In addition to how the individual attends to and perceives information, another variable that can affect memory development is the amount of information that must be processed. This section features excerpts from Artino (2008) and discusses brain functions and limitations from the perspective of cognitive load theory (CLT) . Posed by Sweller, van Merriënboer, and Paas (1998), cognitive load theory suggests that learning is more efficient when aligned with how humans naturally process information, or their cognitive architecture. Evaluating working memory, attention, and perception through this lens can help you better understand the importance of applying learning strategies to achieve successful knowledge acquisition.
This section features excerpts from Artino (2008). Artino’s focus is on CLT’s applicability to instructional design, but consider the benefits to your own career goals. Understanding how differing instructional strategies can affect learning enables us to better manage our own educational journeys and support others during their journeys. If you are a manager or supervisor, you may be called on to train your staff; cognitive load theory provides ideas on how best to do this, providing a more effective learning experience. Thus, when instructional design is addressed, remember that it is not only for the educator. It is well suited for parents, managers, business owners, counselors, and helping advocates in many fields.
As part of a larger class of limited capacity theories (Goldman, 1991), cognitive load theory (CLT) provides a framework for designing instructional materials. The basic premise of CLT is that learners have a working memory with very limited capacity when dealing with new information (Sweller et al., 1998). Moreover, CLT assumes that learners have “effectively unlimited long-term memory holding cognitive schemas that vary in their degree of complexity and automation” (van Merriënboer & Ayres, 2005, p. 6). The implication of these assumptions is that learning will be hindered if instructional materials overwhelm a learner’s limited working memory resources. Accordingly, early CLT research focused on identifying instructional designs that can effectively reduce unnecessary cognitive burden on working memory, thereby supporting improved learning efficiency (van Merriënboer & Sweller, 2005). More recently, cognitive load theorists have shifted their attention to how learner characteristics, such as prior knowledge and motivational beliefs, interact with instructional designs to influence the effectiveness of CLT methods (Moreno, 2006). [. . .]
Components of Working Memory
According to Sweller et al. (1998), humans are conscious only of the information currently being held and processed in working memory and are essentially oblivious to the enormous amount of information stored in long-term memory. Furthermore, as was mentioned in Chapter 2, when handling new information, working memory is severely limited in both capacity and duration; that is, working memory can hold only about seven (plus or minus two) items, or chunks of information, at a time (Miller, 1956). Additionally, when processing information (i.e., organizing, contrasting, and comparing), rather than just storing it, humans are probably able to manage only two or three items of information simultaneously, depending on the type of processing required (Kirschner, Sweller, & Clark, 2006). Finally, new information held in working memory, if not rehearsed, is lost within about 15 to 30 seconds (Driscoll, 2005).
Another important characteristic of working memory is that its capacity is distributed over two, partially independent processors (Sweller et al., 1998). This dual-processing assumption is based, in part, on Pavio’s (1986) dualcoding theory and Baddeley’s (1998) theory of working memory, both of which suggest that there are two separate channels for processing visual and auditory information. The implication of this dual-processing model is that limited working memory capacity can be effectively expanded by utilizing both visual and auditory channels rather than either processing channel alone (Sweller et al., 1998). Known as the modality effect (Mousavi, Low, & Sweller, 1995), this result has important implications for instructional designers.
Long-Term Memory, Schema Construction, and Schema Automation
Unlike working memory, the capacity of long-term memory is essentially limitless. Furthermore, information held in long-term memory is organized and stored in the form of domain-specific knowledge structures known as schemata (van Merriënboer & Ayres, 2005). Schemata categorize elements of information according to how they will be used, thereby facilitating schema accessibility later when they are needed for related tasks (Sweller et al., 1998). Thus, from the CLT perspective, “human expertise comes from knowledge stored in these schemata, not from an ability to engage in reasoning with many elements that have not been organized in long-term memory” (van Merriënboer & Sweller, 2005, p. 149).
As indicated by Sweller (2004), the relationship between working memory and schemata stored in long-term memory may be even more important than the processing limitations of working memory. This is because schemata do more than just organize and store information; they also effectively augment working memory capacity. Although working memory can hold only a limited number of items at a time, the size and complexity of those elements are unlimited (Sweller et al., 1998). Therefore, complex schemata consisting of huge arrays of interrelated elements can be held in working memory as a single entity. As a result, a student dealing with previously learned material that has been stored in long-term memory is, in effect, freed from the processing limitations of working memory—limitations that apply only to novel materials that have no associated schemata (Kirschner et al., 2006). In sum, schemata serve two functions in CLT: the organization and storage of information in long-term memory and the expansion of working memory capacity (Sweller et al., 1998).
Automation is another critical component of schema construction. Automation occurs when information stored in schemata can be processed automatically and without conscious effort, thereby freeing up working memory resources. Constructed schemata become automated after extensive practice, and existing schemata will vary in their degree of automation (van Merriënboer & Sweller, 2005). As Sweller et al. (1998) described, “with automation, familiar tasks are performed accurately and fluidly, whereas unfamiliar tasks—that partially require the automated processes—can be learned with maximum efficiency because maximum working memory capacity is available” (p. 258). On the other hand, without schema automation, a previously encountered task might be completed, but the process will likely be slow and awkward. Furthermore, consistent with CLT, entirely new tasks may be impossible to complete until prerequisite skills have been automated because there simply may not be enough working memory capacity available for learning (van Merriënboer & Sweller, 2005). Ultimately, in view of these theoretical assumptions, schema construction and automation become the major goals for instructional systems that are developed from a cognitive load perspective (Sweller et al., 1998).
Different Types of Cognitive Load
Although schemata are stored in long-term memory, their construction occurs in working memory. Specifically, when learning new material, students must attend to and manipulate relevant pieces of information in working memory before it can be stored in long-term memory (Sweller et al., 1998). Consequently, of primary importance to cognitive load theorists is the ease with which information can be processed in working memory, that is, the cognitive load imposed on working memory. According to CLT, three different types of cognitive load can be distinguished (recall these were first discussed in section 2.2):
1. Intrinsic cognitive load—refers to the number of elements that must be processed simultaneously in working memory for schema construction (i.e., element interactivity). Element interactivity is dependent on both the complexity of the to-be-learned material and the learners’ expertise (i.e., their schema availability and automaticity; Gerjets & Scheiter, 2003). Stated another way, “intrinsic cognitive load through element interactivity is determined by an interaction between the nature of the material being learned and the expertise of the learners” (Sweller et al., 1998, p. 262).
2. Extraneous cognitive load—also known as ineffective cognitive load—is the result of instructional techniques that require learners to engage in working memory activities that are not directly related to schema construction or automation (Sweller, 1994). Much of the early research in CLT revealed that many commonly used instructional designs require learners to use cognitive resources that are not related to, or helpful for, learning (e.g., searching for information that is needed to complete a learning task). Furthermore, because intrinsic cognitive load due to element interactivity and extraneous cognitive load due to instructional design are additive (Sweller et al., 1998), the end result may be fewer cognitive resources left in working memory to devote to schema construction and automation during learning. Consequently, learning may suffer (Sweller, 1994).
3. Germane cognitive load—also known as effective cognitive load—is the result of beneficial cognitive processes such as abstractions and elaborations that are promoted by the instructional presentation (Gerjets & Scheiter, 2003). When intrinsic and extraneous cognitive load leave sufficient working memory resources, learners may “invest extra effort in processes that are directly relevant to learning, such as schema construction. These processes also increase cognitive load, but it is germane cognitive load that contributes to, rather than interferes with, learning” (Sweller et al., 1998, p. 264).
In summary, based on the cognitive demands imposed on working memory from the three sources of cognitive load, CLT suggests that instructional designers should focus on two tasks: (a) reduce extraneous cognitive load, and (b) encourage learners to apply available resources to advanced cognitive processes that are associated with germane cognitive load (Gerjets & Scheiter, 2003). [. . .]
Source: Artino, A. R., Jr. (2008). Cognitive load theory and the role of learner experience: An abbreviated review for educational practitioners. AACE Journal, 16(4), 425–439. Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).
As Sweller suggested, CLT offers a better understanding of how processing limitations can decrease one’s ability to learn effectively. By understanding limited capacity, learners can take more ownership for adapting learning experiences to more effectively support individual learning. Being able to determine what items in the instruction are most important to attend to can increase one’s own ability to regulate learning. If you are delivering instruction, whether you are creating training or help guides, working with a child, or working in a therapeutic setting, understanding CLT can help you better design the content and methods that will benefit the learner.