Overcoming Older Adult Barriers to Learning Through an Understanding of Perspectives on Human Information Processing

Abstract

The importance to older adults of being able to learn to perform activities related to leisure, work, or daily living for maintaining independence and a good quality of life is unquestionable. An appreciation of the challenges that older adults face during learning, as well as insights into ways to help them overcome these challenges, whether through design or instruction, can be obtained through a basic understanding of human information processing. The purpose of this article is to facilitate this understanding within the context of older adult learning. The article begins with an overview of the human information-processing system and cognitive limitations and capabilities associated with aging. Critical components of the information-processing system are then revisited in greater detail from the perspective of older adult information-processing capabilities. Concepts are illustrated through examples to demonstrate how older adults could make more efficient use of their information-processing resources during learning.

Introduction

The ability for older adults to learn to perform various activities, whether related to leisure, work, or daily living, is essential to their independence and quality of life. Lying at the heart of such learning is information processing (Wickens & Carswell, 2006). Those responsible for ensuring effective learning by older adults, including designers and instructors, can benefit immensely from a basic understanding of human information processing, an appreciation of normal age-related declines in information-processing abilities, and strategies for circumventing these limitations and exploiting older adult information-processing capabilities.

The purpose of this article is to facilitate this understanding within the context of older adult learning. Toward this end, an overview of a model of human information processing is presented, with emphasis given to some of the essential elements that comprise this model. This is followed by a discussion of cognitive abilities related to information processing from an aging perspective. The key components of the information-processing model are then revisited within the context of how older adult limitations and capabilities in information processing can impact their learning, and through examples that demonstrate instructional design strategies for promoting more effective learning by older adults.

The justification for linking the assumptions implicit to a theoretical model of human information processing to instructional design strategies that could benefit older adult learners derives from two well-established foundations in behavioral science. The first of these encompasses theories and models of instructional design and learning that incorporate precepts for making optimal use of information-processing resources while minimizing, through misdirected instructional strategies, the depletion of these resources. These theories include Sweller’s (1994, 2005) cognitive load theory, multimedia instructional design models (Mayer & Moreno, 2003; Moreno, 2006), and the four-component instructional design (4C/ID) model (van Merriënboer, Clark, & Croock, 2002). The second foundation addresses normal age-related declines in these information-processing resources, which is a cornerstone of a number of theories of cognitive aging (e.g., Park et al., 2002; Salthouse, 1994).

Designing instruction for older adults that considers human information processing is also, in our view, in accord with supporting successful aging (SA). An influential perspective on the concept of SA is the MacArthur model (Rowe & Kahn, 1987, 1997) that encompasses three principal components: having a low risk of disease and disease-related disability, maintaining high mental and physical functionality, and continuing life-engagement activities including social relations and productive work. Newer ideas about SA include the need to incorporate a life-course perspective, in which active involvement and adaptive regulation (i.e., life-span control) and the ability to continue in one’s developmental processes and aspirations are highlighted (Heckhausen, Wrosch, & Schulz, 2010; Stowe & Cooney, 2015). In this regard, new technologies that are diffusing at an extraordinary pace can empower older adults with opportunities for social engagement, improved health, enhanced mobility, lifelong learning, and other benefits (Berkowsky, Sharit, & Czaja, 2018)—provided that designers can account for normal age-related declines in information-processing abilities in their designs and instructional methods.

Similarly, appropriate instructional designs can provide older adults with opportunities for pursuing productive work activities. This broadens the concept of SA to include social factors that acknowledge the importance of aging for the sustainability of the future workforce and its economic productivity (Rowe & Kahn, 2015). In fact, many countries with aging populations, including the United States, are looking for ways to reintegrate older individuals back into the workforce (World Economic Forum, 2017) and could benefit from the application of instructional programs that are more compatible with the information-processing capabilities of older individuals. A unique example of the benefits that such instructional approaches may bring is the joint initiative established in 2015 by Google, Intel, and TATA (an Indian manufacturing company) referred to as Internet Saathi (Friends of the Internet). In this program, which is part of the Womenwill global initiative of Google (www.womenwill.com/programs/internetsaathi), women who receive training on use of the Internet become local agents who facilitate digital literacy in their villages by instructing women there about the benefits of the Internet and the services they can use for their various needs through the use of Internet-enabled devices. The instruction these women, including older women, receive also provides them with employment opportunities (Gadgets 360, 2017), and can potentially benefit from strategies that consider older adult information-processing capabilities.

A Stage-Based Model of Human Information Processing

Although there are several approaches to information processing, a stage-based approach will be presented in which information is conceived as passing through a finite number of discrete stages (Wickens & Carswell, 2006). This approach to information processing is chosen as it is not only well suited for describing processes underlying learning, but it is also consistent with the cognitive aging literature that has identified various age-related declines that occur in abilities that support information processing (Park et al., 2002).

Figure 1 depicts several component processes—sometimes referred to as processing resources—within a stage-based model of the human information-processing system (Wickens, Gordon Becker, Liu, & Lee, 2004). A key resource is attention—the pool or supply of mental effort or energy that people possess and which can be allocated to other processing resources. Overall, the model is conceptualized in terms of three stages of information processing: perceptual encoding, central processing, and responding, although the flow of information processing does not necessarily have to follow that order. For example, it could be initiated as a thought process instigated by directing attention to working memory to secure needed knowledge from long-term memory (see Figure 1).

Figure 1.

A stage-based model of human information processing in which information processing is depicted as a flow of information between various information stores (shaded) and processing resources associated with these stores. Working memory both stores and processes information.

Source. Adapted from Wickens, Gordon Becker, Liu, and Lee (2004).

The sensory information received by our body’s various receptor cells collectively comprise the sensory register or sensory memory component of the model, which has tremendous storage capacity but only for a brief time (e.g., visual, 1 s; auditory, 3–5 s). For sensory information to warrant further processing, attention would typically need to be directed toward it. For example, top-down selective attention could be applied based on expectations of what, where, and when this information is to appear or on how important this information is believed to be. Attention could also be captured due to bottom-up factors associated with the external features of the sensory information, such as its salience. Attention can also be divided among the processing resources, for example, when it is necessary to manage multiple tasks.

Once sensory information (e.g., printed text, pictures, or spoken words) has received attention, the process of perception enables meaning of that information to be extracted. During new learning, this often requires comparison of that information to knowledge that one has available in long-term memory (LTM). Conversely, if the incoming information is very familiar, its processing by the learner may become so automatic that little or no attention may need to be directed toward it (Figure 1).

What typically occurs during the initial stages of instruction is that the information will require further processing in working memory (WM), the processing resource that represents the human’s short-term or temporary memory storage system. WM keeps information active while it is being used or is needed. It is a kind of workbench of consciousness in which people visualize, plan, compare, evaluate, transform, problem solve, and make decisions. Directing sufficient attention to this information—sometimes referred to as maintenance rehearsal—enables it to become encoded in LTM; otherwise, this information can decay rapidly.

There are two critical constraints associated with WM: the capacity constraint, which limits the amount of information that can be handled, and the time constraint, which dictates how long the information can be kept active if attention is not allocated to it. Because learning can be influenced by WM capacity, it is useful to consider the unit of WM space. This unit is often referred to as a chunk and is defined by the physical and cognitive properties that bind the items comprising the chunk together (Wickens et al., 2004). An icon with an associated text label or three parts comprising an exercise step are examples of a possible chunk of information. Chunking refers to processing multiple pieces of meaningfully associated information as a unified entity. It allows for easier rehearsal of material, which increases the likelihood of its transfer to and retention within LTM.

Three subcomponents of WM that were originally proposed by Baddeley (1986) have potentially important implications for older adult learning capabilities: a phonological loop, which holds and keeps verbal information active, either vocally or subvocally, whether this information is visual (e.g., printed text) or auditory (e.g., speech); a visuospatial sketchpad, which holds visuospatial information (e.g., pictures or pattern-based verbal stimuli) in an analog form, helping to ensure the formation and manipulation of mental images; and a central executive attention control system that coordinates information from these two memory storage subsystems. It is also responsible for thought processes underlying activities within WM through the selection of certain streams of incoming information and the rejection of others, as well as the selection and manipulation of information in LTM.

LTM stores information in multitudes of clusters or associations. When different pieces of information (which can be text, sounds, or images) are processed at the same time in WM, as typically occurs during instruction, these items of information become associated together in memory. Pieces of information that are meaningful when considered together can then form the basis for later reactivation from LTM. Meaningful associations between items stored in LTM are often referred to as associative networks. These are, in turn, often further differentiated as semantic networks (which are based on semantic knowledge), schemas (when the information is organized around a topic), or mental models (when schemas are associated with how a product or system works).

The availability of information from LTM so that it can be used by WM—for instance, to support ongoing instruction—is influenced by two major factors: the strength of the pieces of information, or item strength, and the strength of the association of that information with other items in LTM, or associative strength. The more frequently information has been activated (e.g., the rehearsal of a rule), the stronger is the memory trace. Also, the more recently information has been activated, the easier will be its retrieval. Weak associative strength implies that when we try to activate or retrieve a target item of information by first activating something with which the item has an association, the activation does not sufficiently spread to the target associative items.

Finally, depending on the situation, some type of response, either manual or voice, may be required. The selection of these responses may be directly linked to perception (i.e., they have become automatic), or they may be determined following WM and LTM processing (Figure 1). Actions may also generate new information to be sensed and perceived (the feedback loop).

Normal Age-Related Declines in Information-Processing Capabilities

Older adults represent a very heterogeneous group, implying that age-related changes in cognitive-processing abilities are highly variable, both within and across older individuals. In characterizing these changes, a distinction is often made between fluid and crystallized cognitive abilities. Fluid abilities reflect those abilities that are involved in new learning or problem-solving performance. WM and speed of processing represent two important fluid abilities, with WM believed to be the fundamental source of age-related deficits in many cognitive tasks, including those involving decision making and problem solving (Glisky, 2007). Speed of processing includes perceptual speed, which refers to how quickly one can identify or compare visual patterns or symbols or deal with numerical information.

Slowed information processing is a relatively robust descriptor of age-related declines in cognitive task performance (Cerella, 1985; Cerella & Hale, 1994), and one can argue that speed of processing permeates all information-processing activities as they all take time (Li, Lindenberger, & Sikström, 2001). In fact, Salthouse (1994, 1996) has suggested that age-related deficits in WM and other cognitive tasks can be explained in terms of a general slowing of information processing. However, others have suggested (e.g., Park et al., 1996) that speed of processing and WM provide independent contributions to cognitive task performance.

Generally, fluid cognitive abilities peak somewhere in the 20s or 30s, and then gradually decline with increasing age, although there is great variability in both the number of abilities that show decline and the extent of such declines (Czaja et al., 2006). In contrast, crystallized intelligence abilities, such as vocabulary or other markers of acquired or world knowledge across one’s lifetime, remain relatively stable or increase throughout the life span, at least until about 70 years of age. Such normal age-related changes in cognitive abilities are depicted in Figure 2, which includes several different markers of both fluid and crystallized cognitive abilities (Park et al., 2002).

Figure 2.

Illustrations across the adult lifespan of performance declining on measures of fluid cognitive abilities (e.g., working memory, speed of processing) but being preserved on measures of crystallized cognitive abilities (e.g., verbal knowledge Shipley vocabulary).

Source. Park et al. (2002), reprinted with permission from APA.

Minimizing Information-Processing Limitations in Older Learners

In the ensuing subsections, processing resources comprising the human information-processing system are revisited with the objective of more explicitly identifying strategies for enhancing learning by older adults.

Attention and Perceptual Processing

People tend to direct their attention toward entities that are salient, are located where they are most likely to be found, and which have value for the task on hand. As older adults generally possess less attentional capacity, it is especially important during the early stages of instruction to emphasize material that should be the focus of attention while minimizing distracting information (Sweller, 1994) or situations that require divided attention. For example, the ability to locate critical buttons (on a health care device) or links (on a webpage) would depend on making these stimuli sufficiently salient. Similarly, emphasizing where different types of useful information might be found or appear creates expectancies that can prevent, for example, behaviors such as random visual searches that require high expenditures of mental effort. Likewise, emphasizing the value of certain sources of information, for example, that the home page of a website provides a full listing of health-management tools, creates more efficient use of limited resources of attention.

Example:

Consider designing instruction for older learners to perform a job that involves processing insurance claims, where customer claims may be consistently classified into general types of categories for further processing. Suppose the claims contained within a given category may not only follow different rules for their processing, but these rules may change depending on the circumstances associated with the claim. It is especially important for older learners that they are provided with appropriate strategies to enable them to identify when consistent or inconsistent mappings of claims to actions are required. The goal in designing instruction for these learners would be to help them form different schemas, perhaps in the form of rules, which could serve to differentiate classifications that are based on consistent mappings from those that highlight the basis for the alternative ways in which a claim in a given category may get processed.

When engaged in perceptual processing during learning, another challenge for older learners occurs when they encounter unfamiliar stimuli, events, or other content. For example, this may pertain to images and language that appear on the displays of vehicles related to the use of advanced driver assistance, phone, or entertainment systems or which may be contained in printed or online instructional manuals. The normal human reaction to such circumstances is to decompose the information into more elemental features (Wickens et al., 2004) that can hopefully, through comparison with knowledge stored in LTM, trigger more understanding of the information. This type of processing, however, can be very effortful for older adults. These kinds of issues can be potentially overcome by obtaining some type of appraisal that helps to establish the boundaries of knowledge (and skills) that the learner possesses, which can then be used as a basis for instructional design. Another strategy for minimizing the negative effects associated with older adults encountering unfamiliar content during learning is to direct increased emphasis to the sequence in which the learning content is presented to the older learner to ensure that prerequisite knowledge and skills are identified and taught first (Reigeluth, 2007).

Example:

Consider designing an instructional program for older workers for performing a light industrial task involving the assembly of a small electronic product. The initial instruction might be directed at very fundamental skills such as the ability to discriminate between different types of pins and connectors or between different concepts related to how components connect to one another. Gaining an understanding of such basic skills is especially critical for older adults because of their greater propensity, due to changes in cognitive abilities, for confusing various stimuli (Glisky, 2007).

Another strategy that can be especially helpful to older learners for facilitating the understanding of and differentiation between concepts is to provide analogies to familiar concepts. For example, for older individuals who are familiar with a smartphone, the advanced electronic functions on an automobile display panel may be able to be explained as following the same principles for selecting and activating applications on one’s smartphone.

Working Memory

There are many ways in which age-related declines in WM capabilities can create problems for the older learner. These include introducing too much information at one time or at too fast a rate, not providing the learner with the opportunity to rehearse new information sufficiently so that it could become reliably stored in LTM, or failure to present information in an appropriate sequence. Even small changes in the rate at which information decays in WM, as illustrated in Figure 3, can translate into significant losses of information when trying to absorb new material during learning. This implies the need for increased maintenance rehearsal with older adults to ensure that information is available in WM when needed during learning.

Figure 3.

Two hypothetical negative exponential decay functions in WM.

Note. Relative to the one on the right, the one on the left is likely to be more representative of normal age-related declines in WM. WM = working memory.

This effect of differential decay rate within WM can become even more magnified for older learners given that speed of processing also declines with age (Figure 2), which can impact the ability for information to even make it into WM. Slower processing of incoming information into WM, slower processing of the information within WM, and an increased rate of decay of material within WM can have a multiplicative disruptive effect on the ability for the older learner to process new material.

One way to improve the efficiency of WM during learning or instruction is to present multiple pieces of information together that are meaningfully associated. If this collective information is not too excessive and sufficient time is provided to ensure that this chunk of information is reliably encoded and distinguished from other information being presented, easier rehearsal of the material and thus an increased likelihood of its transfer to LTM becomes possible. This also aids in the retention of the information in LTM, in the form of meaningful associations, making its transfer to WM when needed more reliable. It also increases WM storage when this information needs to be brought back into WM from LTM, enabling the learner to hold more information in WM during the learning process.

Poor sequencing of learning materials can also lead to inefficient use of WM. Two basic distinctions in sequencing of learning content are topical sequencing and spiral sequencing (Reigeluth, 2007). In topical sequencing, a topic is taught to some target level of understanding before proceeding with training on the next topic. In spiral sequencing, mastery of several interrelated topics is achieved by passing through all the topics at a basic level, and then again, in the same sequence, each time at a greater level of depth until the target level of learning is achieved for all the topics. The advantage of topical sequencing for the older learner is that one can focus on one topic without the possible disruption from other material. Spiral sequencing, despite containing built-in synthesis and review features, runs the risk of disrupting the process of schema development by older learners.

Example:

Consider a study that examined the feasibility of an e-learning training program for teaching Microsoft Excel to older adults seeking employment but who had no skills in using software applications (Taha, Czaja, & Sharit, 2016). Participants opened an Excel file and built a spreadsheet which followed a video presentation of a narrator who demonstrated step-by-step use of basic functions. The presentation could be easily paused, rewound, or fast forwarded. During a lesson involving tracking personal expenses, the narrator stated, “Now, I’m going to address the appearance of the labels. I will select all the cells from A3 to F4; notice the bolded border. This means whatever command I give will apply to all of these cells.” In a previous lesson, they were taught that when you click on a single cell, the border darkens to indicate that the cell is “activated” and thus ready for the application of formatting changes. Although the overall design approach was based on topical sequencing, some of the benefits of spiral sequencing were also captured by taking advantage of the opportunity for the “instructor” to reemphasize previously introduced basic concepts, but in a different context so that concepts could build on one another. Further reinforcement of this “activation” concept, but in yet another more advanced scenario involving building a pie chart, emphasized the need to select (i.e., activate) the chart area prior to being able to use the chart tools tab functions to format the data in the chart. Another principle employed that was directed at increasing the efficiency of the older adult’s information processing included ensuring that there is some “doing” (practice) that intervenes in the presentation of new material so that the learner does not have to remember too much before trying something out.

As noted, WM may be conceptualized in terms of two component memory storage systems (Baddeley, 1986): the phonological loop and the visuospatial sketchpad. One strategy for making more effective use of limited WM capacity during learning would then be to distribute learning materials across these two memory systems rather than overloading one of them. The basis for this strategy derives from multiple resources theory (Wickens, 2008; Wickens et al., 2004). According to this theory, there is not one single undifferentiated pool of attention from which attention can be allocated to the various information-processing resources (Figure 1). Rather, there are multiple distinct pools or dimensions of attention, with each dimension described in terms of two levels. The key is that within each dimension, one of the two levels uses different resources of attention than those used by the other level; efficiency can thus be attained by distributing processing across levels rather than overloading one of the levels. Three of the four proposed dichotomous dimensions are especially relevant for older adult learning: (a) the stages dimension, which implies that the resources of attention which are used for (early) perceptual and central processing are largely separate from those used for (late) response selection or execution processes; (b) the modalities dimension, which differentiates the auditory from the visual modality of information input; and (c) the codes dimension, which differentiates spatial versus verbal (print/speech) information input.

The two levels comprising the codes dimension closely correspond to the phonological loop and visuospatial sketchpad subcomponents of WM. Because each of these codes seems to process information somewhat independently, using its own supply of mental (i.e., attentional) resources, it would seem, especially for older learners, that distributing information during instruction across codes would make more sense than more extensive use of one code.

Example:

Consider the design of an application that can be placed on a mobile platform, such as a smartphone or tablet, which is intended for instructing older adults on exercises for improving their strength, balance, and flexibility. The following example is directed at strengthening the thigh muscles; it can easily be performed at home and requires a chair and a resistance band. The older learner may face several challenges. First, the instructions could overload the visual information processing channel (modality), making it difficult to store—that is, hold—this information in WM while also trying to process how to do these steps. Second, the learner must translate the instructions into an integrated verbal (based on the written text) and spatial (based on how the body is to be configured in response to the text) model in WM. In addition, this information needs to be adequately rehearsed within WM to the extent that it could become reliably transferred, as a “schema,” into LTM, thus enabling this information to be efficiently accessed in the future to better ensure compliance with the regimen. From a design perspective, it is important at the start that the information is “chunked” appropriately so that each chunk is well within the older learner’s capability for processing. An example of such chunking is given in the following instructional steps:

1. Tie your resistance band to the front leg of the chair.

2. Stand up straight behind the chair and loop your foot through the other end of the resistance band. Hold the backrest for support.

3. Lift and curl that foot upward toward your buttocks.

4. Return your foot to the ground.

5. Repeat the exercise with the other leg.

6. This is one repetition. You should do 10 to 15 repetitions on each leg 2 to 3 times.

Initially, the learner may need to hold the mobile device or keep it nearby within reading distance while learning the exercise. To help offload visual modality demands, a narration feature could be incorporated, consistent with the multiple resources model of information processing. The narration should not necessarily be completely redundant with the printed text as the older learner may unintentionally devote limited information-processing resources to ensuring consistency between the narrated and on-screen text material (Czaja & Sharit, 2012). Instead, the narration could represent a shorter form of instructions, highlighting key actions in each step. This approach can help to distribute information-processing resources between the visual and auditory modalities. Still, because the auditory narration, just as the printed text, represents verbal information, it can place additional demands on the verbal component of the codes dimension. Thus, to further increase the efficiency of WM capacity, there should be a design feature which promotes a visuospatial (i.e., pictorial) depiction of the exercise steps. Such verbal and visuospatial redundancy could promote learning of the instructions through the construction in WM of an integrated verbal–spatial model, consistent with an information-processing perspective to multimedia instruction (Mayer & Moreno, 2003). These pictures should be placed near the verbal instructions to establish spatial contiguity (Moreno, 2006), and thus stronger associations in memory. In addition, because older learners may have a problem with decreased processing speed, it is probably more beneficial to them to have static snapshots of the positions corresponding to the text as opposed to an analog video. However, the option to view an animated video should be available, for example, when the learner feels more confident with the instructions and desires to see the exercise performed in a continuous fashion to ensure that one is performing it correctly. An advanced application feature could also include voice commands which could control the narration (e.g., pause, repeat step, next step), further ensuring that information gets maintained in WM and processing speed requirements are diminished.

In summary, it is critical that older learners receive the proper presentation sequence of learning materials, are given sufficient time to rehearse this material so that it becomes adequately stored in LTM, are provided with analogies to support learning of novel concepts so that meaning can be attached to the incoming information (which can then be linked to other information), and can use resources of attention optimally with regard to processing information characterized by different modalities (e.g., visual and auditory) and different codes (verbal and spatial). Otherwise, the unfortunate situation depicted in Figure 4 can become too common for older learners.

Figure 4.

A conceptual depiction of the potentially adverse implications of presenting sequentially dependent learning materials to older learners with WM limitations coupled with LTM knowledge deficiencies.

Note. WM = working memory; LTM = long-term memory.

Long-Term Memory

During instruction, learners are faced with having to recall previously instructed material to support the learning of the current instructional content. Often, this requires retrieving information from LTM, such as a fact, rule, or a concept, which may be part of an associative network of items. This process is typically instantiated, as discussed below, by a retrieval cue (Guastello, 2013), and for older learners may be degraded in light of evidence suggesting that there are age-related declines in associative networks within LTM (Naveh-Benjamin, 2000). Two principal ways in which this could occur are through (a) decays in strength of activation of certain elements of information (i.e., item strength) and (b) decays in the strength of associations of network components, which would make activation of an item of information less capable of invoking activation of associated information elements. Either case could make it more difficult to recall information or more likely to confuse information within LTM. It is thus critical during instruction, especially, for imparting useful schemas and mental models to older learners, to ensure that sufficient time is available and directed to not only strengthening critical items of information (to intensify item strength) but also clusters of items that go together (to promote strength of associations).

A conceptualization of a logical structuring of information within LTM is depicted in Figure 5, where nodes represent items of information and the lines connecting nodes represent associations between these items (Guastello, 2013). Different types of schemas are shown; these are depicted as self-contained clusters of related information which are also linked to other schemas. Thus, the individual may have a schema pertaining to use of a primary rule for a given situation, which can become activated by a retrieval cue—something thought of and brought into WM from somewhere in LTM or perceived from some external source such as a display or instructional manual. This activation could then result in a schema related to conditioning information becoming activated, that is, information regarding conditions under which the rule should or should not apply. If there is sufficient activation that spreads to a node which links to a schema comprising a secondary rule, then this alternative strategy may become adopted. The various schemas may also support a mental model that the individual has of some process.

Figure 5.

A conceptualization of organization of information within long-term memory in terms of different types of schemas, and the strengths of items and associations of information both within and between schemas.

Note. Increased item strength could be represented by increasing darkness of the node, and the strength of associations could be represented by different types of lines, from solid to heavy-to-light dashed lines, to the absence of connections altogether.

Example:

Suppose an older financial worker possesses a schema related to purchasing a specific financial product for a customer. This schema may become triggered by a cue, such as a statement of intent by the customer, and then become linked to another schema in LTM that provides knowledge concerning the conditions governing whether the purchase of this product should be undertaken. If the decision is to purchase this product, there may also exist a link to a mental model which dictates the necessary (computer application) technical functions that need to be implemented for successfully completing this specific transaction.

Consistent with this example, when designing instruction for older learners, it is important to ensure that the instruction serves to identify key individual items and associations of information and to ensure adequate rehearsal of key items and associations—both between and within knowledge structures (e.g., schemas, mental models). It is also important to minimize confusion by emphasizing the distinctions among items, and associations (patterns), as decays of item strength or association strength are more likely to occur in the LTMs of older adults.

Conclusion

The human information-processing system encompasses various interrelated processing resources. This article argues for the importance of understanding the capabilities and constraints associated with these information-processing resources as a basis for anticipating where older adults are likely to confront difficulty during the presentation of learning materials. Guidance is also provided on strategies that can be used to compensate or circumvent the barriers to successful instruction that can stem from age-related declines in abilities related to information processing. A better understanding of the human information-processing system and how it may become perturbed with aging can provide many more opportunities for designers and trainers to achieve success with older learners.