutorial.

Adapting Levels of Instructional Support to Optimize Learning Complex Cognitive Skills

This chapter describes some specific adaptive procedures for tailoring levels of instructional guidance to individual levels of learner task-specific expertise to optimize cognitive resources available to learning. Recent studies in expertise reversal effect that were reviewed in previous chapters indicate that instructional design principles that benefit low-knowledge users may disadvantage more experienced ones. This reversal in the relative effectiveness of different instructional methods is due to the increase in cognitive load that is required for integration of presented supporting information with learners’ available knowledge structures. The major implication of these findings for multimedia design is the need to tailor levels of instructional support to individual levels of learner task-specific expertise. The procedures for adapting levels of instructional guidance suggested in this chapter have been developed in conjunction with empirically established interactions between levels of learner expertise and optimal instructional techniques and procedures. The chapter starts with the description of the processes and approaches to learning complex cognitive skills. The appropriate design models for learning complex skills are reviewed and different ways of varying levels of learner control in such models are described. The relations between levels of learner task-specific expertise and optimal levels of instructional guidance are then discussed. Also, empirical studies of the expertise reversal for instructional guidance and sequencing of learning tasks are reviewed. The completion tasks and faded worked examples are specific instructional methods used in the described studies for managing levels of instructional guidance in adaptive learning environments. Real-time monitoring of levels of learner task-specific expertise using rapid cognitive diagnostic methods was used in some of these studies.

Tailoring Multimedia Environments to Learner Cognitive Characteristics

Personalized adaptive multimedia environments provide individual learners or learner groups with experience that is specifically tailored to them. To achieve effective personalization, a variety of information about the learner is required. Tailoring multimedia environments to individual learner cognitive characteristics is becoming a major means for achieving a true learner-centered experience for learners through their interaction with multiple content sources, presentation formats, and delivery means. Personalized multimedia environments are capable of realizing advanced learning and instruction strategies based on a continuous process of adaptation between the learners and instructional systems. This adaptation process could be accomplished through personalized interaction and adaptive presentation of content, learner feedback, adaptive navigation and search, and different adaptation methodologies. As was mentioned in earlier chapters of this book, a major instructional implication of the expertise reversal effect is the need to tailor dynamically instructional techniques and procedures, levels of instructional guidance to current levels of learner task-specific expertise. In online multimedia instructional systems, the levels of learner task-specific expertise change as students develop more experience in a specific task domain. Therefore, the tailoring process needs to be dynamic, i.e. consider learner levels of expertise in real time as they gradually change during the learning sessions. This Chapter describes general approaches to the design of adaptive learning environments from the perspective of tailoring learning procedures and techniques to individual cognitive characteristics of learners. Studies in aptitude-treatment interactions offered a possible approach to adaptive instruction. Intelligent tutoring systems and adaptive web-based hypermedia systems use learner models to tailor learning tasks and instructional content to individual learner characteristics. This approach accommodates learner characteristics (e.g., knowledge, interests, goals) into explicit learner models that guide adaptive procedures. On the other hand, advisement and adaptive guidance approaches realize a greater learner control over instruction and provide individualized prescriptive information in the form of recommended material and tasks based on learner past performance.

Cognitive Load Theory

Cognitive load theory is a learning and instruction theory that describes instructional design implications of human cognitive architecture outlined in the previous chapter. Based on these theoretically and empirically established instructional consequences (usually referred to as cognitive load effects or principles), the theory makes specific prescriptions on managing cognitive load in learning and instruction. The theory distinguishes several different types or sources of cognitive load (e.g., effective and ineffective load; intrinsic, extraneous, and germane load) that are associated with different instructional implications and cognitive load effects. This chapter analyzes cognitive load factors that could potentially influence efficiency of interactive multimedia applications (e.g., levels of element interactivity, spatial and temporal configurations of instructional presentations, redundant representational formats, levels of learner prior experience in a task domain). Basic assumptions of cognitive theory of multimedia learning are discussed. The chapter starts with the description of the sources of cognitive load followed by an overview of the major cognitive load effects.