Harnessing Experiential Learning Theory to Achieve ...

Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2006

Harnessing Experiential Learning Theory to Achieve Warfighting Excellence Dr. Ellen Menaker, Dr. Susan Coleman, Mr. Joe Collins, and Dr. Marci Murawski Intelligent Decision Systems, Inc. Centreville, VA [email protected]; [email protected]; [email protected]; [email protected]

ABSTRACT New technologies have invigorated the need to understand and apply experiential learning theory in ways to optimize learning. Large financial investments are being devoted to high-fidelity games and simulations for the purpose of learning. Integration of experiential learning into computer-based learning, games, and simulations for learning offers a powerful strategy to achieve warfighting excellence. This paper contends that this strategy must be more than just offering the learner an environment to experience. This paper provides an overview of experiential learning theories and describes how science of learning research can be incorporated into designs that unleash the powerful combinations of new technologies and new understanding about learning.

ABOUT THE AUTHORS Ellen S. Menaker, PhD, CPT, is the Chief of Research and Evaluation for IDSI. Dr. Menaker oversees the design, data collection, and analysis phases of front-end analyses and evaluation studies. She also specializes in all phases of the instructional systems design process. She earned a PhD in Research and Evaluation and has over 30 years of experience in the training and education fields. Her academic and industry experiences include conducting research for various military, governmental, and educational entities. Recent studies include human performance analyses, effectiveness evaluations, and front-end analyses. Susan L. Coleman, PhD, CPT, is the Chief of Human Performance Technology for IDSI. Dr. Coleman oversees all phases of human performance analyses, while specializing in instructional systems design processes. She earned a Ph.D. in Instructional Technology and Design and has analyzed performance and designed and developed performance solutions since 1983. She spent the last 12 years analyzing and designing training systems for the military. She has conducted training effectiveness evaluations, design analyses, technology integration front-end analyses, and human performance improvement analyses. Joe Collins is the Director of Operations for IDSI and has over eight years experience in all phases of instructional systems development. Mr. Collins spent three years as a Course Curriculum Model Manager (C2M2) and Training Support Officer (CISO Assistant) for several Navy technical training programs. Mr. Collins currently develops computer aided instruction (CAI) and interactive courseware (ICW). conducting analyses, designing storyboards, creating graphics, and providing subject matter expert reviews. Mr. Collins also provides research and production support for numerous Navy-oriented development projects, including BQC, OS "A" School, MN "A" School, FTC Norfolk Engineering School, Surface Warfare Officer’s Division Officer Course re-engineering, and more. Marci Murawski, EdD, CPT, is the President and CEO of IDSI. She directs the strategic operations of a dynamic company in the business of human and organizational performance. Dr. Murawski believes that learning theory supported by hard scientific research can be integrated with performance technology and technical expertise to achieve higher levels of potential than ever thought possible. This approach has proven successful over her career of working with a wide variety of government, academic, and corporate clients, including the U.S. Navy, Army, Air Force, Marines, and Coast Guard. Dr. Murawski earned her Ed.D. in Instructional Technology with minors in Educational Psychology and Research and Evaluation.

2006 Paper No. 2974 Page 1 of 10


Harnessing Experiential Learning Theory to Achieve Warfighting Excellence Dr. Ellen Menaker, Dr. Susan Coleman, Mr. Joe Collins, and Dr. Marci Murawski Intelligent Decision Systems, Inc. Centreville, VA [email protected]; [email protected]; [email protected]; [email protected] INTRODUCTION Experiential learning has re-emerged as a popular topic with the advent of interventions that create complex virtual environments for learners to explore. New technologies enable us to manipulate environments and collect data on user performance never before possible. Energy and excitement about the possibilities for learning applications with high-fidelity games and simulations are being supported with large financial investments; however, opportunities to achieve excellence may be missed. We believe the integration of experiential learning into computer-based instruction, games, and simulations for learning is a powerful strategy to achieve warfighting excellence, but an understanding of experiential learning theory is essential to deliberately structure these environments for optimal learning. Experience alone may not be an effective or efficient strategy for developing the knowledge and skills, including adaptive thinking, problem solving, and decision making, which must undergird warfighting excellence. The complexity of the situation and the need to accelerate the development of warfighters is felt throughout the military as the following statement indicates: Operating within an uncertain, unpredictable environment, the Army must be prepared to sustain operations during a period of persistent conflict—a blurring of familiar distinctions between war and peace. (Army 2005 Posture Statement as cited in Hannah & Lester, 2006, p. 5) Preparation translates into the development of individual warriors who are now required to be multifunctional. As Hannah and Lester (2006) describe, “a soldier may deploy to Iraq as a mechanic or artillery gunner, but then be required to conduct combat patrols—a task typically assigned to the infantry” (p. 11). Logic might lead us to believe that the more we can immerse this soldier in a potential environment with authentic tasks, the better prepared he or she will be. Although the central premise of this may be true, it is not quite as simple as “Build it and they will learn” or “Make it as authentic as possible so they can learn.” In fact, we can and must be deliberate in our efforts to provide a framework that enables


learners to develop the knowledge and skills needed to adapt to changing environments. To create the optimal learning environments we must examine research that has emerged during the past decades relating to how individuals learn and apply it to experiential learning approaches (e.g., Bransford, Brown, & Cocking, 1999). Over the years experiential learning has taken on several names and banners; each will be discussed briefly so we can distill the key elements that must form the foundation for a learning system that optimizes the impact of experience. This paper explains the tenets of experiential learning and illustrates how to harness the power of experience with deliberate, engaging instructional strategies. Experiential learning is more than just providing the learner experience, and we distinguish between providing an environment in which learning can occur by happenstance and creating one in which actions result in learning opportunities with guidance and feedback strategies. In this paper we address: •

Theoretical learning

•

Current related research

•

Implications for instructional design

•

Strategies for optimizing experiential learning using a desktop simulation as an example

foundations

for

experiential

THEORETICAL FOUNDATION FOR EXPERIENTIAL LEARNING Experiential education stems from the beliefs about learning espoused by theorists including John Dewey, Jean Piaget, Kurt Lewin, and David Kolb (Beard & Wilson, 2002; Kolb, 1984). These theorists contend that learning occurs in a cycle. Although they differ in how they express the cycle, they agree that learning takes place as an individual changes thinking based on an experience and, most importantly, by reflecting on that experience. Learners revisit that thinking again and again as they experiment in new situations, modifying their thinking through the results of new experiences. The following steps summarize the process:


1.

Experience or interact with the environment.

2.

Observe behavior and reflect on experience.

3.

Generalize or form abstract concepts based on reflection.

4.

Experiment and add to or modify concepts based on new experiences.

The degree to which these steps occur spontaneously is a point of departure for learning theorists. One end of the spectrum promotes an unguided approach in which the learner freely explores and constructs meaning through an unstructured process. Early work described as discovery learning (Brunner, 1961), or later problem-based, inquiry-based, experiential learning, and constructivist (as depicted in an analysis by Kirschner, Sweller, & Clark, 2006), adopt the central tenet that the learners must interact with the environment to construct their own knowledge structures. At the core of these approaches is the belief that learners learn by being immersed in an authentic environment. As they make sense of it and build their own cognitive structures, they develop a deep understanding. Multiple experiences are often required for the learner to make the correct attribution and begin building sound scientific theory. While no one would argue that there should not be some structure, some theorists at the other end of the spectrum promote creation of guided experiences that deliberately structure the environment and/or sequences of events based on learner factors, including prior knowledge and previous experience (Kirscher et al.; Schnotz & Rasch, 2005). We advocate a more guided approach that exposes learners to experiences that structure this cycle deliberately. Rather than wait for this process to happen by chance, specific environments are created and learners are exposed to varied activities and events as part of a deliberate but flexible plan. The type of information available to the learner/decision maker is manipulated, environmental factors are varied, and events are sequenced to optimize opportunities to develop skills. To structure this cycle, experiential learning events must: •

Engage the learner mentally.

•

Emulate real-world requirements. Real-world refers to the physical environment and the cognitive tasks. We refer to these as physical fidelity and cognitive fidelity respectively.


•

Allow the learner to experience effects of decisions. Effects can be naturally occurring (e.g., you are ambushed) or delivered by an instructional agent (e.g., a superior questions your actions).

•

Require learner to reflect on outcomes of their actions. Build on established military practices of debriefs, lessons learned, and after action reports.

•

Revisit experiences increasing complexity of experiences to expand learners’ knowledge and skills by increasing number of events, pacing and emotional intensity.

The design of experiential learning environments provides opportunities to construct meaning through experience and with deliberate focus on reflection. This sequence of events has important implications for the development of adaptive thinking. To achieve optimal results, opportunities or experiential events must accommodate differences in individuals’ prior knowledge and address the development of tacit or practical knowledge (Kalyuga, Chandler, & Sweller, 2001). Tacit knowledge, introduced by Polyani (as cited in Sternberg, et. al, 2000), is characterized as “knowledge gained from everyday experience that has an implicit, unarticulated quality” (p. 104). An expert, for example, may appear to just know what to do intuitively and may not be able to give an explanation for how he or she knows what to do. Research indicates that this tacit knowledge may be depicted as complex mental representations that are not directly available to conscious introspection (Sternberg, et. al, 2000). As we try to accelerate the development of adaptive thinking, we provide opportunities for experiences and reflection to build learners’ repertoire of cognitive representations. But, the question of how to accelerate this development and the appropriate mix of exploration and guidance is a topic of great concern.

APPLYING RECENT RESEARCH How can instructional designers translate the best research into designs that build sound cognitive mental representations and accelerate the development of warfighter excellence? We fear that the pendulum has swung to the physical fidelity because technology enables us to create more realistic environments, often at the expense of the cognitive fidelity and reflection aspects of design. Current research provides insight into the learning process that designers must translate into computer-based instruction, games, and


simulations to optimize the impact on learners. The distinction between exposing a warfighter to a simulated environment and exposing the warfighter to a simulated environment infused with proven strategies is subtle in the eyes of the learner but important from the standpoint of accelerating the learner’s development. In this section we discuss the relationship between experiential learning and the development of cognitive structures. Specifically we address the application of research on cognitive load and emotional load. Prior Knowledge and Existing Schemata Learning takes place as we change our cognitive structures. During the process the brain must take in the new information and make sense of it. How much we should guide those processes and when that guidance should be offered remains a serious question within the education and training communities (Kirschner, Sweller, & Clark, 2006; Mayer, 2004). In essence, the learner must build a database, but it must link to what the learner already knows and be put in that database in a way that will make it accessible when needed. This recognizes impact of prior knowledge. What can be a meaningful experience of adding to the database or knowledge structures for a relative expert can be a confusing experience adding only frustration and a sense of incompetence for the novice. Recognizing Impact of Cognitive Load Recent research on cognitive load focuses on how much information we can take in at a given time to make sense of it all (Sweller, 1988; Clarke, Ayres, & Sweller, 2005). What we cannot forget is that experts have an extensive array of experiences stored in longterm memory. In his recent best seller, Blink, Malcom Gladwell (2005) refers to these unconscious databases. As we prepare warfighters for challenging, uncertain situations, we recognize that the richer their databases, the more available mental resources they will have to apply or reconfigure in response to dynamic, unpredictable situations. We contend that experiential learning should be designed to fill those databases with accurate representations and awareness of relationships among them to promote ready access. Experiences should be structured to develop principles and procedures of the discipline, and to optimize the interactions between working memory (the workspace that processes new information and attaches it to previously learned information) and long-term memory, where information and representations are held permanently.


The clogging of workspace has been examined in a body of research relating to cognitive load. Researchers have empirically tested the impact of overload on the senses and distinguish between the amount of information that novices can digest at a given time— the number of pathways (i.e., visual, verbal) that can be accessed—and how well the learner can regulate this on his or her own (Mayer, 2001; Moreno & Valdez, 2005). We do this all the time when we turn down the television while getting a phone number. Yet, as we strive for authenticity in our simulated environments, we must be careful to draw the learner’s attention to the relevant characteristics at least at the outset, so he or she can add to that array of experiences in a meaningful and accessible way. Harnessing Emotional Load An additional advantage of experiential learning environments is the ability to create a higher level of emotional tension. In a well-designed environment, the warfighter can experience the high-level emotions of the battlefield, and such training is important. However, knowing when, how much, and the type of stress to incorporate during the learning process has important implications. Research demonstrates that creating an emotional impact can strengthen memory (e.g., we all remember details of where we were when we first heard about 9/11). Through carefully crafted learning experiences, the brain will imprint the emotional arousal felt during the experience, adding strength to the memory (Goleman, 1995). On the other hand, during periods of stress emotions can overload the working memory, interrupting our ability to process incoming information properly and thereby diminishing cognitive function and the ability to learn from an experience. With the right kind of experience, however, learners can learn to control their emotions, freeing their working memory to fully perform. Harnessing the emotional aspect of experiential learning takes advantage of its ability to teach emotional control. Recognizing the Limitations of Experience Alone in Building a Cognitive Database Merely acting or interacting with the environment may not promote change. In daily life we know all too well that we and others repeat the same mistakes without fully understanding why. Cognitive and emotional loads may prevent learners from being aware of their actions or recognizing the impact of them. While pure constructivist theories of human cognition assume users interactively refine their understanding of an area to construct their own knowledge (Perkins, 1986), the number of repetitions needed to construct knowledge


databases may increase learning time and limit the efficiency. Experts differ from novices not so much in the amount that they know, but in the way that knowledge is organized (Bransford, Brown, & Cocking, 1999; Chase & Simon, 1973). How can designers use this research to inform computer-based instruction, games, and simulations?

Faulty Assumption: Simulations must be at the highest degree of physical fidelity to heighten the experiential value to the learner. •

High environmental fidelity can contribute to cognitive overload. Fidelity has to be considered as part of the instructional strategy and managed to maximize instructional effectiveness.

•

Controlling environmental fidelity while providing high cognitive fidelity may be an effective design for some learner populations but not others.

WHAT DOES THIS MEAN FOR DESIGN? As trends emerge and clients insist on the most up-todate strategies and whiz-bang effects, instructional designers must ensure that design provides the essential instructional elements to support their superficial appeal. Whether or not we adhere to what Fox calls functional constructivism as an approach to enhancing experiential learning, we agree with the concern he raises: Overemphasis on media development threatens to intellectually bankrupt the field, as instructional technologists move farther and farther away from any kind of grounding in a science of learning. (Fox, 2006, p. 22) Designers often work with consumers who operate under faulty assumptions. Much is based on the central belief that experience alone (i.e., immersing the learner in a high-fidelity environment) will achieve the intended learning outcomes). We present several faulty assumptions associated with this belief and provide research-driven counterarguments or qualifications for each. Faulty Assumption: If you experience something you will learn from it. • Learning occurs only if you reflect on it or add it to a schema or mental model. Faulty Assumption: Physically experiencing something is more powerful than reading about it or seeing a video. • While experience is a valuable teacher, outcomes will be sub-optimized if: - cognitive overload prevents learners from understanding relevant aspects of the experience, - learners encode improperly by focusing on the wrong thing or not making connections between concepts, or - learners do not receive feedback on errors or understand what they learned from the experience.


Faulty Assumption: Any form of doing is more effective than being taught. •

Well-designed instructor-led training can be more effective than poorly designed experiential learning. The key is which form is more effective for helping the learner make sense of the content.

•

Evidence suggests that promoting constructive learning requires that the learner engage in cognitive activity rather than behavioral activity (Mayer, 2004). Whatever the learning activity, it is the cognitive engagement that is important.

•

Instructional guidance can be more powerful than pure discovery learning.

•

Curricular focus rather than unguided discovery can lead to more efficient learning (Mayer, 2004).

Faulty Assumption: All levels of learners will find something of value when allowed to experiment in an interactive learning environment. •

There are vast differences in the mental models and schemata of experts and novices. What adds to knowledge for one group may detract for another group (Kalyuga, Chandler & Sweller, 2001; Schnotz & Rasch, 2005).

•

It is important to understand the target population’s metacognitive skills as well. This information will help determine the kind of scaffolding as well as the specificity and frequency of feedback required in the instructional design (Costa, 2001).


Faulty Assumption: Making decisions about what to learn or following one’s own discovery path results in deeper learning. •

Instructional guidance is more effective and efficient than pure discovery (Mayer, 2004).

•

Learners’ judgments regarding their learning preferences often do not result in better learning. (Schnackenberg, Sullivan, Leader, and Jones, 1998, as cited in Clark, 2003).

Faulty Assumption: Learners must be active; therefore books, lectures, and online presentations will not promote learning. Discovery learning is the best way to promote learners as active sense-makers. • •

Instructional designers must balance cognitive and behavioral activity. Activity may help promote meaningful learning; but instead of behavioral activity per se, the kind of activity that promotes meaningful learning is cognitive activity (Mayer, 2004).

Faulty Assumption: Authentic feedback (i.e., natural consequences of actions) is the best kind of feedback and is sufficient to promote learning. •

Authentic feedback is effective but is insufficient for some learners and in some situations. Learners must understand the concepts and principles surrounding the experience to know how their actions contributed to the experienced outcomes.

•

The type and frequency of feedback learners receive will impact learning.

scores because they are a good players, not because they have mastered the key concepts. To optimize experiential approaches we contend that a guided approach, particularly for novices, must be adopted. Research exhibits a long history showing merits of guided discovery as summarized by Shulman and Keisler in 1966 (as cited in Mayer, 2004). Learners also need an element of freedom to discover and construct understanding of the concepts being learned. Balancing instructional guidance and learner discovery is key to effective experiential learning. Learners need enough freedom to become cognitively active in the process of sense making and need enough guidance to ensure that their cognitive activity results in the construction of useful knowledge (Mayer, 2004, p. 16).

OPTIMIZING EXPERIENTIAL LEARNING— BALANCING DISCOVERY WITH GUIDANCE So what does good design based on experiential learning theory look like? There is no single formula that should be applied in every case; however, key guidelines influence instructional strategy decisions. In this section, we offer several guidelines for applying experiential learning theory. We also provide examples of what each guideline might look like when used, in this case a self-paced desktop simulated exercise developed for the Navy’s Operations Specialist (OS) “A” School. This scenario was designed to simulate the multitasking required of an OS in an underway tactical watch environment in the ship’s Combat Information Center (CIC). A sample screen from the simulation is shown in Figure 1. We contend that the same guidelines form the core of the necessary architecture for more technically sophisticated computer-based learning, simulations, and games.

Faulty Assumption: Games by their very nature embody experiential learning. •

Games offer great potential (Mitchell & Savill-Smith, 2004; Pensky, 2001), but experiential learning is more than accumulating experience. For experience to be an effective instructional event, it must be purposeful, it must be reflected upon, and it must allow learners to apply what they have learned. Good instructional design can make this happen.

•

Game scores must reflect the things you want learners to learn. It is important that learners not “game the game,” leading to inflated


Figure 1. Radar Display and Contact Log Entry


1. Balance cognitive fidelity and physical fidelity. Fidelity is cognitive as well as physical (and affective). Cognitive fidelity requires learners to use information to think or act as they would in the operational environment. We often rely on scenarios that place the learner in the role he or she would assume on the job and provide information in a manner in which that individual would receive it. The cognitive processes required on the job should be mirrored in the instructional requirements, providing what we call cognitive fidelity. The learner is then challenged to use the information as he or she would on the job. This approach engages the learner, mentally increasing the likelihood of knowing the content long after the exercise is over (Bates & Poole, 2003). CIC Example: The example scenario simulates one aspect of a CIC watch. This scenario provides both physical and cognitive fidelity. The display depicted in Figure 1 shows the radar interface that CIC watchstanders would normally encounter and provides simulations of the actual radar controls they would use to manipulate the radar display. The information collected through those actions must be processed, evaluated, and recorded in the ship’s contact log. An excerpt from the simulated log is also shown as an overlay in Figure 1. In later steps the learner will actually simulate disseminating the information to other shipboard watchstanders. Therefore, learners must: •

collect and process data as they would on the job,

•

operate equipment controls to properly obtain accurate bearings and ranges,

•

record data as they would on the job, and

•

determine the watchstander to whom they must pass on the information.

2. Sequence experiential understanding.

events

to

promote

The complexity of the scenario or game should be based on learners’ prior knowledge and experiences. Conscious design decisions regarding the type of experiences learners have help them to build accurate mental models and control cognitive load by providing time for learners to process the information. Experiential learning theory calls for a cycle of experience, observation/reflection, generalization, and experimentation. The sequence needs to include all elements of this cycle. Carefully planned events allow


learners to experience, reflect, generalize, and then reuse what they already learned in new applications. CIC Example: With each scenario, learners are challenged to apply what they have already learned in new ways, as well as to use what they just learned appropriately. This allows them to refine their thinking by experimenting with the generalizations they produced as a result of earlier lessons. 3. Deliberately increase tempo and complexity of experiential environments and tasks. In the real world, tasks with varying degrees of complexity often happen simultaneously or in rapid succession. We must consciously and deliberately control sequence, time between events, and the intensity of events to enable the learner to process rather than overload cognitive structures. This guideline also addresses the role emotional intelligence plays in performance and its impact on cognitive processing in high-stress situations. Carefully managing cognitive load during the knowledge acquisition phase enables the development of mental representations that can serve as a well-populated database from which the learner can retrieve under dynamic and unpredictable situations. Pace can then be accelerated by adding events. For example, a learner performing a task (e.g., plotting a position on the navigation chart) may suddenly receive an inquiry on a contact from the simulated Bridge. The learner will need to leave the initial task to collect, process, and evaluate the information needed to respond to the inquiry. This drives home the reality of whom they really work for: watchstanders are there to provide the Bridge the information they need on demand, when they need it. Watchstanders come to understand what it means to multi-task and be responsive in the CIC. In addition, learners learn to anticipate the needs of the Bridge and to be more proactive. CIC Example: The screen capture shown in Figure 2 represents the CIC watchstander’s navigational plotting task at the lowest level of complexity. In the early stages of the simulation the learner obtains position data from the Global Positioning System (GPS) and plots a singular position on the chart, as shown. In later tasks the learner will be required to plot multiple positions and perform advanced calculations based on those plotted positions. •

Initially few variables are presented at a slow pace.


•

As they demonstrate skill, multiple events occur at the same time and learners must multitask, using what they learned in a cognitively complex scenario. They receive more data at one time, must respond to other events that serve as interruptions, and are given inconsistent data which they must reconcile

In the CIC scenarios it was also necessary to strategically balance how far to let learners proceed through the scenario before providing feedback. The answer to that challenge most often depended on the complexity level of the tasks at hand, the potential damage they could do by proceeding incorrectly, and the authenticity of the timing and type of feedback provided. 5. Provide deliberate opportunities to promote reflection. Use techniques to make learners aware of what they have done and why they have done it. This is the only way to ensure learners can replicate behavior under the appropriate circumstances. This helps them construct appropriate relationships within their developing mental models. One technique is to provide overt scaffolding for novice learners to make them aware of what they are thinking and then eventually remove the support. Support and prompts inserted into instruction at the right time encourage learners to consider important factors or examine the impact of specific ones.

Figure 2. GPS Readout and Navigation Chart Plot 4. Use a purposeful feedback strategy. The timing and type of feedback are critical. Learning will not occur without some form of effective feedback. While authentic feedback is a viable strategy, it often is not enough. It is important that learners not stumble on the right answer, unaware of the reason for their correct decision. Strategies must be in place so learners know what they have learned and designers know learners learned what was intended. CIC Example: Feedback is given in several ways throughout the underway watch scenarios. Learners receive: •

authentic feedback in the form of comparative expert examples of logs and plots,

•

tailored audio responses to voice reports, and

•

debriefs with the facilitator at the end of the session. For example, when reporting coordinates to the Bridge if their calculations were wrong; the Bridge provided a realistic oral response such as “The Bridge does not concur (with your calculations and/or evaluation).”

CIC Example: Reflective activities are built into the scenario in the following ways: •

Learners activity.

•

Learners must compare logs to charts, account for unexpected reports, and reconcile inconsistencies.

•

Learners are debriefed by the facilitator who plays the role of the CIC Watch Officer after the watch was completed. They discuss their actions and the cause and effect of their actions, with the facilitator providing additional guidance and insights into how to best accomplish those tasks. Facilitators often debrief students in groups to promote more diverse and effective reflection and feedback.

complete

logs

following

each

CONCLUSION


Learning through experience is a powerful approach when combined with today’s advanced learning technologies. Learning through experience, however, can also be inefficient or, worst case, ineffective in achieving learning goals. Pairing the right learning theory with the right technology can result in tremendous learning and performance outcomes. However, care must be taken to ensure that the


potential of technologies is realized by thoughtful application of appropriate strategies by experts in instructional design. Just as expert warfighters are developed through purposeful experiences with appropriate feedback, instructional designers must also be educated with purposeful experiences and feedback. Arbitrary experience alone is insufficient to prepare instructional designers for the design challenges presented by new simulation and gaming technologies. Successful warfighters require not only weapons and ammunition, but also intelligence to locate the target. Similarly, designers require not only technology and content, but also the appropriate strategies to ensure that they meet the instructional target. Our enthusiasm for simulation and gaming takes instructional design to a whole new level. It requires total synthesis of appropriate theories and strategies. The instructional designer must exercise a high level of professional judgment for determining what elements are critical at each point during instruction. Harnessing experiential learning theory is a critical first step in developing effective games and simulations achieve warfighting excellence.

REFERENCES Bates, A. E., & Poole, G. (2003). Effective teaching with technology in higher education: Foundations for success. San Francisco: Jossey Bass. Beard, C., & Wilson, J. P. (2002). The power of experiential learning: A handbook for trainers and educators. London: Kogan Page Ltd. Bransford, J. D, Brown, A. L., & Cocking, R. R. (1999). How people learn: Brain, mind, experience, and school [Electronic version]. Washington, DC: National Academy Press. Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31, 21–32. Chase & Simon ,(1973). Perception in chess. Cognitive Psychology, 4, 55–81. Chi, M., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7–75). Hillsdale, NJ: Lawrence Erlbaum Clark, R. C., & Mayer, R. E. (2003). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. San Francisco: Jossey-Bass. Clarke, T., Ayres, P., and Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology Research & Development, 53(3), 15–24.


Costa, A. L. (2001). Developing minds: A resource book for teaching thinking (3rd ed.). Alexandria, VA: ASCD. Fox, E. J. (2006). Constructing a pragmatic science of learning and instruction with functional contextualism. Educational Technology Research & Development, 54(1), 5–36. Galdwell, M. (2005). Blink. New York, Little, Brown and Company. Goleman, D. (1995). Emotional intelligence. New York: Bantam. Hannah, S., & Lester, P. (Manuscript submitted for publication). The United States Army’s transformation: The case for a social cognitive approach to leading strategic change. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). Expertise reversal effect. Educational Psychologist, 38, 23–31. Kalyuga, S., Chandler, P, & Sweller, J. (2001). Learner experience and efficiency of instructional guidance. Educational Psychologist, 21, 5–13. Kirschner, P. A., Sweller, J., and Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential and inquirybased teaching. Educational Psychologist, 41(2), 75– 86. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59 (1), 14–19. Mitchell, A., & Savill-Smith, C. (2004). The use of computer and video games for learning. Available from www.LSDA.uk. Moreno, R., & Valdez, A. (2005) Cognitive load and learning effects of having students organize pictures and words in multimedia environments: The role of student interactivity and feedback. Educational Technology Research & Development, 53(3), 35–45. Pensky, J. (2001).Digital game-based learning. New Your: McGraw-Hill Perkins, D. (1986). Thinking frames. Educational Leadership, 43, 4–11. Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology Research & Development, 53(3), 47–58. Sternberg, R. J., Forsythe, G. B, Hedlund, J., Horvath, H. A., Wagner, R. K., Williams, W. M., Snook, S. A., & Grigorenko, E. L. (2000). Practical


intelligence in everyday life. Cambridge, UK: Cambridge University Press. Sweller, P. (1988). Effects of cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.

2006 Paper No. 2974 Page 10 of 10

Van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5–13.