In response to changes in demographics and technology, education in North America is undergoing a transformation. Dorin (2007) summarizes several critical factors in demographic trends that will significantly impact the development of the educational landscape in North America. First, as baby-boomers age there is a dramatic shift in the composition of our population, that is, a greater percentage of the entire population will be represented by those 65 and older. Second, older adults will remain productive in the workforce. Third, life expectancy will increase, adding again to a greater percentage of our population falling into the older adult category.
There are also many changes related to technology use occurring within the older adult population. For instance, older adults are increasingly more interested in opportunities for continuing education (Mannheimer, Snodgrass, & Moskow-McKenzie, 1995) which is more and more frequently accomplished via technology. Outside of educational contexts, we see an interleaving of technology into everyday activities for older adults such as searching for health information on the world Wide Web (Karavidas, Lim, & Katsikas, 2005) and using e-mail as a communication tool to maintain contact with family and friends (Hilt & Lipschultz, 2004). Jastrzembski, Charness, Holley & Feddon, (2005) assert that “older adults may comprise one of the fastest growing segments of the estimated 80 million Internet navigators in the US, having jumped 47% as of 2004. Thus 22% of older adults are now online…” (p. 39).
This chapter provides a review of research findings in the area of aging, describes how age-related declines may impact older adults’ use of computers and presents approaches suggested in the literature to addressing these issues.
BACKGROUND Cognitive Deficits
Speed of Processing
Currently, comprehensive explanations of cognitive aging are represented by two perspectives: processing-speed theory and process-specific theories. Processing-speed theory (Salthouse, 1996) rests on the premise that cognitive declines in many areas are due to a general slowing of cognitive processes. Neuroscience evidence (Hedden & Gabrieli, 2004) suggests this slowing is a result of reduced efficiency in neural transmission, that is, the speed at which electrochemical messages can be passed from one neuron to the next in the brain. This is due to reductions in both the density of synaptic connections and in concentrations of certain neurotransmitters.
In contrast, other theories of aging (e.g., Hasher & Zacks, 1999) refer to more process-specific mechanisms, mechanisms unique to individual processes such as attention, to explain degraded cognitive performance.
A large number of studies provide evidence that speed of processing is a substantive contributor to cognitive aging (e.g., Lemke & Zimprich, 2005; Rush, Barch, Braver & Todd, 2006: Wood et al., 2005) but a number of researchers maintain that speed of processing and process-specific factors work together to impact the course of normal cognitive aging (e.g., Birren & Fisher, 1995; Madden & Gottlob, 1997)
To date, evidence from the human computer interaction and educational technology literature converge with the behavioral and neuroscience evidence regarding the slowing of cognitive processes due to aging. Although speed of processing effects are difficult to isolate in complex on-line tasks, older adults were found to be slower than younger adults in completing an on-line information search task (Boechler, Foth & Watchorn, 2007), using a sequential on-line hierarchy Kurniawan, Zaphiris & Ellis, 2002), and completing tasks in a 3D computer environment ( Sjolinder, Hook, Nilsson, & Andersson, 2005).
Any explanation of the memory deficits we observe during the process of normal aging should be prefaced by a general description of the underlying architecture of human memory. The human memory system is made up of two storage units: long-term memory and working memory. Long-term memory is a repository for information and knowledge that we have been exposed to repetitively or that has sufficient meaning to us. Long-term memory is a memory store that has an indefinable duration but is not conscious; that is, any information in long-term memory must first be retrieved into working memory for us to be aware of it. Hence, any conscious manipulation of information or intentional thinking can only occur when this information is available to working memory. However, working memory has a limited capacity for the amount of information that can be processed at one time. Baddeley, Thomson, and Buchanan (1975) reported that the size of working memory is equal to the amount of information that can be verbally rehearsed in approximately 2 seconds.
Short-term memory, an early term for working memory, refers to a constrained aspect of working memory, that is, the number of items that can simply be held in working memory without any type of manipulation. Manipulating items in working memory (e.g., revising the order, categorizing) uses more cognitive capacity than merely holding items in memory. Age effects have been reported in both short term and working memory measures (Zacks, Hasher & Li, 2000).
The current literature on cognitive aging provides evidence of differential deterioration across different memory systems (Prull, Gabrieli and Bunge, 2000), that is, processes that function in conjunction with the basic architecture of working and long-term memory. For instance, explicit memory refers to the conscious recollection of material, hence, explicit memory draws on the resources ofworking memory. Explicit memories could be memories that are personally experienced by the individual (episodic memory) or memories that represent facts or general knowledge (semantic memory).) Prull, Gabrieli and Bunge (2000) provide neurological evidence that the capacity for laying down new explicit memories declines during the normal aging process, the result being that older adults are less able to purposefully remember material that has been recently viewed whether that is episodic or semantic material. However, Hedden and Gabrieli (2004) note that previously encoded episodic and semantic memories remain quite stable over the lifespan.
Although explicit memory encoding declines with age, research indicates that implicit learning and memory remain relatively stable across the lifespan (Hedden & Gabrieli, 2004). Implicit memory refers to memory for material that has not been purposefully attended to or consciously processed in some way. Implicit memory would include memory for some previously learned skills that have become automatic and the unconscious priming of material rather than direct, intentional recall of material.
This disparity between age-related degradation of explicit but not implicit memory is easily understood because of working memory limitations. Implicit memory, because it is outside of conscious activity, does not draw on the resources of working memory and, therefore, is not susceptible to the age-related declines in working memory capacity (Zacks, Hasher & Li, 2000)
As an example of web-based learning in particular, Boechler, Foth and Watchorn (2006) found that two explicit memory skills, associative memory and short-term memory, were correlated with different aspects of older adults’ performance during an information search task. In this study, associative memory was measured with a paired word test and short term memory was measured with a digit span test where participants viewed a sequence of numbers and immediately had to report that sequence back after it was removed from view. The results of the study showed that associative memory ability was related to recall for website material. Participants with better associative memory were more able to freely recall the page titles of the webpages on the website. This is in accordance with research that shows deficits in older adults’ ability to learn new associations (Kausler, 1994). In addition, short-term memory capacity was correlated with performance on finding target information during the search task. Older adults that scored higher on the digit span test were able to find their way through the website to the target information more often than those with lower scores on the digit span test.
Difficulties with attentional mechanisms, such as the inability to ignore or inhibit extraneous information, have also been reported as part of the normal aging process (Hedden & Gabrieli, 2004). Hasher and Zacks (1999) suggest that this inhibitory deficiency impacts three different functions: the access function, the deletion function and the restraint function. The access function is supposed to inhibit the access of goal-irrelevant information to working memory even though such information may induce partial activation of stored memories. The deletion function operates to eliminate such accidentally activated memories to ensure that the information being manipulated in working memory at any given moment is only related to the goal at hand. The restraint function acts to assure very strong, but unsuitable, activations do not overcome the mental processing of relevant, goal-oriented information. The implication of these findings to computer instruction is that information displays must not be cluttered with visual elements, include extraneous information or information peripheral to the task at hand.
Despite the memory changes described above, studies on the acquisition of new skills is less clear. Prull, Gabrieli and Bunge (2000) divide skill learning into three categories: sensorimotor, perceptual and cognitive. Learning skills does not appear to be dependent on explicit memory processing as amnesic patients show normal sensorimotor, perceptual and cognitive skills. In addition, under certain circumstances, older adults show the same development of new sensorimotor skills as younger adults. For instance, older adults can learn pattern-specific sequences as well as younger adults. However, other studies indicate that when new strategies are needed to learn a new skill, such as tracing a mirror image rather than a normal image or learning a pattern-specific sequence while completing a second task, older adults are slower than younger adults in acquiring sensorimotor skills. Similar patterns ofresults occur for perceptual tasks. Studies using comparable cognitive skills are sparse, but evidence to date suggests older adults can learn new cognitive skills as well as younger adults.
The seemingly ambiguous results of skill learning studies can most likely be explained by the degree of task complexity involved in executing that skill. Mayr, Kliegl and Krampe (1996) discriminate between two types of task complexity, sequential vs. coordination. Sequential complexity refers to the number of steps or operations that need to be addressed in the course of completing a task. Coordination complexity refers to the degree to which information from each of the steps in a task must be applied to or exchanged between other steps in the task. Mayr and Kliegl found an age effect for coordination complexity but not for sequential complexity. These effects were found in figural transformation tasks (Mayr, Kliegl & Krampe, 1996) as well as in a mental math task (Verhaeghen, Kliegl, & Mayr, 1997).
Age effects based on coordination complexity may be particularly evident in dynamic on-line learning environments. On-line environments require a high degree of multi-tasking; listening to the instructors verbal comments, giving attention to text or image-based presentations on screen, tracking text messaging from other students as well as handling the participant board functions such as “raising a hand” to ask a question, clicking the microphone off and on to contribute verbal comments and choosing emoticons to indicate a response to ongoing material.
In general, perceptual decline begins to appear as people enter their forties and increases with age (Wil-lott, 1991). Researchers agree that perceptual decline occurs both at the level of sensory receptors as well as at the level of related neural pathways (Schnedier & Pichora-Fuller, 2000). Research on aging in the last fifteen years has taken into account this relationship between perceptual deterioration (visual and auditory) and cognitive functioning. For example, Lindenberger and Baltes (1994) measured the cognitive and perceptual functioning of both younger and older adults and found that levels of hearing and vision were better predictors of intellectual functioning than the processing speed mentioned above.
Regarding strictly perceptual changes, visual acuity does decline with age as measured by traditional the Snellen acuity task, that is, the ubiquitous eye chart commonly used for testing visual acuity (Schnedier & Pichora-Fuller, 2000). Along with measured decline in the Snellen acuity task, older adults are more affected by suboptimal levels of lighting and contrast in a visual display. This can be exacerbated by problems with “restricted field of view, impaired temporal processing, and slower eye movement in search or pursuit tasks” (p. 176).
In the computer literature, effects of visual impairments are revealed in the diminished ability of users to distinguish between icons and to track and direct the cursor to make selections of icons (Fraser and Gutwin, 2000). According to Hanson and Crayne (2005) other difficulties associated with visual impairment “include problems with visual crowding and font choices in terms of size, style, and color (particularly when combined with background colors or background images” (p. 46). Huang and Yeh (2007) reported that size and spacing between targets impacted older users ability to discriminate between on-screen numbers.
Losses in auditory processing can also be problematic for many older adults. “Hearing loss is the third most prevalent chronic disability among older adults, exceeded only by arthritis and hypertension”(Schneider & Pichora-Puller, p. 157).
Older listeners find it more difficult to detect simple, low-intensity stimuli, discriminate small changes in frequency or intensity , filter out background noise , or precisely locate the source of a target in space. These processing difficulties compromise listening and the encoding of information in everyday situations (Schneider & Pichora-Puller, p. 159.)
Although there are few examples of hearing effects in on-line environments, as there is a paucity of research in this area, it is not difficult to suppose degradations in auditory processing could be problematic in on-line environments. For example, in the absence of visual speech cues to help them interpret ambiguous sounds during on-line interactions, older adults may struggle to keep up in synchronous learning environments where students listen to only audio presentation of the instructor’s voice. Also, poor microphone quality may produce background noise that older adults find difficult to ignore.
Motor Skill Deficits
Along with vision and hearing, Lindenberger and Baltes (1994) also found that motor skills such as balance- gait were associated with intellectual functioning in older adults. Lindenberger and Baltes (1994) provide several hypotheses for these effects. One explanation is that these effects are consistent with the notion that the same slowing of neural processes that impacts cognitive functioning also affects motor functioning. An alternative explanation is that sensory limitations require that extra attention and effort be expended to manage deteriorating motor and perceptual functioning. These are cognitive resources that are then not available for higher-order mental processing.
Besides the relationship to intellectual functioning, a deterioration of motor functioning can present difficulties for older adults in the use of computers due to the demands of using a mouse and keyboard. During normal aging, older adults compared to younger adults demonstrate several deficits in motor control such as taking longer to complete movements, difficulty in coordinating movements and the lessening of the ability to sustain continuous movement (Fisk, 2000). Related to computer use in particular, Walker, Philbin and Fisk (1997) identified three factors that contributed to deficits in motor control when using a mouse to position a cursor on-screen: “poorer perceptual feedback, increased “noise” in the motor pathway” and “strategy related differences”(p.567). These deficits create challenges for older adults, impacting their ability to target small icons on the screen. “Other problems include doubleclicking, dragging, and using scroll bars. Scrolling is particularly difficult in that it requires the complex sequence of moving the mouse to the small target box, holding down the mouse button, and continuing to holddown the button while moving the mouse” (Hanson & Crayne, 2005). Beyond change associated with normal aging, conditions such as arthritis, tremor and partial paralysis can severely impact older adults’ control of the mouse and key board (Chaffin & Harlow, 2005; Hanson & Crayne, 2005).
FUTURE TRENDS: INTERVENTIONS for AGmG issues in on-line learning
supporting diminished cognitive capacities
Two general approaches have emerged toward addressing the cognitive deficits of older adults in on-line learning environments. The first is to design interfaces and on-line materials that specifically cater to the needs of older users. The second approach is to map age-related declines to current, tested cognitive principles of multimedia design as “compensatory strategies” (Van Gerven, Paas & Tabbers, 2006, p.141)
As an example of the first approach, Mayhorn, Stronge, McLaughlin, advocate a systems approach to addressing skill learning and task complexity for older adults in which the first step is a thorough needs analysis. The second step is a task/person analysis in which the abilities of the person are assessed to see if they align with the procedures for carrying out the task. This is followed by the selection, design and evaluation of the training program. Throughout these activities, different aspects of the entire system (e.g., the person, environment and the technology itself) are examined to determine the role each of them play in the successful execution of the task.
In contrast, Van Gerven, Paas and Tabbers (2006) advocate a close adherence to well-established principles for multimedia design. Drawing on John Sweller’s Cognitive Load Theory (CLT)(see Sweller, van Mer-rinboer, Paas, 1998) and Richard Mayer’s Cognitive Theory of Multimedia Learning (CTML) (see Mayer & Sims, 1994), both of which focus on reducing working memory load, Van Gerven, Paas and Tabbers argue ” that these instructional theories bear important benefits for older learners because they support an efficient use of available cognitive resources” (p. 141). Mapping onto principles derived in a series of studies by these authors, Van Gerven, Paas and Tabbers suggest the use of: a) audio/visual presentations to avoid overloading either modality in working memory, b) worked examples instead of practice problems, c) goal-free rather than goal-specific problems, d) parts-to-whole sequencing of material to tap into the pretraining effect, e) omitting redundant information to lessen distractors and focus attention, f) learner-controlled segments to help with the slow down in cognitive processing and, g) presentations that adhere to the signaling and spatial contiguity principles to lessen the necessity of visual search. Van Gerven, Paas and Tabber’s instructional recommendations represent a coherent approach, based on tested principles and effects, and warrants a more detailed look than can be accommodated in the scope of this chapter.
supporting degraded perceptual and Motor processes
To date, solutions for addressing perceptual difficulties focus on adaptive interfaces that allow individual users to maximize their remaining perceptual capabilities. For instance, Arditi (2004) devised software that allows user with difficulties processing on-screen text to make adjustments in a number of text attributes such as font size, height, spacing and serif size. In initial testing of the software, users were able to make adjustments that resulted in improvement of reading rate and acuity.
Kline and Glinert (1995) designed software that included a dynamic magnifier and visual and auditory feedback about the location of the mouse pointer. The software produced mixed reactions from users with some negative reactions to the auditory feedback. Studies which address effectiveness not just user preference for such devices is needed.
Several different approaches have been studied toward addressing motor skill impairment in older adults computer use. For example, Walker, Millians and Worden (1996) found that adjusting mouse movement ratios to a moderate acceleration function where faster mouse movement did not result in the mouse pointer moving a large distance on-screen allowed older users to hit smaller targets. Worden, Walker, Bharat and Hudson (1997) also found that adjusting the mouse parameters so the cursor would decrease speed when approaching an icon, especially if the cursor is moving slowly and increasing the area covered by the cursor to increase visibility resulted in improved performance for older users.
Regarding alternate input devices, Charness, Holley, Feddon, and Jastrzembski (2004) found that a light pen, which could be placed directly on the desired position on the screen, produced faster performance and lessened age-related performance differences compared to using a mouse. However, further research indicated that these results did not hold for tasks that require the user to switch back and forth between one input device to another (Jastrzembski, Charness, Hol-ley & Feddon, 2005).
Even though older adults exhibit declines in cognitive, perceptual and motor processing researchers in the fields of human-computer interaction and educational technology are finding ways to include older adults in educational experiences that make use of technologies. For perceptual difficulties, straight forward solutions seem reasonable; simple fonts and larger font sizes for on-screen text-based presentations as well as for input devices such as key boards, high sound quality and low background noise of equipment. For motor skill deficits, input devices are needed that are responsive but not excessively sensitive to slight deviations caused by uncontrolled movement. For cognitive support, applying tested organizational schemes to on-screen information, using instructional methods such as worksheets to promote self-paced learning, and presenting information using both audio and visual modalities will help older users. Finally, as an overall solution to some of these challenges, we need to provide older adults with more time; time to resolve perceptual ambiguities, to coordinate strategies and responses, and to execute tasks.
Coordination Complexity: Coordination complexity refers to the degree to which information from each of the steps in a task must be applied to or exchanged between other steps in the task. Age effects have been found for coordination complexity but not for sequential complexity.
Explicit Memory: Explicit memory refers to the conscious recollection of material. This could be memories that are personally experienced by the individual (episodic memory) or memories that represent facts or general knowledge (semantic memory).
Implicit Memory: Implicit memory refers to memory for material that has not been purposefully attended to or consciously processed in some way. Implicit memory would include memory of learned skills and the unconscious priming of material rather than direct, intentional recall of mater
Inhibitory Deficiency: A type of attentional deficit exhibited by older adults which is characterized by an inability to inhibit goal-irrelevant information
Processing-Speed Theory: Processing-speed theory is a general theory of cognitive aging based on the premise that cognitive declines in many areas are due to a general slowing of cognitive processes.
Sequential Complexity: Sequential complexity refers to the number of steps or operations that need to be addressed in the course of completing a task.
Short-Term/Working Memory: Short-term/ working memory is the capacity-limited, temporary memory store where all conscious manipulation of information takes place.