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[DRAFT: Do Not Cite or Duplicate Without Author's Permission]

Appreciative instructional Design (AiD):

A New Model

by

Karen E. Norum, Ph.D.
Assistant Professor of Adult Ed & HRD
Adult, Counselor, & Technology Education
College of Education
University of Idaho, Boise
800 Park Blvd., Suite 200
Boise, ID 83712-7742

Paper presented at the 2000

Association for Educational Communication and Technology (AECT)

National Conference

Denver, Colorado

October 25-28, 2000

In recent years, there has been a call for new instructional design models; models that meet the speed of change most organizations must now perform at (Carr, 1997; Gordon & Zemke, 2000; Gustafson & Branch, 1997). Gordon and Zemke (2000) suggest that the instructional systematic design (ISD) models that dominate the field have outlived their usefulness. Although instructional design models based on constructivist learning theory are emerging (see Reigeluth, 1996; Savery & Duffy, 1996; Willis, 1995, 2000; Wilson, 1996; Winn, 1992), the ISD models are still dominated by the behaviorist paradigm (Gordon & Zemke, 2000; Gustafson & Branch, 1997; Seels & Glasgow, 1998; Willis, 2000). Thus, the idea that "behavior can be observed, measured, planned for, and evaluated in reasonably valid and reliable ways" (Gustafson & Branch, 1997, p. 73) is reflected in current models of instructional systematic design. The word "systematic" in instructional design suggests that instructional design is a step-by-step, orderly, sequential, logical, linear process (Banathy, 1996; Gordon & Zemke, 2000). This is reflective of the "machine mentality," which creates the illusion that certain conditions will lead to certain outcomes, thus by following a "lock-step, engineering like" (Gordon & Zemke, 2000, p. 48) model, the instructional designer can create the right conditions for the desired outcomes.

This paradigm has lead to "fiendishly complex" (Gordon & Zemke, 2000, p. 43) models that emphasize efficiency in human learning, instruction, and performance (Carr, 1997). It also can lead to low expectations: Morrison, Ross, and Kemp (2001, p. 3 describe the role of instructional design as planning, developing, evaluating, and managing the instructional design process effectively "so that it will ensure competent performance by learners." Today's organizations find themselves in quickly changing environments and need to develop the capacity to change and adapt quickly. They need to be constantly learning, able to re-create themselves at will and with skill (Fitzgerald, 1995; Senge, 1990; Wheatley, 1999). This does not call for "competent" employees, it calls for quantum (Zohar, 1997) employees: people who are "mindful" (Daft & Lengel, 1998; Langer, 1997) and can think holistically, evoking and co-creating reality(s). This calls for an instructional design model that is systemic rather than systematic in nature.

From Deficit-Based to Value-Based

Current models of instructional design are deficit-based: the gap between current performance and desired performance is systematically analyzed and as appropriate, instruction is designed to fill that gap. It is a problem-solving process: What is the problem and how do we solve it? often begins the analysis (e.g., Seels & Glasgow, 1998). The existence of a problem is often heralded by a gap in learning, evidenced by poor job performance or unacceptable error rates. During the analysis phase, a needs assessment that focuses on this gap is often conducted. By focussing on this gap, the unstated message becomes that there is an acceptable level of error. For example, it is OK for the airlines to lose luggage, they just need to lose less of it.

This deficit-based approach leads to unintended consequences. By focussing on the gap, we tend to focus on fragments: there is a danger we will analyze each puzzle piece instead of considering the place of piece within the puzzle (Cooperrider & Whitney, 1999b; Capra, 1982). The systematic approach to instructional design can set us up to design and develop excellent instruction and training that is not suited for the organization or context in which it needs to live (Tessmer, 1990). A deficit-based approach is slow and past-oriented: it has us looking at yesterday's causes. The assumption is that if we correctly identify the problem, we can then select the solution that corresponds to it (Banathy, 1996). But because of the interdependent nature of systems, we may never find "the" cause of the problem (Capra, 1996; Senge, 1990; Wheatley, 1999) and there is a good chance we will not actually solve the problem (Cooperrider & Whitney, 1999b). Instead, what will most likely happen is that a new problem that demands more attention will come along and the "problem" we were working on will fade into the background. As our attention jumps from problem to problem never actually solving any of them, a negativity is engendered. We become progressively enfeebled, resign ourselves to live with diminished expectations, and become visionless (Cooperrider & Whitney, 1999b). We come to believe that rather than design instruction or training for the "best of" level of performance, we need to design it for an "acceptable" or "competent" level of performance. We design the training or instruction to eliminate what we do not want rather than to give us more of what we do want.

The Appreciative instructional Design model offers a new alternative. Appreciative instructional Design (AiD) takes its theoretical foundation from Appreciative Inquiry (AI). AI was developed by David Cooperrider, professor of organizational behavior at Case Western Reserve University. As opposed to problem solving, AI begins with a search for the best of "what is" rather than looking for exactly what is wrong or what needs to be "fixed." According to Cooperrider, every system has good and bad in it. We are trained to look for the "bad" and "fix" it. But what if we paid just as much attention to the "good" in the system? AI gives us a structure for searching out the "goodness" in the system, allowing us to appreciate "what is" and use that as inspiration for what "could be." It is a valued-based rather than deficit-based approach (Norum, 2000a).

Applying AI to Instructional Design

When AI is applied to the instructional design process, the goal is to discover the factors present when the system is operating at its "best of" level. Instruction or training is then designed around those generative factors. Thus, the gap in performance or learning becomes immaterial. There is no need to analyze the gap between current and desired levels of performance: what we want to know is what is the "best of" level of performance? AiD assumes there is something working in the current level of performance (after all, it cannnot all be poor) and that what is "working" can be found and amplified (Bushe, 2000a). The instruction or training then will be designed to nurture, develop, and amplify the competencies needed to perform at the "best of" level and give the organization "more" of what it wants.

To contrast the ISD model with the AiD model, imagine you are charged with designing customer service training: the organization has been receiving what it considers to be too many complaints. Using a traditional ISD model, the gap between the current (unacceptable) level of performance and the acceptable level of performance is analyzed. One measure for this might be the number of complaints received: how many are received now and how many is an "acceptable" number? The training would then be designed to lower the number of complaints received into the "acceptable" range. Using the AiD model, instead of determining the gap between the current and acceptable levels of performance, a search to discover what the organization defines as the "best of" level of customer service performance is engaged in. When customer service is operating at its "best of" level, what does it look like? Training would then be designed to nurture, develop, and amplify the competencies needed to perform at that "best of" level. Thus, it is quite possible that the training developed will take the employees well beyond the "acceptable" level of performance! By focussing on the potential to create the best of what "could be," this model goes beyond filling gaps in performance.

The AiD model is systemic, advocating "a global conception of the problem and an understanding of the interrelationships and interconnections" (Carr, 1996, p. 17). Another distinctive characteristic of AiD is its focus on "inquiry." While conducting a needs assessment is important in traditional ISD models, it is critical in AiD. From Appreciative Inquiry, we learn that the questions we ask determine what we find and the data we gather determines what we design (Cooperrider & Whitney, 2000). Thus, in the AiD model, a fair amount of time is devoted to constructing questions: the inquiry is at the heart of the process. The AiD model is future-oriented, looking at generating "more of" what the organization wants rather than minimizing what it does not want. This is reflected in the difference in one of the first questions asked: rather than beginning the process by asking, "What is the problem and how do we solve it?" AiD begins with a question designed to evoke stories of what the "best of" level of performance looks like.

An overview of the AiD Model follows.

The Appreciative instructional Design Model: An Overview

The AiD model is based on the "4-D" cycle used in Appreciative Inquiry: Discovery, Dream, Design, Destiny. Cooperrider and Whitney describe Appreciative Inquiry as "the cooperative search for the best in people, their organizations, and the world around them (1999a, p. 10). This involves a search into what gives "life" to the system. A hallmark of AI is the kinds of questions asked during the inquiry: questions that are unconditionally positive, designed to strengthen the positive potential in the organization. The inquiry is based on the assumption that there are untapped, rich, inspiring stories about the organization (Cooperrider & Whitney, 1999a) and that what is "working" can be amplified and fanned throughout the system (Bushe, 2000b). A four-phase model is employed to conduct this appreciative inquiry. Cooperrider and Whitney (1999a) refer to it as the "4-D Cycle":

The AI cycle begins by crafting positive questions that are designed to uncover the "life-giving" forces of the system. The inquiry begins through an interviewing process. During this process, the task is to discover the best of what already is—to appreciate the good things about the system. The "best of what is" is used to inspire "what might be": the Dream phase. Possible (positive) futures are envisioned. The next step is to create the policies, procedures, infrastructures, governance systems, etc. that are needed to support "what should be." This is the Design phase. The new system is co-constructed. As the new system is implemented, the question turns to how to sustain and maintain this new system. The focus of the Destiny phase is how to continue to learn, improvise and adjust so that the system can continuously strengthen its affirmative capacity. This often leads back to the first phase in the cycle: Discovery. A new inquiry begins into the "best of" what has just been re-created.

This same process is reflected in AiD. As in an Appreciative Inquiry, the process begins with Discovery: questions are crafted to discover the "best of" performance level in the organization. Stakeholders (those who will be the audience for the training and/or those who need to support it) are interviewed to elicit stories about what the "best of" performance level looks like as well as their "best" instructional or training experiences. Questions are also asked about the "ideal" system and "ideal" instruction or training. Drawing inspiration from what is working, people are encouraged to dream about what could be. They are dared to expand the realm of the possible. The information gathered in the interviews is relevant to the Discovery and Dream phases of AiD. The goal is to discover what is already working well in the system's performance and to understand why it is working well. What life-giving factors are present at this "best of" level of performance and what does the system want "more of"?

As the Discovery and Dream data is analyzed to find themes, patterns, and refrains (Lawrence-Lightfoot & Davis, 1997), the Design Phase is entered. Competencies that need to be nurtured and developed, what needs to be learned, how to learn it, effective design elements, what needs to be amplified are all considered. Clues to these questions are contained in the stories gathered during the Discovery and Dream phases. Those clues will be used to create instruction or training designed to give the system "more" of what it wants. The goal is to create instruction or training that will nurture, develop, and amplify competencies that will in turn amplify the life-giving factors identified in the Discovery and Dream phases. The design takes place around generative factors.

The Destiny Phase is entered when the instruction or training is implemented. This phase reflects the plan to sustain, maintain, improve, or adjust the instruction or training. It also reflects how the system will know if it is getting "more" of what it wanted. The plan outlines what will be assessed and how. The appraisal of what is working about the instruction or training brings us back to the beginning of the cycle: Discovery.

AiD is a specific application of Appreciative Inquiry. It shares the same theoretical foundations and the "4-D" cycle. Where it differs is in its focus. While Appreciative Inquiry is a system-wide intervention with a focus on organizational change, Appreciative instructional Design is specifically concerned with designing instruction or training. While it is quite possible that the instruction or training designed could be part of a system-wide change effort, that is not necessarily the focus for AiD.

Because the theoretical foundations of AiD make it distinct from current models of instructional design, they are described next.

Theoretical Foundations of AiD

AiD is compatible with the constructivist paradigm, insisting that learners go beyond acquiring knowledge and create it. It advocates user-design: engaging stakeholders in the design of their own systems (Carr, 1997). In the AiD model, the instructional designer is a facilitator, working with the system to help it get more of what it wants. The AiD model also draws from action research methodology particularly in the data analysis process, which takes place in the Design phase. It recognizes that learning and performance take place in a context and that context can "facilitate or inhibit human enterprises" (Tessmer & Richey, 1997, p. 88). AiD understands that organizations and the people in them are living, dynamic systems, embracing the "new science" paradigm (Wheatley, 1999; Zohar, 1997) and ecological thinking (Capra, 1996). It evokes "idealized" design: design that is future-focused, based on what we want, yet grounded in current reality (Banathy, 1996).

Five principles are central to Appreciative Inquiry and thus are foundational to AiD. These five principles (Cooperrider & Whitney, 2000) are the:

•Constructionist Principle

•Principle of Simultaneity

•Poetic Principle

•Anticipatory Principle

•Positive Principle

Several propositions are related to these Principles. What follows is a description of each Principle and its related propositions.

Constructionist Principle

This Principle asserts that organizations are living, human constructions. They are constructed based on what we think we know, thus what we know and how we know it becomes fateful (Cooperrider & Whitney, 2000). "[T]he truth about an organization is what those involved agree the truth is" (Zemke, 1999, p. 30). This principle is strengthened by the proposition that stakeholders in the organization carry in their minds some sort of shared idea of what the organization is, how it should function, and what it might become (Cooperrider, 2000). The Constructionist Principle calls us to unearth and examine the mental models (Senge, 1990) that we hold about an organization and consider how those mental models have effected the fate of the current system.

Principle of Simultaneity

Change begins the minute we ask a question. The questions posed set the stage for what is found. What is found becomes the data we use to re-construct the future. "Even the most innocent question evokes change" (Cooperrider & Whitney, 2000, p. 18). Thus, change is not something that happens after an analysis is conducted; change begins with the analysis. A corresponding proposition encourages us to create the conditions for organization-wide appreciation to "ensure the conscious evolution of a valued and positive future" (Cooperrider, 2000, p. 52).

The Poetic Principle

If organizations are constructed, they can be re-constructed. Just as a poem can be interpreted and re-interpreted as we bring new meaning to every reading of it, so can organizations be re-interpreted as the system they are embedded in changes. This is "The Poetic Principle": as the stories of the people in and attached to the organization change, the organization changes. "There is no such thing as an inevitable organization" (Cooperrider, 2000, p. 47). This principle teaches us that we can choose what to study in an organization: the good or the bad, the joy or the alienation, the creativity or mediocrity (Cooperrider & Whitney, 2000). A related proposition tells us that no matter what the previous history, every system can be altered and re-invented (Cooperrider, 2000).

The Anticipatory Principle

From this Principle we learn that the image of the future guides the current behavior and actions of the system (Cooperrider & Whitney, 2000). Positive images of the future lead to positive actions; negative images lead to negative actions. This image becomes the "referential core" of the system and determines its essential characteristics (Capra, 1996; Wheatley, 1999). It is possible for this image to be incoherent or unclear or even for it to be pathetic. Many organizations are better at articulating what they do not want than at being clear about what it is they do want. An image that is based on what we do not want is likely to engender negative behavior and actions. Malaise, mediocrity, angst, and dysfunction are likely to be present in such an organization. This principle is supported by the proposition that systems are limited only by their imaginations (Cooperrider, 2000). Paradoxically, even the best future images can hold the system back if those positive images become so cherished, they cannot be given up for even better images (Cooperrider, 2000). This proposition reminds us of the Constructionist Principle: our organizations are constructions.