From instructional systems design to managing the life cycle of knowledge in organizations

From Instructional Systems Designto Managing the Life Cycle ofKnowledge in Organizations

Mark Salisbury

Typically, the major phases of instructionalsystems design (ISD) are identified as analy-sis, design, development, implementation,

and evaluation. The design phase uses the informa-tion from the analysis phase to formulate a plan forpresenting instruction to learners. Most approachesto the design phase are rooted in the work of RobertGagne, as described in his book The Conditions ofLearning, first published in 1965. Gagne’s early work(1940 s and 1950 s) was based on assumptions frombehavioral psychology, notably that instruction is thereinforcement of appropriate learner responses tostimulus situations set up by the instructor. Ifstudents have learned, then it is more likely thatthey will exhibit a desired behavior in a givensituation. Gagne’s work was not based on operantconditioning in the behaviorist tradition. In his firstedition of The Conditions of Learning, he incorpo-rated cognitive information-processing views oflearning. For Gagne, behavior was assumed to bevery complex and controlled primarily by a person’sinternal mental processes rather than externalstimuli and reinforcements. Therefore, Gagne sawinstruction as organizing and providing sets ofinformation and activities that guide, support, andaugment students’ internal mental processes. Learning has occurred whenstudents incorporate new information that enables them to master newknowledge and skills. Gagne further developed cognitive views of learningand instruction in later editions of The Conditions of Learning (1970,

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& 2008 International Society for Performance Improvement

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/piq.20007

This article describes a frameworkfor managing the life cycle of knowl-edge in organizations. The frameworkemerges from years of work with thelaboratories and facilities that areunder the direction of the U.S. Depart-ment of Energy (DOE). The articlebegins by describing the instructionalsystems design (ISD) process and howit is used to identify instructionalcontent. This is followed by discussionof how an organizational interventiondelivered by instructional systems de-sign typically is a piecemeal approachto managing the knowledge of theorganization. Next, a framework formanaging the life cycle of knowledgein organizations is introduced, wherethe theoretical foundation for theframework, the Collaborative Cogni-tion Model, is described. Afterwards,the other aspects of the framework—including the types of knowledge,types of learners, and reusing andrepurposing organizational knowl-edge—are detailed. Finally, a discus-sion section presents the frameworkand future directions for enhancingand extending the framework.

1977, 1985). This view of learning as a change in internal mental processesthat results in improved performance is a cornerstone to modernapplications of ISD to solve organizational performance problems.

Using an ISD approach to solve a performance problem in anorganization begins by noting the difference in the current state ofperformance and desired state of performance (Dick, Carey, & Carey,2005). For example, in Figure 1, an organization determines that thecurrent state of quality plans is far below the desired state that describescompleteness and correctness criteria. In other words, the organizationhas found out that their quality plans are not very useful for doing whatthey are supposed to do: ensure high quality of production. There is a biggap between how ‘‘good’’ the quality plans are currently and how ‘‘good’’they need to be to help guide meaningful testing of products before theyare delivered to customers.

As Figure 1 shows, a quality plan is a knowledge product. That is, itembodies conclusions, judgments, and decisions about what goes into aparticular quality plan for a specific product. Also, every quality plan has aset of criteria, or performance objectives, that need to be met by itsdevelopers for its successful completion. These performance objectivesare sometimes implicit, or in the eye of the beholder. Recognizing theexistence of these performance objectives without being able to easilyarticulate them is found in such phrases as ‘‘I know a good quality planwhen I see one’’ or ‘‘shouldn’t a quality plan have ay?’’ Theseperformance objectives spell out what needs to be done and how well itshould be done for a good quality plan. Using ISD, one way to go aboutidentifying performance objectives for a quality plan is to conduct acontent analysis. It always starts off with the same question: ‘‘Whatknowledge does a person need to know to create a quality plan?’’ (Davis,Alexander, & Yelon, 1974). It focuses on identifying the cognitive skillsneeded to create the quality plan. Cognitive skills underlie learning how to

FIGURE 1.

Identifying

Instructional

Content.

132 DOI: 10.1002/piq Performance Improvement Quarterly

learn, that is, getting at the heart of the problem (Gagne, Briggs, & Wager,1992). Once the knowledge is identified, it is listed by topic and each topicis rewritten as a performance objective. For example, the topicCompleteness and Correctness Criteria is rewritten as the performanceobjective ‘‘In the quality plan, the developer will list all approved criteriafor judging the plan as complete and correct.’’ (For a complete descriptionof the steps for conducting a content analysis, see Rothwell and Kazanas,2004.) Figure 1 shows three performance objectives that have beenidentified for creating a quality plan. They make a precise statement ofwhat a learner should ‘‘do’’ in order to accomplish the stated performance(Mager, 1997). They contain a performance component, a criterioncomponent, and a condition component. The performance componentdescribes how proficiency will be demonstrated. The criterion componentdescribes how well the proficiency must be performed. The conditioncomponent describes what conditions must exist when the proficiency isdemonstrated. Finally, the figure shows that in an ISD approachinstruction is developed for learners to achieve the identified performanceobjectives. As is described later, instruction is one of many knowledgeassets that can be used by learners to achieve performance objectives.

Figure 1 also shows that an organizational intervention delivered byISD typically is a piecemeal approach to managing the knowledge of anorganization. Said another way, we discover a problem in an organization,locate the work that needs to be improved, determine the knowledgeneeded to do the work, and design instruction to teach that knowledge.Though ISD is good for solving the latest crisis when it is discovered, ittypically doesn’t prevent the next crisis. In the quality plan example ofFigure 1, once workers are trained in making better quality plans, then theorganization will have the benefit of better quality plans. However, makingbetter quality plans does not prevent another performance gap fromrearing its ugly head in another part of the organization. For example,later on, the organization may find that it has a performance gap in itstesting reports. Only after the ISD process of analysis is invoked again willit be discovered that this new gap is similar to the one discovered earlier inthe knowledge of workers completing the quality plan. At that time,performance objectives similar to the ones previously written for qualityplans will be written for workers completing testing reports. Once thissimilarity is noted, then a determination of how much instruction, if any,can be shared between workers completing quality plans and testingreports can be made.

This example shows that although ISD is quite effective for solvingacute organizational problems with instructional applications, it is notvery effective for identifying the systemic relationships between organiza-tional performance problems. It is this lack of a systems view (Senge,1990) that keeps instructional designers on a never-ending treadmill ofresponding to one performance crisis after another. They are able to keepthe enterprise afloat but don’t have the time, the energy, and mostimportant the big-picture perspective to make the necessary systemic

Volume 20, Number 3–4 / 2008 DOI: 10.1002/piq 133

improvements for improving organizational performance. What is neededis a ‘‘life cycle of knowledge’’ viewpoint from which to analyze, design, andimplement systemic improvements for organizational performanceproblems.

The life cycle of knowledge is the ongoing cycle of creating,preserving, and disseminating knowledge in organizations. Not just anyknowledge, however, but the knowledge that defines the core competencyof an organization—what the organization does best. To successfullymanage the life cycle of knowledge in an organization requires recognitionthat organizational knowledge is complex. Lack of this recognition is whymany traditional approaches to managing the life cycle of knowledge inorganizations have failed. These failed approaches have assumed that thisknowledge is relatively simple (only factual) and have used techniques tomanage it as such (for example, document management). What is neededis a comprehensive framework that addresses the complexity of managingknowledge in an organizational setting that can be implemented in apractical manner.

This article describes a framework for managing the life cycle ofknowledge in organizations that was initially used to successfully build aknowledge dissemination system for the laboratories and facilities that areunder the direction of the U.S. Department of Energy or DOE (Salisbury &Plass, 2001). The follow-up work to this effort was the development of acollaboration application that fed the dissemination system for the DOElaboratories and facilities. The resulting system managed the life cycle(creation, preservation, dissemination, and application) of knowledge forthe DOE (Salisbury, 2003). Recent work has focused on extending thetheoretical foundation of the framework to improve collaboration, and onmethods to identify performance objectives of knowledge work forreusing and repurposing that work. In the next section, the life cycle ofknowledge in an organization is discussed. Then the theoreticalfoundation for the framework, the Collaborative Cognition Model, isdescribed. Afterwards, the other aspects of the framework, including thetypes of knowledge, types of learners, and reusing and repurposingorganizational knowledge, are detailed. Finally, a discussion sectionpresents the framework of and future directions for enhancing andextending the framework.

The Life Cycle of Knowledge in an Organization

The first phase of the ongoing life cycle of knowledge in successfulorganizations is creation of new knowledge, as Figure 2 illustrates. Thistakes place when members of the organization solve a new uniqueproblem, or when they solve smaller parts of a larger problem such as theones generated by an ongoing project. The next phase is preservation ofthis newly created knowledge, which includes recording the description ofthe problem as well as its new solution. The dissemination and application


phase involves sharing this new knowledgewith the other members of the organization. Italso includes sharing the solutions with thestakeholders affected by the problems thatwere solved. Disseminated knowledge thenbecomes an input for solving new problemsin the next knowledge creation phase. Anorganization’s ability to solve problems in-creases with the use of this disseminatedknowledge. In this way, each knowledge lifecycle phase constitutes input for the next phase, creating an ongoingcycle. Because this cycle continues to build on itself, it becomes aknowledge spiral in the organization, as described by Nonaka andTakeuchi (1995).

The growth and sharing of knowledge is recognized as one of the mostimportant elements in becoming a learning organization (Easterby-Smith,1997; Marsick & Watkins, 1994; Senge, 1990), but what has been missing,according to many researchers and practitioners in the field, isdevelopment of a theoretical foundation for describing how people learnand perform in an organization (Raybould, 1995; Salisbury, 2000). Thistheoretical foundation is needed by today’s organizations to avoiddeveloping technological solutions that do not support their entire lifecycle of knowledge (Plass & Salisbury, 2002). To address this situation, theCollaborative Cognition Model, a theoretical foundation, was developed.It describes how learning can take place with one individual, be preserved,and be transferred to other individuals in an organizational setting(Salisbury & Plass, 2001; Salisbury, 2003).

Theoretical Foundation

To represent the complexity of organizational knowledge, a revision ofBloom’s Taxonomy (Bloom, 1956) developed by Anderson et al. (1998)was used as the basis for extending the description of knowledge usedwithin the Collaborative Cognition Model. One of the major differences inthe revised taxonomy by Anderson and colleagues is identification ofknowledge as a separate dimension that describes it as factual, conceptual,procedural, and metacognitive. Another major difference is that theyrecast Bloom’s other categories into a "process dimension," whichdescribes the learner’s cognitive processes in processing knowledge ofthat category. These process dimension categories were also renamedfrom Bloom’s original "knowledge, comprehension, application, analysis,synthesis, and evaluation" to "remember, understand, apply, analyze,evaluate, and create." Note that Anderson and colleagues place "create" asthe highest level of cognition; it describes the individual’s puttingelements together to form a novel coherent whole or to make an originalproduct.

FIGURE 2.

The Life Cycle of

Knowledge in an

Organization.


Anderson and colleagues (1998) describe factual knowledge asterminology, specific details, and elements. Conceptual knowledge relatesto theories, models, principles, and generalizations. Procedural knowledgeincludes skills, algorithms, techniques, and other methods that are specificto a product or process. Metacognitive knowledge was added by Andersonand colleagues to Bloom’s Taxonomy. It is "knowledge about knowledge"and involves general strategies for learning, thinking, and problem solving.Metacognitive knowledge also includes knowledge concerning theappropriate contexts and conditions for the use of the strategiesthemselves. Additionally, it includes the ‘‘heuristics’’ or rules of thumbthat experts use to solve problems.

At the individual level, the Collaborative Cognition Model haselements of Situated Cognition, as described by Brown, Collins andDuguid (1989). The Collaborative Cognition Model supports learning inthe context of the work at the moment—creating an ‘‘authentic context’’for learning. Knowledge workers can access knowledge (and other people)to learn how to construct solutions for pressing organizational problemsin a just-in-time manner. Furthermore, the Collaborative CognitionModel supports Situation Cognition for learners with differing cognitiveneeds by providing different types of knowledge as defined by Andersonand his colleagues (1998) in their revision to Bloom’s Taxonomy (factual,conceptual, procedural, and metacognitive). As a result, the CollaborativeCognition Model supports work and learning to live in the same space,occur at the same time, and become interdependent. As a result, learningis situated in the authentic task of organizational work and takes placeduring that work.

At the team level, the Cognitive Collaboration Model is an extensionof the theory of distributed cognition (see Salomon, 1996, for an overviewof distributed cognition). One of the best-documented examples ofdistribution cognition in a work environment is by Edwin Hutchins in hisbook Cognition in the Wild (1996). Hutchins studied how a crewcollaborated to operate a large ship at sea. According to his description ofthe theory of distributed cognition, cognition is distributed acrossindividuals. That is, no one individual has complete knowledge as tohow to accomplish a complex task such as operating a large ship.Hutchins also describes cognition as distributed across the artifacts of anorganization’s work. On the ship, this means the instruments supplycritical decision-making information to the crew members. According tothe theory of distributed cognition, cognition is in the history of thoseartifacts. On the ship, the previous version of an instrument gives acontext for the present version of that instrument. In an officeenvironment, artifacts are the knowledge products of the organization.These are the ‘‘intermediate products’’ of a larger process, such things asdesign documents and quality plans. Another set of artifacts is theknowledge assets that document the organization’s processes, instruction,work examples, and expert advice used as resources by the members ofthe organization to make the knowledge products. In the Collaborative


Cognition Model, the theory of distributed cognition is extended toinvolve types of knowledge as defined by Anderson and his colleagues(1998); these types are present in the distribution of cognition acrossindividuals, their artifacts, and the history of their artifacts.

At the organizational level, the Collaborative Cognition Model is anextension of Nonaka and Takeuchi’s concept of creating a knowledge spiralin an organization (1995). In Nonaka and Takeuchi’s knowledge creationprocess, transferring knowledge from one organizational member to anotherbegins by the first member converting tacit knowledge (intuitions,unarticulated mental models, and embodied technical skills) into explicitknowledge (a meaningful set of information articulated in clear language,including numbers or diagrams). This explicit knowledge can then be passedon to another member of the organization, who must convert it into tacitknowledge (internalization) before he or she may use it. Again, theCollaborative Cognition Model extends this description of knowledgecreation by identifying the categories of knowledge as defined by Andersonand colleagues (1998)—factual, conceptual, procedural, and metacognitive—that are involved in the knowledge creation and transfer process.

Differentiating Types of Knowledge

Figure 3 shows the four types of knowledge taken from the revision ofBloom’s Taxonomy (1956) developed by Anderson and his colleagues(1998). The figure also shows that documents furnish access to factualknowledge. Other media forms can be used to capture factual knowledge,but documents are probably the most well-known and used medium forcapturing and disseminating factual knowledge (terminology, specificdetails, and elements). The color coding in Figure 3 shows that for mostorganizations it would be desirable to have most of the factual knowledgereside in an explicit form. That is, most organizations would not wantmost of their factual knowledge floating around in the heads of itsmembers.

Figure 3 also shows that instruction allows access to conceptualknowledge. As with the factual knowledge, other resources can permitaccess to conceptual knowledge, but instruction is the best medium forcapturing and disseminating this kind of knowledge (general principlesand concepts). As for desired visibility of knowledge, the same idea is truefor conceptual knowledge as for factual knowledge. Although access toconceptual knowledge may be established informally, as with individualon–the-job instruction, most organizations would want to make most oftheir conceptual knowledge explicit. This is what is done when newcourses are developed. The conceptual knowledge residing in the heads ofthe members of the organization is made explicit in the form of coursematerials. However, as Figure 3 shows, not all conceptual knowledge canbe made explicit; this means that some informal instruction inorganizations will always exist.


Also shown in Figure 3,examples permit access to proce-dural knowledge. Examples de-scribe the step-by-step processfor applying conceptual and fac-tual knowledge to create a uniquesolution for a specific problem.Other means can afford access toprocedural knowledge, but exam-

ples are the best medium for this kind of access. The figure shows thisdesired visibility for procedural knowledge in an organization. Mostorganizations will want to make many of their examples of good workexplicit so they can foster access to procedural knowledge for themembers of their organization. Some of these best examples may becomebest practices for the organization. Note that it will not be possible towrite up each example and make the knowledge that went into thatexample explicit. Consequently, a large amount of procedural knowledgeremains tacit in an organization.

Figure 3 also shows that expert advice provides access to metacognitiveknowledge, knowledge about knowledge. Again, even though other meanscan be used for access to metacognitive knowledge, expert advice is theoldest, most direct, and accepted means. The figure also shows thatorganizations will want to make some of the gems of expert advice explicit forall the members of the organization. However, because it is not possible giventoday’s understanding of cognition to make all metacognitive knowledge inan organization explicit, most of it remains tacit in the organization.

Even though Figure 3 shows that large amounts of knowledge remain inthe tacit domain of an organization, organizations can still manage thatknowledge, and it can be managed through direct connection between twoor more people. For example, a member of an organization can sharespecific details, on-the-job instruction, step-by-step description of previouswork, or some expert advice to other members of the organization. In allthese cases, the knowledge begins tacitly in the first person. Next, itbecomes explicit through the first person’s elaboration. This explicit form isinternalized by a second person and resides as tacit knowledge in thatperson. Although no artifacts remain of the explicit form of the knowledge(no documents, no video, and so on) the tacit-explicit-tacit cycle isexecuted. The managing piece comes in by facilitating processes wherethose who need to know something can be connected to those who know it(French & Bazalgette, 1996). It also creates another link between individualand organizational learning (Kim, 1993).

Differentiating Types of Learners

Figure 4 shows that when Anderson and colleagues revised Bloom’staxonomy, they made knowledge a separate dimension with four

FIGURE 3.

Differentiating

Types of

Knowledge.


categories: factual, conceptual, procedural, and metacognitive (Andersonet al., 1998). They recast Bloom’s other categories into a ‘‘processdimension,’’ which describes the learner’s cognitive processes whensolving a problem in that category.

Figure 4 also shows that novices are usually working at the level oftrying to understand and remember. This is why it takes novices so longto get anything done; they are really stuck at the level of just trying to getwhat’s going on and put it into memory. The figure also shows thatpractitioners are usually working at the level of analyzing the situation andapplying knowledge to form a solution. They already understand what todo and remember how to do it. Give them a problem similar to one theyhave solved before and they will quickly analyze the problem and take aprevious solution, adapt it, and apply it to their new problem. Finally,Figure 4 shows that experts should be working at the level of evaluatingsolutions and creating new and unique ones. The word ‘‘should’’ is put inthis explanation because if an organization is using its experts likepractitioners, doing the everyday work, then the organization is notgetting the most from its experts. If the organization’s experts arespending all their time on the work of the day, then the opportunity is lostfor better ways to do tomorrow’s work.

Figure 4 illustrates how to give learners appropriate knowledge assets.Of course, an appropriate knowledge asset depends on the type ofknowledge they seek. Novices use the system to become practitioners,practitioners use the system to become experts, and experts use thesystem to create new knowledge. In the process of becoming practitioners,novices seek to understand and remember conceptual knowledge.Instructional materials are appropriate knowledge assets for them becausethey offer access to conceptual knowledge. Note that novices still requirefactual knowledge to fully understand and remember the conceptualknowledge (similar to a student requiring access to the manual tounderstand the instruction presented in the classroom). In the process ofbecoming experts, practitioners use examples to analyze and applyprocedural knowledge. Note that practitioners still require factual andconceptual knowledge to apply and analyze procedural knowledge.

FIGURE 4.

Differentiating

Learners and the

Knowledge They

Seek.


Experts create and evaluate expert advice. By doing so, they foster accessto metacognitve knowledge for others in the organization.

Reusing and Repurposing Knowledge Assets

Considerable attention has gone into developing methodologies forreusing knowledge work in recent years. Much of it has focused on themethodologies for developing ‘‘learning objects’’ or ‘‘content objects’’(Barritt & Alderman, 2004; Hamel & Ryan-Jones, 2002; Rehak, 2003;Robson, 2002). However, even though quite a bit has been published onsharing knowledge, especially in the area of communities of practice(Brown & Duguid, 2001; Lave & Wenger, 1991), little has focused on themechanics of how to identify and track knowledge for reuse (Osterlund &Carlile, 2005; Wiley, 2004). The result has been that for mostorganizations, reuse is addressed only at the institutional level, if at all(Davenport, 2004).

Figure 5 describes how performance objectives can be employed forreusing knowledge work. It shows two performance objectives that wereoriginally developed for different tasks (writing quality plans and testingreports) and described differently but were later found to be fundamen-tally the same. This created the opportunity for reusing a knowledge asset.Because both performance objectives could now have the same identicaltext, this text can be a single document that is referenced by bothperformance objectives. Now, when users click on Performance Objective2 & 4, they are taken to the same text—regardless of whether they areaddressing the performance objective for a quality plan or for a testingreport. (The ‘‘&’’ operator means that the two performance objectiveshave been combined into one objective.) This way, whenever thedocument for this combined performance objective is changed, it ischanged for users no matter which knowledge product they are workingon (quality plan or testing report).

FIGURE 5.

Reusing

Knowledge

Assets.


Repurposing Knowledge Assets

Figure 6 shows four performance objectives that are almost the same.Performance Objective 3 and Performance Objective 7 are both labeled‘‘creating completeness and correctness criteria’’ for a quality plan. Theyare written very similarly, but there are some subtle differences.Performance Objective 3 is specifically written for the workers at site A;Performance Objective 7 is written specifically for workers at site B. In asimilar situation, Performance Objective 6, ‘‘applying completeness andcorrectness criteria’’ for a testing report, is written for workers at site C. Ithas some subtle differences from Performance Objective 8, ‘‘applyingcompleteness and correctness criteria’’ for a testing report, which iswritten for workers at site D.

As in Figure 5, Figure 6 shows that because Performance Objectives 3,6, 7, and 8 are very similar, knowledge workers will apply the same generalprinciples and techniques to satisfy them. That means the instructionmodule for all four performance objectives can be the same. This situationforms the basis for repurposing a knowledge asset. The instruction is ashared knowledge asset. However, not all knowledge assets are sharedbetween the four performance objectives. Each performance objective hasits own unique set of knowledge assets that describes the context (place inthe process, physical site) in which the performance objective is addressed.

Figure 5, as does Figure 6, shows that one of the documents has textthat describes the common elements of the performance objective. Thistext is the same regardless of whether a worker who is writing a qualityplan from site A or site B, or a worker who is writing a testing report fromsite C or site D, accesses the Performance Objective 3161718. (The ‘‘1’’operator means that the four performance objectives share commonknowledge assets, but each has additional knowledge assets that are notshared with the others.) Note that all workers who access thisperformance objective access the same instructional module as well.However, depending on what part of the process they are coming from

FIGURE 6.

Repurposing

Knowledge

Assets.


(quality plan or testing report) or what site they are coming from (site A,B, C, or D), workers see a different contextual document. For example, aworker from site A trying to write a quality plan would see ‘‘QualDocument—Site A,’’ while a worker from site B trying to write a qualityplan would see ‘‘Qual Document—Site B.’’ On the other hand, a workerfrom site C trying to write a testing report would see ‘‘Test Document—Site C’’ (and to be complete, a worker from site D trying to write a testingreport would see ‘‘Test Document—Site D’’).

Discussion

This article describes a framework for managing the life cycle ofknowledge in organizations. The theoretical foundation for the frame-work, the Collaborative Cognition Model, details how learning can besupported at the individual, team, and organizational levels. At theindividual level, the model supports learning in the context of the work atthe moment—creating an ‘‘authentic context’’ for learning. At the teamlevel, the Collaborative Cognition Model supports learning in the contextof a ‘‘distributed environment,’’ where cognition is distributed acrossindividuals, their artifacts, and the history of their artifacts. At theorganizational level, the Collaborative Cognition Model supports creatinga knowledge spiral in an organization where transferring knowledge fromone organizational member to another begins by the first memberconverting tacit knowledge into explicit knowledge, before passing it on toanother member of the organization—who must convert it into tacitknowledge before he or she may use it. The Collaborative CognitionModel also supports different types of learning at the individual, team, andorganizational levels. It supports novices, practitioners, and experts intheir need for varying types of knowledge: factual, conceptual, procedural,and metacognitive.

Whereas the Collaborative Cognition Model presents a theoreticalfoundation for a framework for managing the life cycle of knowledge inorganizations, reusing and repurposing knowledge work is part of thislarger framework and constitutes a means for supporting learning in acollective and ongoing manner. Much has been written about the learningthat goes on in organizations; however, little attention has been placed onmodeling that learning—at least not with the same vigor that has beenused with modeling and optimizing work processes. This article has putforth a means to model the learning processes of an organization. Thepremise is that for organizations to reach their potential they mustintegrate learning into their work. Said another way, effective organiza-tions must be able to work and learn together—concurrently. Thelearning processes must be modeled and combined with the workprocesses. That is, the work processes and learning processes must live inthe same space, occur at the same time, and be interdependent.


The view of learning presented in this article is an entirely differentview of learning from the one based on the ‘‘learning occurs after training’’approach. In that approach, training is done for tomorrow’s production.When training is complete, workers will be able to apply that training asthe opportunity presents itself. As a result of this view, training is typicallylooked on as a noncritical input to production. It can be delayed, oreliminated, because there is enough time to develop a workaround for themissed training, before it can affect tomorrow’s production.

In a contrary view presented in this article, learning is part of the workprocess in the ‘‘learning during work’’ approach, and it has to occur duringtoday’s work process to get today’s work done. It is essential to today’sproduction, and without it the work does not get doneright and on time. In this view, eliminating learning ordelaying it only reduces an organization’s ability to gettoday’s work done. Consequently, learning is looked onas a critical part of the work process.

Finally, for organizations to manage the life cycle ofknowledge and systemically improve individual, team,and organizational performance, they need to uncoverthe drivers of work, the performance objectives thatmust be met by the work. These performance objectivesare the key for improving the workflow process andoverall productivity. They determine what to measurefor providing feedback on how well they are working,along with how to go about making improvements inthe way the work is done. They also determine whatknowledge to reuse and repurpose, and why it should bereused and repurposed.

Future Directions

Further work is needed to use performance objectives for evaluatingthe performance of workers. From this perspective, performance shouldbe evaluated in terms of the knowledge that individuals bring to bear onthe problems of the organization. The contribution of individuals to theorganization’s stockpile of factual, conceptual, procedural, and metacog-nitive knowledge can be used as an information source for individualperformance assessments. Obviously, a count could be conducted toquantify contributions to procedure manuals, online instructionalmodules, documented work examples, and recorded expert advice.However, these contributions can take place informally: sharing a fact,offering on-the-job instruction, sharing an example, giving a nugget ofexpert advice. Further research is needed into development of newmethods for using performance objectives to measure and track thesecontributions.

This article has put fortha means to model thelearning processes of anorganization. Thepremise is that fororganizations to reachtheir potential theymust integrate learninginto their work. Saidanother way, effectiveorganizations must beable to work and learntogether—concurrently.


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MARK SALISBURY

Mark Salisbury, Ph.D., is an associate professor in the OrganizationalLearning and Instructional Technology Program at the University of NewMexico, where he teaches graduate courses and conducts research in thearea of knowledge management. Mailing address: College of Education,Hokona Hall, 380 MSC05–3040, University of New Mexico, Albuquerque,NM 87131–1231. E-mail: [email protected]


From instructional systems design to managing the life cycle of knowledge in organizations

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