Using Cognitive Principles to Improve Instructional Procedures

Recent cognitive research indicates that many commonly used instructional techniques are inadequate as they overload limited working memory and interfere with the two primary components of learning, namely, schema acquisition and automation. For example, data are available indicating that instructional formats which unnecessarily split attention between referring sources of information or contain additional redundant sources of information seriously interfere with the learning process (Chandler & Sweller, 1991; 1992; Sweller, Chandler, Tierney & Cooper, 1990). Several short term experiments and long term field studies in both educational and industrial settings have shown that alternative, cognitively based instructional packages designed to reduce the burden on working memory are superior to traditional techniques used by educators for generations. This paper explores the conditions under which these alternative instructional techniques are likely to be most beneficial. It suggests that when information has a high intellectual component, then the instructional format becomes critical and cognitively based instruction is highly effective. Where the intrinsic nature of the information imposes fewer intellectual demands, then the format of instruction is not as important (Sweller & Chandler, in press). A number of studies which support these hypotheses using computer based materials and other technical based equipment are discussed. Over the past few decades there have been clear indications of general agreement among cognitive theorists concerning many of the basic mechanisms involved in higher intellectual activities. While a broad model of human mental processing is gaining acceptance, many conventional instructional techniques violate the most basic assumptions of the model and may be inadequate. The research reported in this paper tests the viability of alternative instructional techniques, more in accord with our cognitive processes. Before detailing this research, I will discuss some widely accepted cognitive structures. Some Aspects of Human Cognitive Architecture Everything that we learn interacts with either working memory, long-term memory or both. Working memory is where current mental activity takes place. It is both limited in duration and critically, for present purposes, very limited in capacity (Miller, 1956; Simon, 1974). Modern conceptions of working memory (e.g. Baddeley, 1992) suggest that it is not a single, undifferentiated entity but rather, may consist of multiple stores. Separate auditory and visual stores have been suggested. Baddeley, for example, suggests a discrete auditory store for handling verbal information and a separate visual store for handling visual images. While modern accounts pay more attention to the processes involved in working memory instead of storage considerations (also see Halford, Wilson, Wiles & Stewart, in press), all still emphasise its very limited capacity. In contrast to working memory, long-term memory is huge, with unknown limits. Surprisingly, a full realisation of its more important characteristics such as its enormous capacity only occurred in the last two decades and is a major finding of the cognitive science revolution. We now know that many of the cognitive activities and skills that previously were thought to be strongly associated with working memory are a function of an enormous knowledge base held in long-term memory. For example, Since De Groot (1965) and Chase and Simon (1973), it has become apparent that the skill of chess masters does not derive from their use of working memory to engage in complex search activities. Rather, the use of long-term memory to recognise board configurations and remember the best moves associated with each configuration is the essential cognitive characteristic associated with problem solving skill. Simon and Gilmartin (1973) estimated that experts in complex areas such as chess have remembered tens of thousands of problem states and their associated moves. It is this store of information that is the source of expertise. Mechanisms of learning interact closely with working and long-term memory. When dealing with higher intellectual functioning, there are two mechanisms of learning that are critical: Automation and schema acquisition. Automatic processing (Shiffrin & Schneider, 1977; Schneider & Shiffrin, 1977; Kotovsky, Hayes & Simon, 1985) allows mental processes to occur smoothly, rapidly and without conscious control. It requires long periods of practice during which procedures can occur only under conscious control. Controlled processing places a burden on working memory that automatic processing reduces. A major function of automation seems to be to by-pass our limited capacity working memory. The second learning mechanism is schema acquisition which also by-passes working memory by using long-term memory. The concept of a schema, discussed by Bartlett (1932), is now widely used in cognitive psychology. Consequently, many varying definitions of a schema exist (e.g., Thorndyke & Hayes-Roth, 1979; Gick & Holyoak, 1983), each inevitably reflecting the theoretical perspective of the proposer. For the purposes of this paper, a schema will be viewed as a cognitive construct that categorises information according to the manner with which it is dealt. For example, we recognise common objects such as cats almost instantaneously because we do not need to process more than a small amount of what we see, with our schemas filling in the rest. We can rapidly read many different types of text because the marks on the page, even if unique in the case of handwriting, correspond to previously acquired schemas. Similarly, we can solve problems in areas in which we are expert because our schemas allow us to categorise each problem according to its solution moves. For example, consider the algebraic problem: 2y 3 = 5. A competent mathematics student will categorise this problem as one that first requires a number to be added to both sides (in this case 3) and then divided by a number (in this case 2). Notice that the problem is categorised according to solution mode, irrespective of the numerals and pronumerals involved in the problem. The schema required to solve this problem can be applied to solve an infinite number of expressions in this category (e.g., 3t 9 = 3 or 6h 1 = -7). (See Koedinger and Anderson, 1990 for a computational model demonstrating the use of schema based solutions in mathematical problem solving). The chess grand masters discussed above derived their expertise from their thousands of previously acquired schemas. Thus schemas, stored in long-term memory, obviate the need to process large amounts of information in limited capacity working memory. When automated, they permit us to deal with huge quantities of information quickly and easily in a manner that is impossible if we are forced to rely solely on working memory (see Low & Over, 1992 for techniques for detecting schemas). Instructional Consequences of our Cognitive Architecture This architecture has consequences for instructional design. Instruction should be directed to facilitating schema acquisition and automation without imposing a heavy cognitive load that exceeds the processing capacity of working memory. There are two general sources of cognitive load. Firstly, extraneous cognitive load can be brought about by instructional designs that require students to engage in activities irrelevant to schema acquisition and automation. Secondly, some subject matter, because of its nature, imposes an intrinsic cognitive load. This section is concerned with extraneous cognitive load that is amenable to reduction by appropriate instructional designs. Cognitive load theory (Sweller, 1988, 1989, 1993) has been used to identify some traditional instructional procedures that impose a heavy, extraneous cognitive load. Two of several effects (see Sweller & Chandler, 1991 for details of other effects) generated by this research have given rise to alternative instructional techniques designed to reduce extraneous cognitive load and facilitate schema acquisition and automation. These are very briefly summarised below. The Split-attention Effect. Research indicated that some worked examples and instructional materials are ineffective because they impose a heavy extraneous cognitive load. The conventional mode of presenting some worked examples and instructional materials require students to mentally integrate disparate sources of information before the example can be processed. For example, consider the conventional instructional format shown in figure 1. Before this electrical material can be understood, the diagram of the electrical appliance and associated statements must be mentally integrated and this continuous search and match process is mentally taxing. The heavy cognitive load is imposed solely because of the structure of the materials. Physically integrating the diagram and statements by placing the statements on the diagram eliminates the need for mental integration and thus reduces extraneous cognitive load (see figure 2). Tarmizi and Sweller (1988), using geometry, and Ward and Sweller (1990), using physics, demonstrated the superiority of integrated worked examples over conventional worked examples. --------------------------------------Insert figures 1 and 2 about here --------------------------------------Furthermore, Sweller, Chandler, Tierney and Cooper (1990), Chandler and Sweller (1991) and Chandler and Sweller (1992) found advantages for integrated instructional formats over conventional "split-source" formats using initial instructional materials in a wide variety of training areas in both educational and industrial settings. The fact that the effect was demonstrated in unrelated areas such as Numerical Control programming, electrical installation testing and human anatomy in both laboratory and long term field studies suggests that the eliminat