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Training Older Adults To Use New Technology

School of Psychology, Georgia Institute of Technology, Atlanta.

Address correspondence to Jamye M. Hickman, School of Psychology, Georgia Institute of Technology, 654 Cherry St., Atlanta, GA 30332-0170. E-mail: jamye{at}gatech.edu

	Abstract

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Enabling older adults to interact with new technologies is an important immediate and long-term goal for training research. The literature provides general guidance for developing training programs; however, it is important to determine the type of training that works best for younger and older adults, for immediate performance, and for learning. In the present study we assessed the relative benefits of guided action training and guided attention training for younger and older adults learning to use a novel technology system. Guided action training yielded better performance during training for both age groups. However, assessments of learning revealed a clear benefit of guided attention training for both age groups. These data suggest differences in training benefits for immediate performance versus more general learning.

THE degree to which technology has become a part of everyday activities is evident in a number of contexts. In the kitchen there are multiple programmable appliances such as the microwave, dishwasher, coffeemaker, and oven. Recent entertainment technologies abound, including multifunction cable television, digital video recorders, digital music players, and digital cameras. Communication with family and friends may include use of electronic mail, cellular telephone, and videoconferencing. These are just a few examples of current technologies—newer ones are being introduced into the market place regularly.

A critical issue now and on the horizon in the field of cognitive intervention and training research is how to train older adults to interact safely and efficiently with these technological advancements. Previous studies suggest that it is not sufficient to develop training programs and expect that they will be similarly effective for younger and older adults (e.g., Jamieson & Rogers, 2000; Mead & Fisk, 1998; Paas Camp, & Rikers, 2001).

General theories of training and skill acquisition emphasize the importance of providing practice that emphasizes the consistencies in the task (e.g., Schneider, 1985), ensuring that attention is allocated to the critical task components (e.g., Fisk & Schneider, 1984; Sweller, van Merrienboer, & Paas, 1998) and considering performance goals when developing the training program (e.g., task-appropriate training; Bransford, Franks, Vye, & Sherwood, 1989). In addition, learning can benefit from augmentation training that highlights the relevant aspects of the task in some way (Eberts & Schneider, 1985; Gerjets, Scheiter, & Catrambone, 2004; Kirlik, Fisk, Walker, & Rothrock, 1998). Training should support learning the task well enough so that an individual can use the system when the instructional materials are no longer accessible, retain information over time, and generalize knowledge to other similar tasks. Often training that is best for immediate performance is not best for longer term mastery and learning (e.g., Schmidt & Bjork, 1992).

With respect to training older adults, the recommendations include, but are not limited to, providing self-paced training (Czaja, 1997), using goal-oriented training to improve performance (Hollis-Sawyer & Sterns, 1999), and minimizing working memory demands (Morrell & Echt, 1996; Sweller et al., 1998). There is also some evidence to suggest that older adults require specific procedural training to learn to use computer technology (e.g., Morrell, Park, Mayhorn, & Kelley, 2000).

The research thus provides some guidance about how to train younger and older adults. However, it is not clear how to balance the different recommendations. For example, ensuring appropriate attention allocation (which is good) may be working-memory demanding (which is presumably bad for older adults). Procedural training and part-task training may be necessary for older adults to be able to learn the tasks (e.g., Mead & Fisk, 1998; Morrell & Echt, 1996), but such training may not enable them to develop an understanding of overall system structure (e.g., Mead & Fisk, 1998; Van Merriënboer, Kirschner, & Kester, 2003). Providing augmentation during training (e.g., highlighting system components when they should be activated) can improve performance, but it may impede learning if the trainees become overly reliant on it and it is not available after the training has been completed (e.g., Anderson, Kulhavey, & Andre, 1971, 1972; Eberts & Schneider, 1985; van Merriënboer & Sweller, 2005). The key may be to find the level at which working memory is engaged but not overloaded.

The purpose of the present experiment was to provide insight into these issues. We compared the relative benefits of two types of training for younger and older adults learning to use a technology system. We provided guided action training to reduce working memory demands by telling participants which steps to perform and in which order. Researchers hypothesized that this training is best for older adults because it minimizes working memory demand. We designed guided attention training that assisted participants in properly allocating their attention but required them to actively determine what to do for each step of the task. The literature suggests that this training would be most supportive of learning for younger adults because it requires them to engage in elaborate information processing to learn how to perform the tasks (cf. Schmidt & Bjork, 1992). However, it was possible that the guided attention training would be too working-memory demanding for the older adults and hence be ineffective (Van Gerven, Paas, Van Merriënboer, & Schmidt, 2000).

Training was self-paced, and we encouraged participants to learn the system. That is, we told them to complete the training materials at their own pace and that they would later be tested on their ability to use the system without access to the training materials. We designed assessments to differentiate between performance and learning (cf. Schmidt & Bjork, 1992). We measured task completion time and accuracy during training in order to determine how well each training method supported performance of the task. We then required participants to use the system without access to the training materials—for both familiar tasks on which they had received training and unfamiliar tasks on which they had not received specific training. These transfer measures provide an index of learning (e.g., Schmidt & Bjork, 1992). The ability to complete trained tasks without access to the instructional materials and the ability to perform untrained tasks would both be considered near transfer because they involve transfer within the same domain (Barnett & Ceci, 2002). These conditions allow us to measure how well participants learned the system (trained tasks) and could generalize their knowledge (untrained tasks).

This study was grounded in the idea of representative design as suggested by Brunswik (see Hammond & Stewart, 2001; Hoffman & Deffenbacher, 1993). It is important to assess performance and learning for tasks that are representative of an older adult's environment. The system we designed simulated a situation wherein an older adult is required to learn to use a novel, complex, technological system. Our system had a variety of system screens, links across screens, as well as multiple functions and subfunctions. The training required users to learn the layout of the system and how to activate different functions, similar to what would be required for other menu-based systems such as a cell phone, online library catalog, automatic teller machine, or in-vehicle navigation system. Consequently our discoveries of differential training benefits are generalizable to the learning of similarly structured technologies.

	METHODS

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Participants
The younger adults were 32 undergraduates attending a metropolitan Atlanta university, 18 to 29 years of age (M = 19.63 years, SD = 1.8). The older adults were 32 residents of metropolitan Atlanta contacted through an independent living facility and a community center, 65 to 79 years of age (M = 71.76 years, SD = 4.2). Older adults received $100 for the 10 hours of participation; younger adults received $100 or 10 extra credit points, or a combination of the two, not to exceed $400 or 10 points (e.g., $70 and 3 points).

As part of a larger study, the participants completed the Center for Research and Education on Aging and Technology Enhancement battery of tests, which includes cognitive and speed ability tests and education, demographic, health, technology, and computer experience questionnaires (Czaja et al., 2006). We excluded 1 older adult due to ability scores and experimental task performance scores more than two standard deviations below the mean.

Table 1 presents a subset of the ability data to compare age-related differences as well as the pre-experiment differences between the training groups within each age group. Overall age differences were typical (all ps <.05): Younger adults self-reported better general health and performed better on measures of perceptual speed, memory span, reaction time, and working memory. Older adults reported higher levels of education. There was not an age-related difference in vocabulary (p =.49).

Materials
Hydroponic garden control system
We designed a computer simulation of a hydroponic garden control system to simulate a complex menu structure with multiple functions, variables, and systems states (see Figure 1). Hydroponic gardening is gardening without soil using another growing medium, such as nutrient-enriched water. The system had an intentionally complex structure and was designed to allow learning to occur and to minimize ceiling effects.

View larger version (65K): [in this window] [in a new window]   Figure 1. Screen captures of the six main displays comprising the hydroponic gardening control system. The system was intentionally complex to allow for learning to occur. Participants navigated amongst the screens by using the radio buttons at the top (e.g., Seeds, Medium, Climate) or by selecting the Settings, Advanced Settings, or Messages buttons. There were multiple functions on each screen, some of which were specifically trained and some of which were only included in the test phase. For example, on the Medium Selector Screen (top middle), the level could be selected by manually entering a number or by using one of the preset buttons (labeled Preset 1, Preset 2, Preset 3, and Preset 4). As is illustrated in Figure 2, during training participants used the manual selection method, but at test we asked them to use one of the preset buttons

Mouse training
We provided mouse training to establish familiarity with the primary functions of the mouse that participants would need for the simulator system, such as moving the pointer and clicking the left mouse button. In addition, this training provided a base level of knowledge of the operating controls used in the system (e.g., drop down menus, horizontal and vertical sliders). We determined successful mouse training by a 90% accuracy criterion and gave participants three attempts to achieve this level of accuracy. If the participant did not reach this criterion, we replaced him or her. We replaced 1 older adult in each training condition.

System training
We designed two training tutorials to present the structure of the system: guided action training and guided attention training. The hydroponic garden control system was computerized and presented such that each task began from the same initial system screen. The task instructions were printed on paper and displayed in a flip book beside the system. The left side of Figure 2 illustrates exactly what participants saw in the flip book when completing the training tasks. In the guided action condition, the training material provided the specific action that the participant had to take. For example, for the task "Adjust the gel medium to level 105," the first step was to "Click medium," followed by "Click gel"; the steps proceeded in a "Click this" manner. In this training condition, participants had to perform each action as described in the training materials.

View larger version (30K): [in this window] [in a new window]   Figure 2. The left-most side illustrates the training materials for the guided action and guided attention training conditions. This is the form in which the training materials were presented—step-by-step instructions telling the participants what to do on each trial. On the right-most side of the figure are examples of the task instructions for the two types of test trials: a trained task (which was explicitly trained) and an untrained task (which was from the same system display screen but not previously trained). Note that for the test trials participants did not receive the step-by-step instructions; they only received the task goal

Participants in each training condition completed the same 35 task trials comprising the goal and the steps required to accomplish that goal. The critical difference was that participants in the guided action training were told exactly what to do at every step, whereas participants in the guided attention condition had to do more active processing of the information to determine the appropriate actions to perform.

We told participants to complete the training trials at their own pace with the goal of learning the system. That is, we told them to emphasize accuracy and that they would be tested after the training to assess what they had learned.

Assessment measures
We measured task completion time and accuracy of performance (a) during training, (b) at test for trained tasks, and (c) at test for untrained tasks. We calculated task completion from the time the participants pressed Start for a trial until they pressed Stop to indicate they had completed the trial. We did not include tasks that had not been completed correctly in the average task completion time. We calculated accuracy as the number of tasks that the participant had completed correctly. In order for the task to be counted as correct, the participant had to complete every step of the task in the right order.

Figure 2 illustrates the difference between training trials and test trials. For the training trials, we provided participants with the task goal (e.g., "Adjust the gel medium to level 105") as well as the steps necessary to accomplish the goal (see left side of Figure 2). For the test trials, we provided them with only the task goal (see right side of Figure 2).

There were two types of test trials: trained tasks and untrained tasks. The trained tasks were a subset of the tasks completed during training. They assessed participants' ability to perform functions for which they had received training on each of the six system displays (see Figures 1 and 2). The untrained tasks assessed participants' ability to perform novel tasks on these same displays (see Figure 2). The untrained tasks were of similar difficulty to the trained tasks, because they were from the same screens and had an equal number of steps. We instructed participants to complete the test trials as accurately as possible.

The test trials consisted of 17 trained and 17 untrained tasks, which were intermixed and randomly ordered, with the restriction that no more than 2 trained or 2 untrained trials were presented consecutively, nor were 2 tasks from the same screen presented consecutively.

Procedure
The study took place over three sessions on different days. Session 1 consisted of ability tests conducted in a group testing session. During Session 2, participants received more ability tests and mouse training in an individual testing session. During Session 3, participants received refresher mouse training followed by the 35 training trials and then the 34 test trials. Participants completed all three sessions within a 2-week span.

Design
This design was a 2 (Training Condition) x 2 (Age) between-participants design. The between-participant independent variable was training method (guided action or guided attention). Age group (younger or older) was a quasi-independent variable. Dependent variables were accuracy (percent correct) and task completion time (in seconds).

	RESULTS

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Training Trials
One assessment of training benefits is the level of performance during training, when the training materials are available. In this case, the instructions consisted of step-by-step instructions that specified the action to take (guided action training) or step-by-step instructions that identified the relevant task concepts in the display that should be attended (guided attention training). During training, participants tended to perform better for the guided action condition.

Figure 3a and Table 2 present the mean completion times for the 35 training trials. Younger adults were significantly faster overall, F(1, 59) = 146.91, p <.01, ² =.71. However, the type of training provided also influenced performance: There was a significant main effect of training condition, F(1, 59) = 18.80, p <.01, ² =.242, and a significant Age x Training Condition interaction, F(1, 59) = 15.83, p <.01, ² =.21. Follow-up analyses of task time revealed that the benefit of the guided action training was significant for older adults, F(1, 29) = 17.80, p <.001, ² =.38, but not for younger adults (p =.47).

View larger version (18K): [in this window] [in a new window]   Figure 3. Mean task completion time (with standard error bars) for younger and older adults. (a) training trial data; (b) test trial data for trained and untrained tasks

Test Trials: Measures of Learning
We assessed learning through two test conditions that measured transfer of system knowledge. During test trials, we gave participants the task goal but not the step-by-step guidance information (see Figure 2). The trained tasks were trials that participants had performed during the training phase of the study—however, participants had to remember the steps. The untrained tasks were novel tasks that participants had never performed—they had to be able to generalize their system knowledge to be able to perform these tasks.

Trained tasks
The left panel of Figure 3b and Table 2 present the mean task completion times for the 17 trained task trials. Younger adults performed the tasks faster than older adults, F(1, 59) = 95.46, p <.01, ² =.62. However, there was no task time difference between participants in the guided attention versus the guided action condition. The main effect of training condition was not significant (p =.23), nor was the Age x Training Condition interaction (p =.85). A post hoc test did show a benefit of the guided attention training for younger adults, F(1, 30) = 6.70, p <.05, ² =.18, but not older adults (p =.49).

For accuracy there were significant main effects of age, F(1, 59) = 66.53, p <.01, ² =.53, training condition, F(1, 59) = 11.53, p <.01, ² =.16, and their interaction, F(1, 59) = 8.16, p <.01, ² =.12. Contrary to the pattern observed during training, accuracy rates were higher for the guided attention condition relative to the guided action condition (see Table 3). Follow-up analysis revealed that the benefit of guided attention training was significant for older adults F(1, 29) = 10.01, p <.01, ² =.26, but not for younger adults (p =.25).

In sum, although the guided action trainees were better able to perform tasks during training (Figure 3a), this advantage did not remain when the training materials were no longer available. When required to perform the trained tasks based on what they had learned, the guided attention trainees performed better than the guided action trainees (left panel of Figure 3b)—more accurate for older adults and faster for younger adults.

Untrained tasks
Perhaps a more sensitive measure of how well participants have learned to use a system is their ability to perform tasks for which they did not receive specific training. As shown in the right panel of Figure 3b and in Table 2, the younger adults performed the untrained tasks faster than the older adults, F(1, 59) = 87.12, p <.001, ² =.60. Moreover, performance was faster for the guided attention training group, F(1, 59) = 7.55, p <.01, ² =.11, and this effect interacted with age, F(1, 59) = 3.82, p <.05, ² =.06. Follow-up analyses indicated that the benefit of guided attention training was significant for both younger, F(1, 30) = 6.05, p <.05, ² =.17, and older adults, F(1, 29) = 5.49, p <.05, ² =.16, but the benefit was larger for the older adults, as is evident in the right panel of Figure 3b.

For the accuracy data there were also significant effects of age, F(1, 59) = 57.11, p <.001, ² =.49, and training condition, F(1, 59) = 8.69, p <.01, ² =.13, as well their interaction, F(1, 59) = 4.32, p <.05, ² =.07. As observed for the trained task test trials, accuracy rates were higher for the guided attention condition relative to the guided action condition (see Table 3). Follow-up analyses revealed that the benefit of guided attention training was significant for older adults, F(1, 29) = 6.56, p <.05, ² =.18, but not for younger adults (p =.10).

In sum, the differential benefits of training were clearly evident for tasks that had not been specifically trained (i.e., untrained tasks). Participants who had received guided attention training were faster (both younger and older adults) and more accurate (older adults) than participants who had received guided action training.

The role of working memory
We designed the guided action training condition to reduce demands on working memory capacity, whereas the guided attention training condition presumably imposed demands on working memory. We randomly assigned participants to training condition, thereby distributing individual differences in working memory (the training groups did not differ within each age group; see Table 1). Not surprisingly, there were age-related differences in working memory. However, older adults showed even greater benefit of the guided attention training than younger adults. These data suggest that the guided attention training protocol provided sufficient guidance to enable older adults to perform the task and to learn the system but not to overload their working memory capacity.

We conducted an analysis of covariance using working memory (alphabet span) as the covariate. For younger adults, the analysis of covariance did not change the results. For the older adults, the guided attention benefits became larger (² increased) when within-group individual differences in working memory were controlled. For the accuracy for the trained trials, eta-square increased from.26 to.32; for the untrained trials, eta-square increased from.18 to.24. These data suggest that working memory is an important mediating variable for guided attention training benefits.

	DISCUSSION

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When step-by-step instructions were available during training, participants who received guided action training performed faster (for older adults) and more accurately (for both age groups) than did participants who received guided attention training. Thus if training materials will always be present (e.g., in the form of a checklist) when a user performs tasks on a given system, then it is preferable to provide the specific actions for the user to perform. For example, a medical device might come with an attached list of step-by-step instructions that specifies the actions to take in sequence.

However, interactions with technology systems often take place when training materials are not available (e.g., retrieving a message from voice mail, troubleshooting an error on a recording device, entering an address on a navigation system). Consequently, our goal was to understand how best to support system learning so that users would remember what to do or could generalize their knowledge to novel tasks on the system. When participants had to perform tasks on the present system when the training materials were no longer available, there was clearly a benefit for those participants who had originally received guided attention training relative to those participants who had received guided action training.

Benefits of attention training were evident for younger and older adults and for trained tasks as well as untrained tasks. For older adults, the benefits of the guided attention training were evident for both accuracy and task completion time (see Tables 2 and 3). For younger adults, the benefits of guided attention training over guided action training were primarily evident in task time—they were 23% faster for trained tasks and 24% faster for untrained tasks. The training benefits were not evident for accuracy (perhaps due to a ceiling effect). However, similar accuracy performance across conditions makes the task time data even easier to interpret (Luce, 1986): Those younger adults who had received guided attention training were able to perform the test trials more quickly, indicating they had learned the system better compared to those who had received guided action training.

From a practical perspective, these data have clear implications for the development of training programs to optimize training for younger and older adults learning to use a technology system. If the goal is fast and accurate performance with training materials in view, then guided action training is best, especially for older adults. If, however, the goal is to support learning to enable people to use systems even when the training materials are not available, guided attention training is best (again, especially for older adults).

These data also have relevance for the development of a framework or model for understanding training and aging. Clearly, it is inappropriate to take a simplistic view that older adults will always learn better in the least working-memory–demanding condition. Learning demands working memory (e.g., Fisk & Schneider, 1983; Schneider & Chein, 2003; Schneider & Fisk, 1982, 1984; Schneider & Shiffrin, 1977). Learners must activate relevant information in working memory and allocate attention to processing that information and linking it to other information (either other new information or information stored in long-term memory). The covariate analyses indicated that individual differences in working memory mediate the benefits of different training procedures.

Training must be designed to facilitate learning. However, it must also be designed to require attentional processing by the individual, lest the person become so reliant on the training materials that he or she does not learn to perform the tasks. This is presumably what happened to the guided action trainees in the present study: When the materials were present, performance was very good, but when the training materials were absent, performance was quite poor.

How can we, as gerontologists, prepare older adults to learn to interact effectively with new technologies? Gerontologists must evaluate the task demands, specify the ultimate task goals, identify the relevant system components, understand how they are interrelated, and provide a means to guide the learner's attention in a way that challenges him or her to process and relate the information but at the same time provides support for learning. This recipe may sound relatively easy, but it requires a thorough understanding of the task, the learner, the task environment, and training methodologies (see Rogers, Campbell, & Pak, 2001).

Older adults are capable of learning to use new technologies. The optimal training method will likely involve a combination of specific training procedures. The present study makes clear the relative benefits of guided attention versus guided action training for younger and older adults and suggests how experts should incorporate these types of training into a training program.

	Acknowledgments

 
This article is based on the master's thesis Age-Related Effects of Action Versus Concept Training on Developing a System Representation by the first author (Hickman, 2004). This research was supported in part by Grant P01 AG17211 from the National Institute on Aging under the auspices of the Center for Research and Education on Aging and Technology Enhancement, Grant R01 AG18177, as well as a seed grant from the Georgia Gerontology Consortium. This research was also supported in part by contributions from Deere & Company. We thank David Sanderson for his assistance with data collection and Neil Charness, Jerry Duncan, and Bruce Newendorp for their support and advice on this research.

Portions of these data were presented at the 111th (Toronto, Ontario, Canada, August 2003), the 112th (Honolulu, Hawaii, August 2004) Conventions of the American Psychological Association, and the 14th Annual Southeastern Student Convention in Gerontology and Geriatrics (Tybee Island, Georgia, March 2003). Portions of these data appear in the proceedings of the 47th Annual Meeting of the Human Factors and Ergonomics Society (Denver, CO, October 2003; Hickman, Rogers, & Fisk, 2003).

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