8
Technology and Learning in Current and Future Older Cohorts

Sherry L. Willis

SunTrust Equitable Securities recently issued a report evaluating companies involved with technology-enabled learning on the Internet. The report provided some interesting information on the financial world′s perspective. It stated “although the Internet′s impact on commerce … will be notable, ultimately we believe the improvements on how we learn will be the single greatest change that the Internet has on our society” (Close, Humphreys, and Ruttenbur, 2000, p. 5).

In this chapter I consider the current and future role that technology can play in the learning activities undertaken by older people in our society. Learning is typically defined as the acquisition of new information through practice or experience (Howard and Howard, 1997). Of particular interest in this chapter is learning that occurs in everyday life and that often occurs in response to a need or problem. For example, the older adult acquires new information on various treatment options as a first step toward medical decision making. When technology is involved in acquiring this information, the older adult must not only learn the new substantive information, but may also need to acquire knowledge and skills required to use the technology.

In this chapter, two major domains of technology-based learning are considered: the use of technology to acquire knowledge and skills related to substantive topics (e.g., healthcare, leisure activities, job skills) and the knowledge and skills required to use technology (e.g., Internet searches, e-mail). In the commercial and public sectors, technology-based learning is often referred to as “e-learning.” E-learning has been defined as learning experiences delivered or enabled by electronic technology (Commis-



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Technology for Adaptive Aging 8 Technology and Learning in Current and Future Older Cohorts Sherry L. Willis SunTrust Equitable Securities recently issued a report evaluating companies involved with technology-enabled learning on the Internet. The report provided some interesting information on the financial world′s perspective. It stated “although the Internet′s impact on commerce … will be notable, ultimately we believe the improvements on how we learn will be the single greatest change that the Internet has on our society” (Close, Humphreys, and Ruttenbur, 2000, p. 5). In this chapter I consider the current and future role that technology can play in the learning activities undertaken by older people in our society. Learning is typically defined as the acquisition of new information through practice or experience (Howard and Howard, 1997). Of particular interest in this chapter is learning that occurs in everyday life and that often occurs in response to a need or problem. For example, the older adult acquires new information on various treatment options as a first step toward medical decision making. When technology is involved in acquiring this information, the older adult must not only learn the new substantive information, but may also need to acquire knowledge and skills required to use the technology. In this chapter, two major domains of technology-based learning are considered: the use of technology to acquire knowledge and skills related to substantive topics (e.g., healthcare, leisure activities, job skills) and the knowledge and skills required to use technology (e.g., Internet searches, e-mail). In the commercial and public sectors, technology-based learning is often referred to as “e-learning.” E-learning has been defined as learning experiences delivered or enabled by electronic technology (Commis-

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Technology for Adaptive Aging sion on Technology and Adult Learning, 2001). Because this chapter is concerned with the use of technology to support and enhance the learning activities of older adults, the terms learning or e-learning will refer to learning in a technology-based context. The potential impact of e-learning is large, given the importance of education and the current resources allocated to education in our society. The United States currently spends more than $700 billion annually in the education and knowledge arena—the education industry is the second largest, behind healthcare (Close et al., 2000). The education market for kinder-garten through grade 12 is considered the largest (59.6 percent), followed by the postsecondary sector (36.3 percent). Although the corporate training market (16.1 percent) is currently third in importance, it is projected that technology-based learning will penetrate the business world at a faster rate than any other educational sector. The corporate spending figure does not include the $40 billion plus spent by the government on training. The smallest and newest arrival in the education industry is the lifelong learner. The lifelong learning market (3.9 percent) is projected to develop into a prominent segment within the technology-based learning marketplace as the Internet occupies a larger presence in citizens′ daily lives (Close et al., 2000). Of particular concern in this chapter is this lifelong learning segment. A major purpose of this chapter is to consider current knowledge and research in the area of cognitive aging as it applies to learning in a technology-based context. I begin, therefore, by discussing cognitive processes and skills that have been studied in cognitive aging research. I briefly review traditional views of learning, problem solving, and decision making and distinguish between well-structured versus ill-structured learning tasks. I discuss research on expertise and examine differences between experts and novices in how they learn and solve problems. The similarities between the novice′s approach to learning and the approach of many older adult learners and of the typical Internet user searching for information are considered. The various segments of the e-learning industry are briefly described, and their potential role in addressing the learning needs of older adults is discussed. I then consider conceptions of learning evolving within the e-learning context and the potential benefits and challenges of the technology-based environment for the older adult. The role and challenge of the electronic technology context for learning and information seeking are briefly considered. COGNITIVE PROCESSES INVOLVED IN LEARNING: INFORMATION SEEKING AND PROBLEM SOLVING At the heart of complex, long-term learning activities is the ability to acquire and retain relevant information and to solve problems. Acquisi-

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Technology for Adaptive Aging tion of information involves learning, whereas retention and recall of acquired information involve memory. Problem solving involves assessing the present state, defining the desired state, and finding ways to transform the former to the latter. Decision making refers to the evaluation of possible solutions and the selection of one for implementation (Reese and Rodeheaver, 1985; Reitman, 1964). Problem solving as studied in the laboratory has tended to focus on well-defined problems that are relatively short term and have clear-cut solutions. Examples of well-defined structured problems include older adults′ learning of computer-based tasks, such as using an ATM (Rogers, Cabrera, Walker, Gilbert, and Fisk, 1996), an electronic bulletin board (Morrell, Park, Mayhorn, and Echt, 1995), or text editing (Czaja, Hammond, Blascovich, and Swede, 1989). However, the types of problems associated with learning that the older adult often encounters in the real world may vary along a continuum from very well-defined problems with “right answers” to problems that are highly ambiguous, ill-structured, and either have multiple possible solutions or have solutions based on personal choice or consensus among problem solvers (Willis, 1996). Real-world problem solutions often take longer and involve an iterative process (Leventhal and Cameron, 1987; Willis and Schaie, 1993). An example of an ill-defined, poorly structured task is the seeking of health information on the Internet and use of this information in decision making regarding a medical problem. Table 8-1 presents characteristics of well-defined versus ill-defined problems as defined by researchers (Sinnott, 1989; Willis, 1996). Problems vary along this continuum, but the distinction is an important one in discussing the use of technology for learning. TABLE 8-1 Characteristics of Well-Defined Versus Ill-Defined Problems Well-Defined Problem III-Defined Problem Problem is well framed, easily understood Problem is ambiguous, ill-defined All or most information needed to solve the problem is available or knowable Not all information is available, knowable, or agreed upon Often there is a well-defined procedural knowledge or plan relevant to the problem No well-defined or agreed procedural knowledge or plan for “solving” theproblem Often there is one “correct” or agreed-upon solution to the problem Multiple possible solutions; solutions based on choice or consensus, not on “fact” SOURCES: Chi (1985); Reitman (1964); Sinnott (1989); Willis (1996).

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Technology for Adaptive Aging In this section I describe some of the findings from survey research on the approach of the typical Internet user who is seeking information on line. Because health information is one of the more common topics for an Internet search, I describe survey findings on health information searches, based on the Pew Internet and American Life Project (Fox and Rainie, 2000, 2002). Over 25,000 adults were surveyed including over 4,000 adults over the age of 65; both older Internet users and nonusers were surveyed. The Internet search behaviors described below have been reported for older Internet users, as well as users in general. I then relate the search behaviors of the typical Internet user to experimental studies on expertise and problem solving (Chi, 1985; Hershey, Walsh, Read, and Chulef, 1990; Meyer, Russo, and Talbot, 1995). Expertise involves not only substantive knowledge of a domain, but also an understanding of how to organize and use the knowledge in solving a problem within the domain. Experts and novices differ in their possession and use of both. Although many people use the Internet quite often, the search behaviors of the typical Internet user are similar to the approach taken by novices, rather than experts. Importantly, the search behaviors of older adults have some resemblance to those of experts, but also some similarities to those of novices. Searching for Health Information on the Internet Most health information seekers go on line without a definite search plan (Fox and Rainie, 2000, 2002). They typically start with a global search engine, not a medical site, and visit two to five sites on average, spending 30 minutes on a search. About one-fourth of those who seek health information are vigilant about verifying a site′s information; another fourth are concerned about the quality of the information they find but follow a more casual protocol; and half rely on their own common sense and rarely check the source of the information, the date when the information was posted, or a site′s privacy policy. The average information seeker reports feeling assured by advice that matches what they already know about a health condition and by statements that are repeated at more than one site. Age is a factor in the perceived success of a health information search. Although 37 percent of young adults report always finding the information they were looking for, only 19 percent of those over 50 years report such a high rate of successful searches (Fox and Rainie, 2000, 2002). Declarative Knowledge I now discuss prior experimental research on differences between experts and novices in information seeking and problem solving and re-

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Technology for Adaptive Aging late these research findings to search behaviors of the typical Internet user. A major difference between experts and novices is in their possession and use of two forms of knowledge: declarative and procedural knowledge. Declarative knowledge involves knowing what facts and information are relevant in solving a particular problem (Anderson, 1985; Chi, 1985). It is one′s knowledge of the subject matter. Three aspects of declarative knowledge are considered: size of the knowledge base, organization, and relevance and quality of knowledge. Size of Knowledge Base Subject-matter experts are believed to have a large body of domain-specific knowledge (Hershey et al., 1990). In contrast, novices have been shown to have a very limited knowledge base. Although it is certainly the case that older adults have large knowledge bases in many domains, sometimes the data do not bear out conventional wisdom on this point. For example, older women who are diagnosed with breast cancer might be expected to have a larger amount of knowledge than younger women because of prior reading or discussions with friends and relatives. In a study of decision making, however, older women with breast cancer had no greater domain-specific knowledge than young or middle-aged women (Meyer et al., 1995). Moreover, they remembered less of the information regarding breast cancer that was presented during the study and so their knowledge base did not grow to the same degree as in younger adults. Organization of Knowledge The knowledge domains of experts are organized hierarchically with information indexed via meaningful interrelations, allowing experts to scan their memory of a topic quickly and efficiently (Chi, 1985; Hershey et al., 1990). In contrast, novices in a subject area have only a limited body of relevant declarative knowledge. Because novices are just acquiring the relevant information, they have less understanding of how to efficiently organize information on a topic, and the organization of their knowledge base is therefore less hierarchical and integrated. Older adults with experience in a subject domain would be expected to have declarative knowledge bases that are hierarchically organized and well integrated (Hershey et al., 1990). However, research on learning and memory indicates that older adults often do not organize or chunk information efficiently for recall, even when they are dealing with familiar subject matter (Smith, 1996). They fail to use mnemonics such as superordinate categories spontaneously, suggesting that information is not always organized hierarchically.

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Technology for Adaptive Aging Search behaviors of Internet users seeking health information suggest that they do not begin the search with a well-organized body of knowledge, nor do they search in a manner useful to building a systematic hierarchical, integrated knowledge base (Fox and Rainie, 2000, 2002). Most Internet users reported starting with a general search engine. Almost half of information seekers started with the first search results on the retrieved list and worked their way down, reflecting neither hierarchical organization of knowledge nor an understanding of the most relevant information regarding a problem. Many information seekers did not know that search engines may be paid to list sites in a prominent position. Only one-third reported ever book-marking a health-related web site (Fox and Rainie, 2000, 2002). Quality and Relevance of Information Experts have the ability to select quickly the most relevant information for a particular problem, obviating the need for a more exhaustive and time-consuming search through their declarative knowledge. In contrast, novices are less adept at differentiating higher-order versus lower-order information, and thus they have difficulty determining the most relevant information for a particular problem (Hershey et al., 1990; Staudinger, Smith, and Baltes, 1992). Some research suggests that older adults also have difficulty determining what information is particularly relevant to the current problem (Labouvie-Vief and Hakim-Larson, 1989). Older adults often base the decision of whether information is relevant in solving a current problem on whether the information was useful in solving previous problems. They fail to consider similarities between the current problem and prior problem-solving experiences. The expertise literature suggests that the organization of the knowledge base is critical in quickly identifying the most relevant information for a given problem. The inability of older adults to identify the most relevant information may suggest limitations in the organization of their knowledge base or limitations in their ability to reorganize their knowledge base to address a new problem. Investigators have also found that older adults, as compared with young adults, focus more on pragmatic, concrete, subjective information based on or compatible with their own past experiences (Sinnot, 1989). Their approach is characterized as being sensitive to the interpersonal context and focused on inner and personal experience as the way of thinking and knowing. In a similar vein, Leventhal and Cameron (1987) argue that a distinction should be made between two types of declarative knowledge. They distinguished between semantic memories, which represent the individual′s conceptual knowledge about the problem, and episodic

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Technology for Adaptive Aging memories, or autobiographical information, based on the subject′s prior experiences with respect to a particular problem domain. The former (i.e., semantic memories) is similar to the typical use of the term declarative knowledge. However, the more personalized knowledge (i.e., episodic memories) may be particularly relevant for older adults, given that they would be expected to have a more extensive bank of experiences and thus would be more disposed to employ this personalized knowledge in problem-solving situations. Leventhal and Cameron suggest that there may be a conflict between the two types of knowledge in solving a problem. For example, with regard to medical decision making, the more objective, semantic knowledge may inform a person that certain diseases (e.g., heart disease) are asymptomatic and hence one cannot rely on how one feels in making decisions regarding the efficacy of medications or when to see a doctor. In contrast, the personalized knowledge based on episodic memories may argue that, in the past, sickness is related to not feeling well (e.g., symptoms); hence, if there are no symptoms, then one is not sick. Different decisions and problem solutions will be reached depending on which knowledge system is utilized. Leventhal and Cameron (1987) found that personalized knowledge (episodic memories), not the more objective declarative knowledge, is the more critical and predictive of seeking medical attention and complying with prescribed medical treatment. The individual′s own personal belief system or representation of the problem appears to become increasingly salient in the solution of problems when the goal of the problem is less clearly defined and there are less well-determined heuristics or algorithms (i.e., procedural knowledge) to employ in solving the problem (Leventhal and Cameron, 1987). Information seekers using the Internet have been found in survey research to be remarkably confident regarding the validity and quality of information on the web, particularly regarding healthcare (Fox and Rainie, 2000, 2002). Almost three-fourths of those engaging in self-directed searches of health information think that one can believe all or most of the health information that is found on line. What are the major mechanisms information seekers employ to determine the quality of information? First, if the information fits with facts they already know, they are more likely to believe what they read. Thus, Internet information seekers are somewhat like older adults in that they rely inordinately on their own prior knowledge or psychologically meaningful data in determining the validity of information. Whereas the expert quickly identifies the specific unit of prior knowledge that is relevant to a given situation, older adults and the Internet user seeking health information often draw on prior knowledge indiscriminately, whether or not it is relevant to the current problem. Second, if they read the same “facts” on several different sites, their trust in the site information increases. Many information seekers are

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Technology for Adaptive Aging not aware that information is often syndicated and appears on multiple sites (Fox and Rainie, 2000, 2002). Procedural Knowledge Procedural knowledge represents the individual′s understanding of how to go about solving a particular problem—how declarative knowledge relevant in a particular situation can be combined to produce a solution (Anderson, 1985; Chi, Feltovich, and Glaser, 1981). Often the procedural knowledge involves performing a set of operations or following an algorithm. Deficits in procedural knowledge are generally seen as more serious than lack of declarative knowledge in problem solving. Two aspects of procedural knowledge are discussed: extensiveness of the search and the plan. Extensiveness of Search One of the most interesting distinctions between experts and novices is that experts attend to fewer pieces of information than novices. However, the information attended to by experts is usually higher-order information within the hierarchical knowledge base (Hershey et al., 1990). Because experts have both well-organized knowledge hierarchies and well-honed strategies for solving problems, they are more efficient in selecting only the most relevant information and plugging it into the problem strategy. Experts arrive at problem solutions faster than novices. This finding appears to be due not only to the more extensive declarative and procedural knowledge of experts, but also to greater efficiency in using this knowledge (Hershey et al., 1990; Meyer et al., 1995). Novices, particularly young adult novices, in contrast to experts, engage in more time-consuming information searches and amass large amounts of data in the early stages of problem solving. Novices′ intensive and lengthy searches often are due to their limited understanding of what particular information is most relevant to solving a given problem. Having accumulated a mass of information, novices are more likely to reexamine the same information more than once, because the information is poorly organized and the novice may have insufficient command of the topic to hierarchically organize the information. Moreover, the information sought by novices is often at a lower level in the hierarchical structure of domain-specific knowledge constructed by experts (Chi, 1985). There is a superficial similarity between experts and older adults in that both groups conduct more limited searches and reach decisions or problem solutions more quickly than younger novices (Leventhal, Leventhal, Schaefer, and Easterling, 1993; Meyer et al., 1995). This finding

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Technology for Adaptive Aging of a similarity between elders and experts in the brevity of their searches is interesting because often older adults are making decisions relatively quickly even though they have relatively limited information regarding the problem. In contrast, experts are making decisions quickly based on extensive declarative knowledge. Some researchers have suggested that the similarity between experts and older adults in the speed of making decisions reflects very different processes. Leventhal et al. (1993) found that the speed of seeking medical attention is associated with older adults′ increased need to conserve physical and emotional resources. The strategies that adults use in dealing with health threats are believed to change and to become more efficient with age. These strategies include being strongly motivated to detect and avoid threat as soon as possible and to do so with minimal resource expenditure. Thus, the brief searches and quick decisions by experts are a reflection of their knowledge of the domain. In contrast, the decisions of older adults reflect urgency, even in the absence of knowledge. Plan, Script, or Algorithm One of the hallmarks of the expert is quick development of an appropriate plan to go about seeking relevant information and solving a particular problem. The expert “plugs in” the relevant information to the plan or script. In contrast, novices spend much time developing or discovering a plan as they go about solving a problem. The Pew Internet survey findings indicate that Internet users have relatively little in terms of a plan (procedural knowledge) at the beginning of a search. This lack of a plan is reflected in their starting a search with a general search engine and going consecutively down the list of sites rather than scanning for the most relevant or high-quality sites (Fox and Rainie, 2000, 2002). Reports that Internet users do not routinely bookmark sites or return to these sites for further information also reflect lack of a plan. They depend on the algorithms of global search engines rather a preconceived plan of their own. Table 8-2 provides a summary of the similarities and differences between expert, novice, elder, and the typical Internet user with regard to declarative and procedural knowledge. Declarative and Procedural Knowledge in Training on Computer Skills The literature on training older adults to use computer technology illustrates the salient features of declarative and procedural knowledge discussed above. Research on the most effective training methods supports the salience of hierarchically organizing the information to be

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Technology for Adaptive Aging TABLE 8-2 Similarities and Differences Between Expert, Novice, Elder, and Typical Internet User Characteristic Expert Novice Older Adult Typical Internet User Declarative knowledge   Size of knowledge base Large and relevant Limited Large? Limited   Organization of knowledge base Dense, well-integrated, hierarchical Bottom up; sparse; not well-integrated Top down; not always hierarchical; focus on gist, not detail Bottom up; incremental   Quality and relevance of information Highly selective of information Larger chunks of information Cannot differentiate lower- from higher-order information; many small bits of information Emphasis given to personal experience, meaning; values experience over scientific findings Limited quality check of information; evaluates quality based on own prior knowledge Procedural knowledge   Extensiveness of search Limited search; attends to selected, highly relevant information Extensive search; attends to many bits of information; seeks second opinions Limited search; attends to few bits of information; is information most relevant? Brief search—30 minutes; searches two to five sites   Plan, script, or algorithm Well-developed plan; plugs problem information into predetermined plan Time spent developing plan Overuse of heuristics and strategies that have worked in past No apparent plan; begins with general search engine and works down list of sites consecutively SOURCES: Fox (2001); Fox and Rainie (2000); Hershey, Walsh, Read, and Chulef (1990); Meyer, Russo, and Talbot (1995); Sinnott ( 1989); and Willis (1996).

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Technology for Adaptive Aging learned, proceeding in training from simple to more complex or higher-order concepts and skills, and highlighting for the older learner the most salient information and skills to be acquired. In addition, the particular importance of procedural knowledge is supported in the training studies. Zandri and Charness (1989) found that use of advanced organizers was particularly effective for older adults learning to use a calendar and notepad system without a training partner. Czaja et al. (1989) reported that older adults learning text editing profited from a goal-oriented approach in which elements of text editing were introduced in an incremental fashion, moving from simple to more complex tasks. Rogers et al. (1996), in teaching older adults to use automatic tellers, found that an on-line tutorial that provided specific practice on task components was superior to written instructions. The particular importance of procedural knowledge in learning a task was supported in several studies. Morrell et al. (1995) reported a procedural instructional method to be more effective in learning to use an electronic bulletin board system. “Hands-on” training methods, which imply an emphasis on procedural knowledge, have been shown to be more effective than verbal descriptions of navigational tools for older adults who were learning to navigate the web (Mead, Spaulding, Sit, Meyer, and Walker, 1997). Likewise, behavioral modeling of use of a software package followed by actual practice with the package has been employed successfully with older learners (Gist, Rosen, and Schwoerer, 1988). Rogers (2000) has provided a summary of training recommendations for older adults. Although they were not developed from the same declarative and procedural knowledge framework within which this chapter is organized, it is relatively easy to see that, in following them, both types of knowledge would be gained and applied with greater efficiency. They include the following: Allow extra time for training; self-paced learning is optimal. Ensure that help is available and easy to access. Ensure that the training environment is free from distractions. Training material should be well organized and important information should be highlighted. Provide sufficient practice with task components. Provide an active learning situation. CHALLENGES IN E-LEARNING AND E-INSTRUCTION Emphasis on Content by the E-Learning Industry The e-learning industry considers content to be the most critical component of learning through the Internet, and the greatest financial resources

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Technology for Adaptive Aging and effort in the e-learning industry are often being targeted at content (Close et al., 2000). High-quality content is seen as the feature that is most likely to attract individuals to learning on the Internet. Why is content viewed as so important by the e-learning industry? It is believed that extensive, in-depth content will (a) encourage customers to spend more time on the Internet, (b) attract more site visitors, (c) increase interaction among learners, and (d) foster customer loyalty to a site. At some sites, users play a role in the development of content via chat rooms and threaded e-mail; the content of a site may increase as a result of user input. Content as conceived in the e-learning industry appears to place primary emphasis on declarative knowledge. The findings discussed above indicate that a focus solely on the volume and depth of declarative knowledge is insufficient for successful learning. Of critical importance is the industry′s ability to organize content in ways that facilitate information searches and to highlight the relevance of particular content for a specific task. Moreover, the average problem solver or information seeker has serious deficits in procedural knowledge, a deficit that is shared by most current e-learning resources. Synchronous, Asynchronous, and Blended Learning Settings E-learning content can be delivered by several methods, including synchronous, asynchronous, and blended settings. These settings differ in the amount of interactivity between the learner and instructor and among the learners (Close et al., 2000). Asynchronous learning, where learners and facilitators are not on line at the same time, can involve self-paced courses or can involve asynchronously scheduled classes with interaction between students and teachers that falls short of the virtual classroom. Interaction between participants includes threaded discussions and e-mails. In contrast, synchronous e-learning enables all users to participate simultaneously. Thus, while one person is presenting material, others can often ask questions of both the presenter and the other participants. Frequently, synchronous e-learning environments allow shared applications and workspaces (e.g., whiteboards) for brainstorming, revision, etc.; full audio and video capabilities; shared web use; and archiving for recordkeeping, quality assurance, and later reference. What are some of the strengths and limitations of each approach? Asynchronous settings require more up-front preparation and time on the part of the developer or instructor. Synchronous e-learning requires less physical course design for trainers and instructional designers. In asynchronous e-learning, reproducing all of the information that is in the head of the

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Technology for Adaptive Aging trainer or subject-matter expert in a manner that is engaging for a self-study learner can be hard work. Synchronous e-learning eliminates part of the work by putting the trainer or subject-matter expert back into the picture. At the same time it should not be assumed that teaching in a synchronous setting is identical to teaching in a traditional classroom (Duckworth, 2001). Both the instructor and the students must learn somewhat different communication skills in a virtual classroom. Learners will need instruction on how to take turns (e.g., when asking for help), the use of features that foster interactivity, and instruction on course content. Thus, learners will be multitasking in ways that are unfamiliar to them. This may be particularly challenging for older learners. At the same time older adults may find having an instructor on line particularly helpful to organize and pace the presentation of material. Unlike the traditional classroom, however, instructors teaching on line may be less able to read participants′ confused expressions when a new concept is perplexing. There would appear to be more burden on students to monitor their own learning and to initiate questions for clarification. Although there is always the need for an instructor to continually ask questions to increase interactivity and to monitor students′ progress, continually diagnosing each learner′s particular strengths and limitations would seem more challenging in a virtual than a traditional classroom (Duckworth, 2001). The freedom to learn asynchronously may require extra motivation and time management skills on the part of the learner (Tang, 2000). The student must take the initiative to determine the time and place to cover course material. Ironically, asynchronous learning settings may offer greater opportunity for highly individualized instruction. Personal electronic “tutors” can offer different pathways (sound, virtual depictions, alternative explanations) to help learners grasp what was not clear the first time around. Visualization technologies may enable instructors to model and present many different types of material in dynamic ways to help people learn by doing rather than observing. However, this personalized approach to instruction appears to depend on interactive high-speed technology that is often not available in the older learner′s home. Without this interactive, personalized technology, asynchronous learning would seem to place a greater burden (than synchronous settings) on students to monitor their progress, to determine how much time and emphasis to give to various course materials, and to determine when errors have been made. These executive planning and monitoring skills that appear to be needed for self-paced instruction have been shown to exhibit some age-related decrements (Czaja, 2001).

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Technology for Adaptive Aging The Costs of E-Learning Although terms such as cost-effectiveness, learner control, interactivity, and accessibility are frequently used to describe the benefits of e-learning, the bottom line is that quality e-learning is labor and time intensive. E-learning instructor guides recommend that a technical assistant, in addition to the instructor, be available for synchronous learning settings (Duckworth, 2001). Multiple assistants may be required if multiple sites are involved. A live e-learning session can be derailed when one participant calls because he cannot load the plug-in, another sends a message to say she is having trouble with the audio, etc. More time is needed at the beginning of an e-learning course. Time is needed for students to master interactive technology. Time is needed to establish “netiquette” (Tang, 2000). Time is needed to establish rapport and group identity. Time is needed to solve problems such as incompatibility of student and program technology and interruptions in transmission. Moreover, discussion and teamwork often take more time in e-learning than in the traditional classroom. Work is often asynchronous and it takes extra time for group members to respond to messages and reach agreements. USING TECHNOLOGY FOR SYNCHRONOUS, DISTRIBUTED LEARNING In the previous sections I have discussed some of the cognitive processes required for the efficient use of technological innovations such as the Internet that support long-term learning. Throughout this treatment, the importance of declarative and procedural knowledge has been emphasized both as a means of understanding individual differences between expert and nonexpert learners and as a critical framework for training people in the use of technology to foster their own learning. A concrete example is in order. What follows is a brief case study, an illustration of one type of learning affordance available to older people through current technologies. After describing the system, some of its strengths and weaknesses relative to the needs and capacities of the older user are discussed. Figure 8-1 depicts the CentraOne™ system, a desktop-based environment for allowing multiple users to interact with each other. Although it can be used for purposes other than learning (e.g., social interaction or support groups), it has been developed primarily as a learning environment. It combines recent developments in web-based telecommunications and applications sharing with established forms of content delivery. Course outlines, lecture notes, discussion questions, and the like are often

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Technology for Adaptive Aging FIGURE 8-1 A desktop, multipoint, synchronous e-learning environment. presented visually but, in addition, multiway audio and video communication is possible. A shared virtual whiteboard is available for brainstorming and team work, and other applications resident on participants′ workstations or the web can be shared among users. There are mechanisms for the control of communications, provision of synchronous feedback to the coordinators, entry of new participants, etc. All of these resources are also available in breakout groups. Archiving of “meetings” allows recordkeeping, and archives can be accessed by those unable to attend. This system and other similar products available today get high marks on several grounds. They can provide the vocational, avocational, social, and affective benefits of group learning for all older adults, even those who are homebound, mobility impaired, or simply not attracted to the traditional classroom. They rely on many familiar conventions in the use of icons and allow integration with very common applications for word processing, presentations, and web browsing. They are not overly technical, and many of the tools are labeled well or have easily recognized meanings. Still, as can be seen from Figure 8-1, there is a good deal of learning that must go on before one can even begin to use the environment, and this learning will be more of a challenge for the older user.

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Technology for Adaptive Aging First, there is the matter of learning what operations and interactions are allowed by the system. A user cannot be expected to engage in text chat, propose an ad campaign in a shared whiteboard, or signal that they are temporarily leaving the group if they do not know that this functionality is available. Some tools are not visible in the default configuration, and confusion results when facilitators assume users can make use of these tools when, in fact, the users do not know they exist or where to find them. Even if they know that the system is capable of these functions, accessing them is not always intuitive or transparent. For example, many people might assume that the upraised hand icon is intended to signal a desire to stop some action. In fact, it signals the facilitator that there is someone who wishes to speak and puts that person in a queue to be given speaking privileges. And what the heck is that person with the exclamation mark on his or her shoulder intended to convey? It should come as no surprise that a 2-hour tutorial is needed to bring relatively technology-savvy people to a reasonable comfort level in using the system at an introductory level. Learning to deliver content and dynamically control delivery and user interactions takes considerably longer. Returning to the declarative versus procedural knowledge framework that has guided much of this chapter, there are myriad facts that must be learned to use this system well. These include “what” and “where” information about logging in, participation in conversations, feedback, and developing content. Whereas some of this information may have been learned previously and so falls under the heading of semantic declarative knowledge (Leventhal and Cameron, 1987), other facts are linked uniquely to this particular product and so, at least during the acquisition phase, are rightly considered episodic declarative knowledge. It is unlikely that older adults will have the same semantic declarative knowledge as their younger counterparts, and well-known deficits in episodic learning (see Kausler, 1991) will slow their learning of new material. The same might be said for procedural knowledge. Many of us learned long ago that there are generic algorithms or scripts for taking turns in a guided group discussion. The algorithm involves signaling to the facilitator, frequently by raising one′s hand, that one wishes to speak and then waiting for the facilitator to provide a visual or auditory signal that one′s turn has come. This algorithm works in the learning environment illustrated, and one might expect people to adopt it rather easily. Before this occurs, however, users must integrate superficial differences in the environment with the general constraints of the algorithm. For example, the users need to know that it is by accessing the raised hand icon that they signal a desire to speak. They must also learn that their microphone will not be enabled unless they execute a specific key press while talking. Facilitators must learn additional actions that they must

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Technology for Adaptive Aging perform to allow users to speak sequentially. Each of these components to the specific instantiation of the algorithm must be learned and remembered to use the system well. That is, there is an episodic component even for those cases in which semantic procedural knowledge is available. When procedural knowledge is not stored in semantic memory, cognitive demands can be overwhelming, particularly for older adults who have limitations in dividing attention (McDowd and Shaw, 2000), memory (Salthouse, 1991), and learning (Kausler, 1991). Consider, for example, setting audio controls so that one can hear and speak clearly in the environment. Some of the instructions for setting these controls, labeled an Audio Wizard Mode Setting in Figure 8-1, are puzzling, to say the least. Would the typical older adult know the meaning of terms such as “voice activated” or “full duplex audio”? Would they know that, on a system with an internal microphone, using an external microphone produces considerable feedback? Would users even know that they have an internal microphone? Not likely. Furthermore, many people do not have general experience in setting controls that would transfer readily to this setting. Complicating matters further, both declarative and procedural knowledge can be important in learning both the content being delivered and the tool being used to deliver that content. Recent research by Laberge and Scialfa (under review) showed that content and technology knowledge made independent contributions to individual, age-related differences in search rates. Greater knowledge of local geography helped older adults search a web site, but they were hindered by less experience with the World Wide Web. Thus, expertise with both the material being learned and the means of delivery will impact the older user. Although it has not been a focus of this chapter, there is good reason to mention age deficits in attentional deployment in the context of using technology to foster long-term learning. The environment that serves as the basis for this case study has the capability to use several active areas simultaneously. That is, feedback on a critical question can be collected, tallied, and displayed in one area while groups of users are working on an outline in a second area and when a presenter is progressing through content in a third. Facilitators often make the assumption that users are attending to these multiple regions optimally so that they register critical state changes shortly after they occur. This is a naive assumption at best, particularly for the older user, who will have difficulty dividing attention, sustaining attention when information changes at a rapid rate or is embedded in noise, and switching attention once it is focused on a particular subtask or region (see McDowd and Shaw, 2000). Because it is covered in other chapters (see Schaie, this volume), the role of sensory and perceptual aging is not treated in detail here. How-

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Technology for Adaptive Aging ever, it is critical to bear in mind that when developers focus on content, they frequently do it at the expense of signal-to-noise ratios. Print is reduced and sound quality is compromised. White space is eliminated to allow for more links, action buttons, and the like. Such changes are likely to hamper older adults in learning to use these tools and in using them efficiently once they are mastered. Although there have been numerous recommendations for universal design that would make technology user friendly for people of all ages, adherence to these recommendations is not common practice today. As a consequence, for older adults, learning through technology will continue to fall short of its potential. THE CONTEXT FOR E-LEARNING An issue relevant to both current and future older e-learners concerns the contexts in which most adults have access to technology, learn to use it, and more importantly update their technological skills. Technological competence, access, and use are significantly associated with being in the work force or being in a household with someone in the work force. In addition, interaction with younger generations is important to learning and updating computer skills (U.S. Department of Commerce, 2002). Older adults are at a disadvantage because they are less likely to have regular contact with the workplace, educational settings, or to intergenerational exchanges with the young. Approximately three-fourths of the work force (73 percent) today report using a computer and 65 percent use the Internet at work (U.S. Department of Commerce, 2002). However, prevalence of computer-related activities in the work place does vary with age; only 25 percent of workers aged 65 years or over use the Internet or e-mail. There appears to be “spillover” from work to family in computer use (U.S. Department of Commerce, 2002). The presence of someone in the household who uses a computer or the Internet at work is associated with substantially higher computer ownership and use at home of the computer or the Internet. This spillover also occurs for those over age 55 years with greater Internet use in the home if someone in the household uses the Internet at work (U.S. Department of Commerce, 2002). In addition, family households with children under age 18 are more likely to use the Internet than family households with no children. Non-family households, composed primarily of persons living alone, have the lowest use of the Internet. With increasing age, adults are less likely to live in households with the younger generation and are more likely to live alone. Forty percent of older women age 65+ live in a nonfamily household (Administration on Aging, 2002). It is these older women living alone who may most benefit from proficiency in various forms of technol-

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Technology for Adaptive Aging ogy in order to maintain an independent lifestyle (U.S. Department of Commerce, 2002). Broad and equitable access to e-learning is possible only when an appropriate infrastructure is in place, involving high-speed telecommunications and connectivity for individuals and communities (Commission on Technology and Adult Learning, 2001). Broadband Internet services that give e-learning its revolutionary potential are still relatively limited, particularly among low-income, poorly educated people. Community services such as those offered through libraries may provide access to technology in low-income communities. For e-learning to become widely accessible, consensus will have to be reached on shared technical standards for key features of the e-learning environment. Common standards for such things as metadata, learning objects, and learning architecture will ease communications among different platforms, operators, providers, sites, and learners. Common standards can reduce the time and cost of producing high-quality content and applications. Standards exist; the challenge is for government and business to adopt consensus standards. Finally, use of e-learning systems is driven largely by the technology available to the participant. The use of state-of-the-art video and audio streaming can lead to delays when used with a slower Internet connection. The “fit” between the user′s technology and the technology required for a given program or course is critical for a positive learning experience. REFERENCES Administration on Aging. (2002). A profile of older Americans: 2002. Washington, DC: U.S. Department of Health and Human Services. Anderson, J.R. (1985). Cognitive psychology and its implications (2nd ed.). New York: W.H. Freeman. Chi, M.T.H. (1985). Interactive roles of knowledge and strategies in the development of organized sorting and recall. In S. Chipman, R. Segal, and R. Glaser (Eds.), Thinking and learning skills: Current research and open questions (Vol. 2). Mahwah, NJ: Lawrence Erlbaum. Chi, M.T.H., Feltovich, P.J., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152. Close, R.C., Humphreys, R., and Ruttenbur, B. (2000). E-Learning and knowledge technology: Technology and the Internet are changing the way we learn. New York: Sun Trust Equitable Securities. Commission on Technology and Adult Learning. (2001). A vision of E-learning for America′s workforce. Washington, DC: American Society for Training and Development. Czaja, S.J. (2001). Technology change and the older worker. In J. Birren and K.W. Schaie (Eds.), Handbook of psychology and aging (5th ed., pp. 547-568). San Diego, CA: Academic Press. Czaja, S.J., Hammond, K., Blascovich, J.J., and Swede, H. (1989). Age related differences in learning to use a text-editing system. Behaviour and Information Technology, 8(4), 309-319.

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