The ffamcwork most o h pssd to think about the training criterion ..... Skills are learned and they reflect the success- ..... story implanented by the critic. Howevar ...
Individual and Team Training J O H N P. CAMP BELL'^^^ N A T H A N R. K U N C E L
The objectives of this chapter are to identify and discus critical issues patainiae D trainiw design, trainee charadexistics, the awesanent of training effects, and the identification of critical training needs, with particular re lev an^^ to t k dynamics of the twenty-first #ntury global economy. All training issues are hmcd by the parameters of the -cat interaction. Withiu this hmework there are at least three major hkagcs in the path &om training to organhtional effectivInes and they should not be mislabeled. Al-w approaches to training design exist but it is argued that they must all consider certain 'd v d s ' that cannot be avoided or finessed Also, &ee individual differences ahays play a critical role in determining training outcomes and research in this domain is expanpalihdarly with regard to the motivational dekmhants of mastery and transfer. Finally, the literature exhibits considerable consensus in the labeling of critical training needs for the new century. This array of identified needs is discussed in terms of cumnt RBtD that is being brought to bear on them.
McCloy. Oppler and Sager (1993) argued that it is For the purposes of this chapter, training is defined useful to think of direct and inas a planned intervention that is designed to and that t h m are three.kindsof d k c t enhance the detennimnts of individual job perforknowledge, skill, and volitional mance, when the individual functions inConsequently, performance on a prdcuhr factor dently or as a member of a team. Individual job cmbc improved d y by byjjobralevant performance is conceptualized as in recent research knowledge, job relevant skill, or developing mare on performance modeling (e-g., Borman & advantageous choick behavior (e.g., dooaing to Motowidlo, 1993; Campbell, Gasser & Oswald, come to work more oAen, choosiug to expad more 1996; Murphy, 1989); that is, performance is effort, or choosing to expend effort for a m m ausdefined as behavim or actions that are judged rela tained period of time). vant for the organization's goals and that can be Individual differences in each of the direct ddascaled in terms of the level of the individual's conmhumts are in turn a fuaction of multiple iodirsct tribution they repsent. For virtually any position, d ~ t sone, of which k tcahing and insmcjob, or occupation, performance is multi-tion.Othcrindirectdetermiaantsaresuch~m s i d and it is reasonable to think in tenns of the abilities (i.e., relatively stable traits) or inmajor perfomce factors as describing the critical ti0110of a 'motivationals nature (e.g.. new nward dimensions along which an individual job holder systems, threats, etc.). There could Plso,be interocshould be trying to excel. Given a sample of job holders, individual ~BcT-tive effects. Training then is an attempt to enhmoe performance relevant knowledge, &ill and/or , ences in performance on each of the factm are a choice behavior via instruction. . function of multiple determinants. Campbell,
tho-
Individual ond
A similar explication applies to tcam training. Obviously there are diffaent types of teams varying fiom the very traditional work group to the very autonomous, highly cross-trainad, self-managed, high performance worlr team (GuPn & Shea, 1992). Thcre is nothing necessarily mysterious about teamworlr or team self management It is simply that the determinants of team performance that are responsive to a training intervention may also involve interactive effects among the knowledges, skills, and choice behaviors of its members, as well as individual levels of knowledge, skill, and choice. The question of how to enhance both the individual effects and the interactive effects via training is the tcam training issue. Defined in the above way, this chapter will be limited to learning-based interventions that are, in some Sense, formally designed to enhance particular performance determinants. Consequently, other important learning-based experiences such as the organizational socialition process or infonnal training will not be discussed,even though many of the principles of training design apply to theae experiences as well. Also, the general domain of management development has been discussed in another chapter and will not be explicitly addressed here.
OBJECTIVES The objectives of this chapter are to (1) review and discuss the major issues pertaining to: the design of training for individuals and teams, the inshuctional conditions that influence training effects, the interactive effccts of learner characteristics, and the problem of tmsfcr of training;(2) summarize the research literature on the evaluation of training effects; and (3) summarize the literature on the identification of specific critical training needs. That is, what are the most important substantive training needs that have broad relevance for the world of w o k Thc information reviewed comes h m theory, research, and practice and we will by to identify implications for all three as we go along. Also, we will try to be explicit about those aspects of theory, research, or practice that are universal in nature and those that may incorporate some degree of cross national specificity.
TWO MAJOR ISSUES Two major issues underlie any attempt to design or implement training, evaluate its effects, develop training theory, or conduct R&D type research on training issues. One conccm8 the universality of the
aptitude - treatment intemction and the,other is the ubiquitous criterion problem.
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The Aptitude Treatment Interaction (ATI)
:
Cronbach (1957) and Cmnbacb and Snow (1977) made the A n a virtual icon tbr research on training and instruction. The principal message b that all trainees am not alike and some of the characteristics where people differ are correlated with training achievement. That is, even though everyone is given the same baining experience (i.e., the 'treatment'), some peopk will do better than othm as a function of having mom or less of the 'aptitude' (e.g.. cognitive ability, need for achievement, anxiety, self efficacy, level of previously acquired knowledge and skill, level of interest in the material, etc.). Fwthu, the magnitude of the camlation may differ acrosa sample thot have had different treatma (i.e., imrtructional p m grams). The AT1 of most interest is dre cane whae X (the aptitude) and Y (the aitaion variable) are measured in the same way in each group and the regression l i cross. The major implication is that to maximize aggregate gain across all groups, the same training program should not be given to everyone. Ideally, trainets should be 'assigned' to prognuns based on their X soon (which also wuld be a weighted or unweighted composite of x, + x, +.. x&. This problem is formally equivalent to personnel placement and classification (when jobs or job levels are treatments) and to the assignment problem in linear programmhg (e.g., when diffkent locations or routes am treatments). The most e x m e cast is to provide a different training exporience for each individual, which would be optimal, but expensive. However, Cronbach and Gleea (I 965) showed that, if the comlations across treatments are different, then the gain in agpgatc achievement grows exponentially with the numba of treatments. It is a potentially powerful phenomenon. Some are of the opinion that the significant advances in instructional technology in just tbe laat few yews and prospects for even more etading development in the next 5 to 10 years will realize the power of the exponent sooner rather than later (e.g.. Jensen. 1998). It could very well be in the form of multi-media intelligent tutoring systeaPrr which wuld customize the t d h g experience down to 'virtually' the optimal level, and at a reasonable Nee. Cronbach's presidential addrcna to the America Psychological Association in 1956 pointing to the poteatial power of the AT1 (Chmbach, 1957) filled the instructional world with hope, and many attempts were made to tind wfhl A m , primarily in education (Cronbach & Snow, 1977; Snow.
1989; Snow & Lohman, 1984). Two Afls have beem found with some consistency. Thesmo9t thpat bas been the interaction of g e n d cognitive ability and the 'struchxe' of the instructionalprogram when more structure means more insbucbr guidance, more detailed objectives for the learner, more explicit specifications for the content to be taught, and mom h p e n t feedback (Snow, 1989). High ability people do better with low structlae and vice versa for low ability individuals, although the variance accounted for by the interaction has not baa large. The seumd ATI is represented by the intion of trainee anxiety or s e l f ' and the degree of program structure. High aaxicty and low self-efficacy individuals tend to do better in more -programs. In general howcver, a relatively narrow m e of ~TXshasbeen investigated, and j&marily in &tion settinns. The individual diff-ces variable is almost always general cognitive ability or one of a few personnlty variables, and treatment differences have been largely limited to the 'structure' parama ter. Consequently, the research yield has been limited in tams of its value to organizational training. As will be discussed in a later section, the picture has begun to chauge a bit with regard to individual differences in trainee motivation. However, as we noted some time ago (Campbell, 1988), the potentially powerful interaction of initial individual differences in maha kuowledp aad skill with diffkmms in instructional content is still given scant attention in organizational training. It tends to be one size fits all with little regard for diffaences in the initial state of the learner's knowledge, skill, or characteristic choice behavior. A reasonable hope is that more versatile computerized tutoring systems will change this situation. We believe it is attuismto assatthattkcarealways relevant individual differeaces in trainees and that thae arc always potential dBin training programs. Cansequendy,the AT1 represents e uoivaael set of questions that apply to any trainiag effm l b t is, what individual differences cornlate with train& achievement? What treatment differa~xsare the most relevant? What interactions might be likely? What should the dependent variable be?
The Criterion (Dependent Variable) Problem Training is implemented for certain pwpofxs, howeva appropriate or inappropriate they might be or firom whatcvc~theoretical p u p d i v e hey came. This begs the question of the extent to which the program did, or did not, accomplish its objectives;
and if not, why not Consequently, tbe need for the ~ssessmcatof training effects is a major issue, regardless of culture, national economic system, industry sector, or one's p r e f ' imtmctioaal
theory. Following the d&&th of h h h g being used above, the objectivesof l r a h g are to incram job howledge, increase jab relewnt skill, or enhance individual choice bdmk. The uitaim ~oancanswhethaornotthcintcn&d~ wae~~~~mplishad,aadtowhatextentThisiatrue regardless of whether the desired chang8~arc defined by the oqmidon, the individual or via a mllabaration betweenthe two. However, Dipboye (1997) and Was, Camrab Bowers, Rhodenizer and Bowers (1999) have *inted out that oqphli0118 (and individaal8) can have otha pupam for trainhg an wdl. For example, mining can serve a mrmba of symbolic firnotim. Organizations can use it to show variaur audience8 that they are concaned about suppoatine skill improvement among their cmployoca, or qpmpriately managhg a dinrse work fixcc, a preventing sexual harassment, or improving suparvision end management.Individuals can use &&hg ~ ~ , ~ t i O n a l ~ o r e v a r g r a d u r t t degrasassymbolsofaccomp~and&stur Both- organizations and individuals may use miniug certification as a way to avoid or aid litiption,u in establishing 'exput' credentials, or for mating tho tcaIMofrmoutofc€tultsettlcmentbycon~ oertain types of training (e.g., fot rahcing sexual harassment). While none of those purposes am necessarily inepproPriate and may serve individuals or oqanbdom well, they me not our collc~llhem, except as they might intdke with dcstnb ing to improve p e r f h c c nlcvmt imowlcdgc, skill, or choice behavior. The ffamcwork most o h pssd to think about the training criterion problem is still the Kkkpmkk (1959, 1996) class~cationof possible meamma into four types: (1) mach'o)~~, or h a scIf-qmls of training effectiveness; (2) Iccuning, or indqmk t end-of-training meesures of knowledge, skill or attitudinal change;(3) M m i o r , or mmeasur of p e r f i back m the job setting (ie., the trensSa issue); and (4) resultr, or a m m m t of mentainorganizational~tbathvedmct implications for the ' b u s h p r p o ~ 'rmd tbe ~ 0 1 1 ' sviability. U d , ,there four 'types' have neva been all that w d ~ d (W, -T B-=tt, TS U 1997). For example, d o n a may refar to global judgmmts of how much the individual liked the
ods,ort6judgmen~abouttbeerrpertwoft~~ aqubitim oritmayalaorcfcrtomasteryof~b
Learning may refer only to .howl*
skillsorchangesmmeasuredatti~.It~abonot clear whetha behavior r c h to asmmmt of tho same variables specified by the laming &to& excepttbttheyareapsessbdmthcjob~or w h d k it ref= to the awesmat of job perkmance itself, not its detumhmts.
Individual and Tcom lMning
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As noted by m y obsuvua (e.g, AUiga ct I, md detailed inA third question ir 1997; Salas & calmon-Bowers, 2001; Saler &.al., whethatheperfolmanccofindividuatrorteemr 1999). there! has not been much research directsd constitutes a causal determinant of differeDoer in at modeling .training achievement itself. Catsinly ~tional&~Althwghanewould it has not kept pace with recent rarearch and hopsthiswouldimtbethecaw,itiapotddy theory on the nature of job performance (Barman & possible to design jobs for individuah or,tcauu that have nothing to do with any relevant oganiPMotowidlo; 1993; Campbell, 1999; Campbell, McCloy, Opplu & Sager, 1993; Hedge & Borman, t i d goal. 1995); For example, thae is only a modestamount The most leasodte view of thax issues is fint thattheboaomlineorrcsults~must~ of data pataining to the internlationships among mwtructvalidmeanacsof~affbcmultiple measures of training achievement Alliger et I (1997) prescpt mcta-analysis d t s firom 34 tivcness, which m the beginning asePmes some studies. Om clear finding is that global h i x reacbasic agreanent among the stakeholden about the tions are not comlated with learning or mastuy, or n of the e o n ' s goats. ~ n the f p with mu& of anything else for that mettn. Similar m'bcd performance roles for individualn a d ~,howcverbroadornarrowtheyarc,mustbo results h m educational se#ings (Green1997, McKeachie, 1997). call into @on the i n d k h . valid dctumhnts of organizational effectivenu?& inate use of studcnVtduee evaluations for variow *,is amatta for o r p a i d o n , teem, andjob P\aposes. Esthafm of the-cornlatiom among the design. Finally, the objectives of tmining mud other Kirkpatrick criterion typcs have not been well address valid dctemhmts of individual or term established.However, it may be a Mile task and not performance. Once these three qucatioaa are worth the invstmmt, given the mduqmS& ~~wecanuclcfullyaskwhethaaprrof the types themselves and the virtual lack of any ti& tmhhg program mats its objectives; a d if specifications tbr how they should be intcmlatcd. not, why not. Obviously them am a numba of What~tIickref48toas(1isrcally thing8thatcaadisturbthetrainingachicvcmmtto jobperformancelinkegeaswellastbe~ the care conof trahhg. Amcasurc of learning is notan intermediate surrogate for pafin betweea individual or team performance and tba thejobscttingarforindi~ofunitoroganizlr- bottom line. A causal model of thin s u p a m must take such variables into ~ccouutas welt tional effectiveness (in, 'results'). By any of the design models to be diacmd below, &e Wrrct objectives of training programs arc not to impnnn the ovwall job pafomraoce of individuals or to UNIVERSALS IN TRAINING DESIGN irrcrtaseprofitability or return on mvcamcnt for the organizaIion.~eam;m~~oalscanbe~in~ms of changes in bowledge, skill, choice behavior, or Oim that the AT1 and the criterion issue am evenattitudea,butnooneirainingprogramcan alwayswithus,wewouldliitoarguolhttbsra arcalso~univ~mtmbhgdcsigathatare include all the stof individual job perfarmmrce (somc are not amenable to training),and ~ ~ ~ g t o n a t i o n a l o r c u l ~ ~ r r well as to the current controvuaics ova altcrnativo bottom line or results indices have multiple determinants m addition to the performaucc of indivimodeh of training design. Parts of the discussian duals. Then is considerable discussion in the draw h m Campbell (1988). The list simply identifies a number of issues that must be considered rrnd practitioner literature about the need to link train@ aaetofdccisionsthatrnustbemade~alrainadvities to the mlum on investment (ROI) for training costa (eg, Pany, 19%; Willips, 1996; ing program is completed A bdmmtd point u that even if these questions and M o n a are not Purcell, 2000). In every published discld011 of this considered explicitly, or are aggrdvcly (and mbissue to datc, the difficulty of controlling for alterguidcdly) rejected as irrelevant, thy will still be native explanations is largely ignored and obtaining a valid metric for both costs and bmefits is made to a n s w d by defkult, ohuoys; and can be inked aftcrtheEacthmwhatwasectuallydoaa souDdmuchtooe~y. Westillbelieve that G a g ~ C ( 1 % 2 ) w a s v e r y ~ This is not an argument that the totality of indiWhenheaguedthatthemoetfunAammtal~ vidual paformaace or bottom line indicatom of issueisthe~cationofwbatistoboleamad organizational dectivencss are not important. They obviously are. Howcva, whether a program What new knowladges, skills, belie&, attitudes, a ' choice behavim &odd the individual &%it af€a meeta ita objcdives and w h & h the objectives trainingthattheycouldmt,orwouMmt,exhibit reflect valid detumhmos of perfomarm are two bekc. In our tcnninology thin is the specifidon d i f f ~ ~question& llt The linkagebcmm ampctab of the troinSngprogrmn objectives. Every t r a i d q cies l e a d m tmbhg and job paformame itself is h t e m ~ ~ t i will o a have them. If not stated explicitiy, established by a needs analysis, regadem of thcycaubeinfared~whataduaUyhappaaaThir whetha the linkage is easy to idmtify (evcxyona fimdamentalquestionis8a~~aedby~~ agrees it is 'obvious') or rquirca a more thorough
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Handbook of Indu~nial,Work and OrganizationaI Psychology - 1
three prior issues: goal analysis, job design, and needs assessment
Job Design and the Organization'i Goals What are the organization's operating goals, and what are their implications for job design? How should jobs be deiined and structured to best contribute to goal accomplishment? For example, given certain R&D goals, what kind of R8tD operations should there be? What kinds of research positions are needed? Again, if a training program is designed to enhance the determinants of ~erfonnanceon a critical job factor, performan& on this factor should, by design, have something to do with the goals of the organization. The responsibility for making sure such a linkage exists goes far beyond the training function (Harris,1994). Models and procedures for goal analysis and job design are not within the boundaries of this chapter, but such questions &mot really be avoided. In the training context, they will be answered one way or another, again if only by default. When an organization buys a training program off the shelf from a vendor, it has implicitly asserted that the content of the program d a s not create goal conflicts and is consistent with the way jobs are designed to meet the organization's goals. Determining Training Needs By our definition, training needs reflect current or anticipated deficiencies in determinants of perfirmance that can be remedied, at least in part, by a training intervention. Training needs exist in a number of contexts. For example, current job holders may be deficient in terms of their performance, and the primary purpose of training is to remedy the deficiency. In a different context, if eligibility for promotion is a function of high performance on particular performance components, training on the determinants of those performance components may be offered to, or sought by, anyone wanting to be considered for promotion. Also certain components of perf~rmance~may be forecasted to be mitical in the future; as when new kinds of equipment, will be introduced. For example, what will be the training needs for air traffic conirolIers when new, and very different, air traf6c control systems come on line. Finally, training may be n&.for the determinants of performance components that must be executed in totally new environments, as when NASA was faced with training people to drive a car on the moon. Ideally, a needs assessment would have thne major steps: a description of the factors that comprise effective performance, specifications of the determinants of performance on the factors, and
identification of the'performance determinants that would benefit h m a training intervention Stap three could focus on indivi(the M t i o n a l pason analysis) or on teams,or on forecasted fubln training needs for everyone in an occupation, a even for everyone enteriug the labor force (e.g., US D q a b m t of Labor's Secretary's Co&on on the Achievement of Nexssary Skills, 1992). This makes virtually all job analyses and criterion dcvelopment methods relevant for needs identificntioa However, using the available work analysis methods to identify training needs is hampered by several factm'.F k while there has been much -t progress in d&eloping descriptive modela of work performance (Bonnan & Motowidlo, 1993; campbell, McCloy, Oppler & Sager, 1993; Murphy, 1989; Organ, 1997), such efforts are utill relatively primitive for purposes of identraining needs. That is, current models dcscrii a small number of factors that are still very broad in nature. This may be quite valuable for guiresearch on personnel selection, but for training purposes we must move down the hierarchy to mom specific factor descriptions. In this regard, the most progress has been achihred ih descriiing the canponents of leadership/supcrvision(Bass, 1990)management (Borman & Brush, 1993) and now pahap 'pcrfhmmce as a team memba' (Olson,2000). Another constraint is that current methods for iden-g and mapping performance atd(KSAs) rests on the judgments of SMEs who are asked to 'li' KSAs to performance factan. Unfortunately, the KSAs themselves, at least thoee that are potentially trainable, vary h m being 'ade quately' specified (e.g., requires 'statistical analysb skills at the introductory course level') to being significantly underspecified (e.g., requires good 'writing skills') to being woefully underspecitid (e.g., requires high 'adaptability'). Perhaps the worst example of underspecification is to ask an SME to rate the criticality of 'problem solving skill' lu a performance detemhant We assert that at its current level of specification, problem solving skill is a virtually meaningless consiruct for purposes of identiwg training needs. More about this lata. Finally, a third difficulty is the crudenew of our methods for identifying the perfomancc detuminants that are amenable to a training intervention. That is, what kinds of detumbnta are minable and which are not? We may pay too much attention to the obvious suspects, namely specific knowlsdge deficiencies. While most job analysis methods operate at too g e n d a level and require SMEs to make too many judgments about very underspecified variables, which reduces their usefulntss as methods of training needs analysis, there are methods tbat adchu~ training needs more directly. One of these is the critical incident Whniquc (Anderscm & Wileon. 1997) for which relevant observen are asked
Individual and Tcmn lhining
to consider a potential population of specific perfbmmcc examples and d e s c r i b a sample of episodes that reflect v a y effective and very in& tivepertbnnrmce.ne~icanalsobeanlted why the individual was able to &'bit the effective +so& or what antecedents led to the ineffective iacident While the i n f . about causal detmnk nads are aiU subject to the errors of human judgment (Morgsan Bt Campion, 1997), which can never be eliminated completely, the inference is tied to vay specific events. The individual is not nquirpd toinfalinkagesbetweenbroadco~orvariables and broad k t o r s comprisingjob perfimnance. The critical incident technique is sometimes grouped within a broader class of methods genaally referrad to as cognitive task analysis (CTA) (Cbipmaq Schraagen & Shalia, 2000). In one sense the literature on CI'A, which is now relatively large (Schnagen, Chipman & Shalin, 2000), represents an attempt by co~nitivepsychology to define w& aualysb as part of cognitive science since most work is now mental rather than physical. However, most of the CTA malysh techniques are not new and encompass various interviewing f o m t s , observational techniques, and protocol analysis methods. Thcrr are many variations of these basic methods h m the standard job analysis intenkv, to sofhvam models for eliciting protocols, to interrupted recall techniques for pcople watching themselves on video tape. Summaries of methods are provided by AM& (2000), Cooke (1994), and schraagcz~et al. (2000). Turf battles aside, CTA does provide certain typa of information for specifying training needs that arc not addressed by more 'traditional' m& ods. This occlw because the general goal of CI'A is to explain the detamhnts of expea as compared with novice performance. Consequently, while traditional job analysis attempts to descrii the cantent of a work role and infer the KSAs that would be comlatcd with individual diffmncts in pcrfimane in that role, CTA attempts to address lum, individuals an able to paform work tasks at a particular pafonnance level. To be more specific, how doesthecxpatdoitl The hmmork for exploring how the expert docs it is taken h m cognitive science reprcscntatiom of pafomance dckzmhmts. That is, while a Cl'A begins with a 'traditional' description of the worlt content, the procedure then attempts to ncovcr information such as the foUway in which the q e d n o v i c e tmnslates the prcscrii work goals into their own opera-
wsoala
T k mental models mpc&mvicea use to repmat their performance rcqukcments and work wntaxt.
‘The cognitive nsoiais expertdnovices use to ~lishthe.tasksthey'deanimpartant.
283
Co@ve I C B O m an Usually repesaltcd 88 dm =levant knowledge and skilld c b m b n b , a d the co@w strategies and strakgy selection don thrt enusedtoapplytbem In sum, what individual goals, mental mode4 cognithe I C B O m and cognitive stratcgiw do expertsusethatdistinguishthemfiroma0~7As outlined by Chipman et al. (2000), there en thfw principle applications of CTA: Bpecifyiogthe determinants of c q m l performance for purpom of training and sclc(:tion; aiding the design of humanf system int.emdons; and analyzing the basea of effective teamwork The number of bona fide applications of CTA to the specification of &ahin$ needs is actually quite small. Much more effott hat^ been directad at using CTA for software development to aid decision making,canstluctcontrol syrtems, or construct the teaching protocol itsc1E One of the most direct applicatiom of CTA methods was the identification of tmining aaQ for air traffic controllers reported by Meam at d. (1988). S e v d different analysis methodo w ~ a used to elicit the goals, mental models, knowldge and skill representations, and cognitive stratcgicd used by a sample of expuienced controllc~~ whsn dealing with simulated control tower ATC job samples at a major airpoR The participant experts were 6rst asked to t h i d out loud about how they would dead with a set of air traffic situations as m y c d by the paper copy flight strips. Thsy were rhea asked to watch a video of a controller working on a simulation of an air traffic scenario. The siwrlatioas were nm on the ATC trainhg equipment which was identical to the actual equipment d displays used in tbe tower. Part way through the simulation, the video was stopped and the participantwasaskedtotakeovaandtov~wbat he was going to do, and why. Then each participmt was asked to work through three additional 45-minutesimulations using the same equipmcof Finally, each participant completed a series of paet &nulation memory tab such as rscalling d drawing the m c patterns, recalling the flight ships, and recalling significant evcntn that hap penedduringthescenarios. The analysis pmduced considerable protocol information canccming the mental modds, co@tive ft80unxsSand cognitive strategies that experieeced controllas seemed to nse. For example, one f k q m t mental map of the air M c sector organized planes by the type of problem they rqnwated rathor than topographically. E x p e n d conmllq had an Rmadnphr detailed knowledge of what l q p a ~in the sector each day, rmd they cngagcdbacon~~of~ori~potontialproblems extendingova a fairty long time f b e . It was also the case that experienced cantmllm bad developed some task elements to automaticity. That is, they could &one task automatically (dala eotry using keyhad) while simultaneouslyworking on a
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Handbook of Ind1~~trial. Work curd
controlled processing task (talking to pilots). These and other findings led directly to training pnscrip tions to accelerate the development of A X expatise. The finding that high level of performance is o h a function of developing critical tasksto a state of automaticity such that they no longa require attentional resources leads to the prescription that the analysis of high level perfbmance in tenns of its consistent versus controlled pmesshg wmponds should be an important part of analyzing training needs (Rogers, Mauer, Sales & Fisk, 1997). In general, CTA methods build on the more traditional job analysis techniques and do provide additional information about what explains higher level versus novice performance. Unfortunately, it can quickly become time conmuning and expensive and is not feasible for all training design problems. However, even a modest amount spent trying to contrast the resources and methods used by novices and experts could provide useful information regarding traiaing needs, keeping in mind at least one major qualification. There are degrees of hedom problems in CTA and the designer must avoid being misled by the idiosyncratic expert.
Specifying Training Objectives Once tmining needs are identified, they muat be translated into training objectives that lead directly to specifications for training content Needs assessments as d c s c r i i above are not statements of objective. Objectives identify what the learner should know or be able to do after finishing the program that he or she could not do before. They m stated in observable terms, and they include some indication of the conditions under which the individual should be able to exhibit them and at what level of proficiency. This is the. heart of training design. If the traiuhg objectives cannot be spccifie4 it implies that the trainer cannot be clear about what to teach. These statements are often attacked as shopworn rhetoric that is no longer usem (Gordon & Zemke, 2000). Nothug wuld be further from the truth. It does not matter whether one's orientation is behavior analysis, cognitive science, constructivism,. humanism, or consultant of the month dictum. Any training program that is actually implemented will focus, if only by default, on achieving some kind of change that can be nthr'bited to same level under some set of conditions or constraints. In practice, describing the training objectives explicitly is not an easy task,which may be one reason people tend to avoid it. An important meta-issue hem is that each training objective must incorporate the appropriate 'capability'. For example, a tmining course for data analysts.could formulate objectives having to do with correctly formatting data files and using the proper commands to nm standard data analysis sofiware, or it could specify objectives for teaching
analysts how to solve novel analysis problems. The former has to do with the comet a d o n of certain well-specified steps, while the latter r e q h problem solving of a specific technical salt. That is, the capabilities to be learned m differeat While this is a rather obvious example, the situations whm it is not so obvious wuld lead to a serious misststa ment of the training objectives (Gagnt, Brim & Wager, 1988). Table 13.1 is a suggested frtunewark for highlighting differences among the major types of capbilities that could be the objective of a tmhhg intervention. Thinking in taxonomic tams at tbi8 stage is intended to help avoid inwqmating the wrong capability in the description of a lminhg objective. There is nothing particularly startbg about this taxonomy. All are legitimate training objectivesand no hierarchy of importawx is implied The distinction between a knowledge capability lloa a skill capability is the conventional one that distinguishes knowing what to do h m being able to actually do it. Sometimes the differmKx betwsen knowing and doing is small (e-g., computing desriptive statistics) and sometimca it is very large (e.g., resolving conflicts b e t w c c l l s u ~hit, ting a golf ball). However, the classic blnctional design mistake is teaching one but hoping for the other. The word skill is used in almost many ditrkrent ways as the term performance. We have tried to give it a more specific and more useful meaning here. Skills are learned and they reflect the successful application of relevant h&lcdge capabilitiesto solve vroblems or vroduce outcomes that c a i ~be well&ified such- that the comet solution or desired outcome is known, and the methods and procedures used to generate auccasful solutions Cn outcomes are known as well. For examj)le,there m correct ways to solve linear 'progmmhg problems, erect the h m e for a house, and slalom ski. AU of tbese are well-structured problems to which, known pracedraes can be applied. This does not mean that mastering the metbods and pmcdura (i.e., skills) is easy or that knowledgeable people no longer argue about precisely the best methods to ure (e.g., striking a golf ball). The problem solving cavability ref' to the application of knowlsdge &d &i to ildeveloping solutions for ill-structured mblema (New01 & Simon, 1972). That is, there M no a p a i d comctor best solution; the knowledges and akib that am the most applicable are not completely specifiable; and the problem or goal itself may not be easily identified.All this makes the term problem solvings#1Ia bit of an oxymoroa As argued below, the train@ implicatiom are Merent for a skill VUN IS problem solving capabilay. Also, by this d e t i o n , effective problem solving is the successful application of a c q u i d knowledge and skill to an ill-structulsd problem when ' v f u l ' is necarParily a judgment
.
I n d W and Te&
385
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Table 13.1 A taxonomy of capabilities that ampolentipny trainable A.
hmeaxs in kxmldga 1. ~ ~ 0 f t b e ~ ~ f 8 C b ~ ~ t o o b j t E q m 0 ~ p r o ~ a r a , ~ s t C
2. ~ c d g c o f f s c t r ~ ~ ' ~ ~ k t i ~ 3. . KnowMgc ofrules a pucuha for acan+lishiq rpecific .goah or objective8 ~thtiveio c o g n w i rodJ .
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call by lmowledgcablc pwple. Inthis sense, effco ., tive problem solving is domain specific. But what else t it? What leads to proficiency m being able to . identify problems, specify them in a useful way, identify knowledges and skills tha! am relevant to the p b l a n as d&d, and apply them? Hcurislia~ :that cum be taught? The ubiquitous 'mcta-cognitions'? Or straightforward pattnm matching, which maka effective problem solving depeadent om the similsrity of tbc clPrent situation to problem siturtiom a c o u n d in tbo past? These issug m revisited later m this chapta. Attitudes and beliefi arc included m,tbe cqmbik iiy taxoJmmy bumae tbey are the explicit aa implicit capabilities of intmst in many mining prolpams directed at such tbp as individual wlfeffictcy(Oia, 1 9 9 7 ) , a t r i a d c s t o w e r d d ~ merit, a beliefb'concuning the pmcdud justice of pmcmd polidea. Two things m importoat.to lmrw~~mpabilitieaFilat,tb behmarchangatmattitudesarbeliefi,ardtBs
tbe mhing content itself could inchde knowledge, ski&& or problem solving. For example, mea m g hold sexist beliefs becaw (a) thay do.not know their cunmtbeliefs are sexist, @) thy hve no nonsexist inskills rcbthe to intcmctian withWomm,or(~)thcy~nco~raddal withneworrmiquehatasanentoolderdl. . fn sum, the description of the training objectiva isthefun~talstepin~duJign,d~ ably the most neglected. Fkoducii objdvea with tbe above specit3catiopb raquinr muob cognitive ~radit~difticuhtodevdopwrypowaPtl rcmmxmat~contingcacicsfi3fmaint.ininenrh behavior on the part of traiuhg ddgnm. It kboo much Wreoatingindexcrcisingpropaty. Wewhat we should do, but t h m alwayr.vay pa^ eusaive~lewbopromisemuch~~~roltiorw
e~
Spedfying Training Content
The tminiq content u dctdsd by the t d a i q desind(hopedtin?)~uinbehaviorbacom- objc?ctiw. It ia colnpod of the Imowl*, rleillr, pkx aae ( A j m '1985) and Eer h m guamntecd. md'pttam of choice bchaviar tht the tmke Second, whii the capability specifi@ ar implied by nnutacq&bDbeabktomec~theobjeotivsr.By t h e ~ o b j e c t i v e m a y b e a n a t t i t u a e o r ~ .design, being able to exhihit the objecliva b
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Handbook of Industrial, Work and Organizcrtional Psychology - 1
dependent on mastery of the training content For example, what content must a graduate student master to write a research proposal,make a presentation to a relevant audience, or write down a research question? The distinction between training objectives and training content is important, but sometimes subtle. Sometimes they are very similar (using a keyboard for word processing) and sometimes they are not (e.g., learning more about your own personality to facilitate acquisition of team member skills). Besides identifying content elements, the sequence in which they an to be learned must also be specified. If the sequence is not clear, either the objectives or the subject matter is not well enough understood (GagnC, Briggs & Wager, 1988). Content specifications can be demmined from three principle sources. One is simply to use expert judgment. People who 'bow' the topic specify the training content. Content can also come h m more formal theory. For example, in basic skills there are now rather welldeveloped conceptual descriptions of what arithmetic is (Rcsnick & Ford, 1981). Closer to home, certain theories of leadership provide a specification of what knowledges and skills must be mastered to enhance leadership performance (Bass, 1990). However, one reason that leadership theory has not progressed firher than it has is that a comprehensive substantive model of leadership performance has never really been provided. Training and development will become more powerful as om taxonomies and substantive understanding of performance phenomena grow. We simply need much more research on what constitutes competent performance in various important domains (Glaser & Bassok, 1989). A major contribution of research in cognitive and instructional psychology to the methodology of content specification is again tbe application of a wide variety of CTA techniques to elicit the howledges, skills, and strategies that high level performers use in contrast to novices. Such methods can focus either on the expert protocol to elicit the information, skill, or problem solving capabilities that should be enhanced or on the novice protocol to discern the faults and mistakes ('bugs') that should be corrected or avoided (Schaafstal& S c h u e n . 2000). One CTA technique for representing the training content that seems overem~hasizedis the elicitation of expert vs. nonexpert sehantic networks. That is, participants are asked to consider an array of concepts and procedures, and judge the relative similarity between each possible pair. Once the similarity matrix is obtained, various multidimensional scaling or network analysis techniques (e.g., Pathfinder) can be applied to obtain a dimensional or relational latent structure for the concepts (Goldsmith & Kraiger, 1997; Kraiger, Salas & Cannon-Bowers, 1995; Olson & Biolsi, 1991). A retuning finding is that the similarity matrix is not the same for e+
as it is for novices and a different dimensional or cluster structure is produced. Granted that training and experience can produce changes in such a Mlarity mairk, it is not clear how this information is usdul for training design. The concepts to be clustered usually have very little specification beyond the name or title and the respondents are not d. to generate explanations for their similarity judg- . ments. Relative to the taxonomy of capabilitiw given in Table 13.1, this procedure focuses only on 'labels', which may not capture the capability specified by the training objectives. Specifying Instructional Method8 and Training Media Given that a particular body of content is to be learned, the next consideration is the set of instnuctional methods that should be used to teach or promote mastery of the content. We use the term imhrctionaI methodr to mean the generic teaching methods or learning events that a trainer potentially has available. For example, the direct presentation of information to the student is a generic instruo tional method, as is simulation. A generic learning method might be executed through any one of several specific techniques, or media. For example, information presentation is possible via reading, lee turcs, or the internet. This is a 'generic' vs. 'brand name' distinction and we would argue that the number of generic methods is relatively few. Table 13.2 is a suggested taxonomy of such methods. There are two critical features of instructional methods that training design should attempt to optimize (Gap6 & Briggs, 1979; Glascr, 1976; F5~1trick, Cross. Kozma & McKeachie. 1986). First, the 'capability' incorporated in the training objective and the training content should be repramted with as much fidelity as possible by the training method. For example, if the capability is electronic trouble shooting, and trouble-shooting is specified as an application of known procedures to identify one or more knowable malfunctions (i.e., trouble-shooting skill), then the training method should provide an opportunity for the learner to generate the app@ate application of acquired knowledge to the specified trouble-shooting problems. HOWCVCT,if o course in sodesign is intended to d e c t a problem solving capability, the teachiag method should provide a series of novel and incompletely specified problems as stimulus mataial. In addition, the design must ask whether this training objectiveis also a fimction of knowledge and skills that nnut be mastered before the problem solving capability itself can be addressed. Designing training mchda to incorporate the desired capabilities w ill be mcecssful to the extent that the functional descriptions of the naxssary capabilities are (1) valid and (2) sub . stantive enough to guide the training design.
Table 13.2 lhxonomy of gawk inrbxcla'o~lmethod &vatu for occupatio~ltraining . 1. ~ - r i o n ~ r s m ~ . F m m ~ d i t i o n a l l ~ t o d i s-~ t -a r r a ~ ~ ~ h b e m a r t w i d c h r ~ w d ~ f r &~~~timofvabalmeii~alfi~auditoryinfamrtiontolfie~cwer.Howsva,-tboryh~ ~ti011#mryinchrdeguidgmdsugeeaionrfaborrto~hinfosrrmtia?r,~~inwhlehtho infarmti~is~~iawtlmowp 2 Modehg. One nry cqrceializcd form of infarmnth
irpriog8moddobdemqolr~c'~edarirsd 1.4*d~~od.YLqrrd*S~,a.lm.Mod.~*rrwhba.** 'karmnitutbe~m~fatioDttchnique~bmost~dirrcted.tarkin~..'2be~rrnyb thc~,ative~,~~~~aperfvlpevc~rmbo((~~IhepIba'~uhollMibP~ 3. Informotion A.aenWim Phu Awisionfir Laorna Rupom. Many h t m c t i d techniqm .ttarr*to meage~lcarnor*from*lyprovidiasthe '
tbe amt&d md derlimg with querticar o f ~ ~ m ~ n l c v a n t f m t b e ~ o b ~ ~ t . ~ p . e ~ t o b t r o , r P c h u ~ ~ n u t b p r o b l e r n r , ~ t o i n r r t r u c t a ~ m i n M ~ l m g u a e c ~ ~ r ~ d ~ t a r y r t c m ~ ~ b o w t o ~ ~ a r r y r t e m r , p u f o n n i o g r p s d B c ~ d ~ ~ ~ . ~ T b c ~ n e l c m e ~ t r m t h . t m ~ ~ i r ~ o f t h c b . n # . n d t h s
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rrrpansasm'~'hr~~lltartwhicheomauclo~upornWctothejob~hWtbe~~ Simon. 1996). n to be wed (Aodaroq 5. s m b n ~ nSidation . metbods arc r direct rttcmp to rcpnrent dl tbe miel-a dth rchllljd~tub with~muchfidelity~pib1efor~stimahgcorditionr.Iherrrponseqtbsdtioarlmdawhichchd o b j e a i v m u t ~ b ~ W . l d ~ c ~ c ~ l t ~ ~ f t b e ~ ~ t h t b s t b e . ~' b , t b a s ~ b c ~ ~ h t b e k n d w i d t h d 6 d c l i t y f o r t b e ~ u l u r o o l d i t i a o l , ~ ~ ~ ~ ~ ~ 1 ~ ~ mp-dbyv==6. . & h c & ~ . ~ ~ t m b n g e r r i m ~ i t ~ t k ~ d c o ~ t e x t . ~ , ~ ' d o s r w t ~ '
th.ttbs~~~tcntoftbe~~laedirectfyrel~~(he~Obj~.~beau#~mffbodb a e c u t e d o o ~ p b ~ n o t ~ I v c t h e t r a i n i n g d e s ~ o f ~ ~ o b ~ ~ l h r t r r r ~ ~ ~ ~ .d .m d ~ ~ t t b e m ~ t o f O J T ~ t b m L ~ a w r m p y a D l l j r ~ b C t b C ~ . tO.ChQ.
hother principle of method bsign is that, whatewr tbe capability to be mastered, the learner must. be dowe&enmnkged, or induced to actively 'pm. &ace' tbat d i l i t y during training. Produdon facilitates bo& l e . snd retention (Pesry & DOWLU,1985; Schmidt & Bjork, 1992). This is an true far a knowledge capability 8s it is for a physical or cognitive skill. For example, if a training objective C O L ~ ~ S Dhowladge S of the new tax law, the trahing method must induce the train- t o p
Whilebzandnamesceavayin~ofthevalid ity which they reflect that capability ot provide tbo m t ; v to produce the relevant p tbo . bulkofthcpotentialutilityofa~~hr already k Y a l carJ-,b~ FecadinO rtspr. thb regard, it is worth notiug that buying tho ktsrt bnad~techniques~uhcrenlally~of training .design actually star& Historically,thisbelsothepintwbsremPchof~ tllcrweerchanleamingmd.skill~~hr chreeorconstrudtbatknowledgeinoneormore bear c0~:cnlIatCd.~ - i a the , &objectiw., training content and naraic instructional methob ways. So called '--based' instructional melhods o r c t a L a s g i V e n ~ - t h e ~ ~ ~ (Ivancic & Hesketh, 199511996)attempt to increase c o g n i t i v c ~ m b y r c q u k i a g t h e ~ t o on tbe optimality of the h i n g c o d r t o ~ b analyze and coacct the source of their own urors. p o r a b e d i n a s p s c i f i c ~ o a a l m e t b o d . ~ o f. h intemthg qIac&ul is wheth~such ~ l ~ f - b a s c d them learning d t k m have d w d r 'mw . imtmction would be differentially effective dcpadlook'mtbelast10-2Oyson. ingoponthetypeofcapabilay. Atthiapoinfifthcaboveissueshaveban ddmwd, we ban anived at some basic m c a E F F E m INSTRUCTIONAL ti011(fbr~objectivcs,trainingcwtent.ami C0M)mONs garaic~onalmetbods.Itis*wrwthat the proa a d cans of ahnative brad namca tech~ o r ~ o f m ~ ~ irstsldiba i o n , ni(e.g., p e c X c information prcacatation mscwch questtom haw revoivod ~ ~ s i m ~ c t c . ) b e c o m e r e l c v a u tad . trat~~fer, ,
,
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Handbook of lnd~'lrio1,Work and Organizational Psychology - 1
around the effectiveness with which instructional methods (a) provide appropriate goals for the learner (trainer-generated, self-generated, or cooperatively determined), @) provide appropriate guidance for goal accomplishment, (c) provide the opportunity for appropriate kinds of practice, (d) provide the appropriate types of feedback, and (e) keep the learner motivated and interested. These are in fact the classic 'learning principles' (Blum & Naylor, 1968). The three parameters that have received the broadest attention are goals, practice, and feedback. Issues pertaining to learner interest and motivati? go far beyond the instructional method itself and will be discussed later in this chapter as an important domain of individual differences critical for learning. Goals
As discussed above, the training objectives constitute goals for the training designer; and these objtives are conceptually and substantively distinct h m the goals provided to the learner during the period of instruction. The last 30 years have generated a large research literature on goals as a powerful determinant of individual performance (Locke, 2000, Locke & Latham, 1990). While we do not want to recapitulate all of that, it does deal with a number of issues that are particularly relevant for training. Goals can influence performance via one or more of three general mechanisms (Kader, 1990). First, goals can inform people as to where they should direct their efforts. Second, goals can provide the standards or criteria upon which outcomes or rewards are contingent. Third,goals can set the occasion for using different strategies for goal attainment, as when the individual concludes that simply increasing effort will not by itself lead to goal attainment. All three of these mechanisms assume that the individual has made a commitment to the goal. Within the instructional process itself, goals can be dificult or easy, general or specific, and distal (i.e., the f m l learning objectives) or proximal (i.e., subgoals for different parts of the instructional sequence). Other things being equal, specific ddifcult goals, if they are accepted, lead to higher perfoxmance. However, in the instructional setting othq things may not be equal. Difficult goals may not be accepted because the costs outweigh the benefits of goal attainment and m l e differ in how they evaikte the positive and'negative outcomes of goal attainment (Kanfer, 1990). Also, providing proximal goals too early in instruction or too fb quently may direct too much attention away from the mastery process itself ( b d e r , 1990; Kanfer, Ackerman, Murtha, Dugdale & Nelson, 1994). However, despite their attentional costs, specific
pro~goalsmaybemorenecesseryforlowability or poorly prepared learners as a means for guiding their efforts (Snow, 1989). An important conditional here is that the nature of the proximal goals themselves must not induce low self efficacy for achieving them (Mitchell et d,1994). The goals for trainees incorporated in the instmational method may be congruent with or divergent fiom the 'sense making' goal retranslations made by the trainees themselves (Baldwin & Magjuka, 1997). For example, individuals may see the goals for technology training as a signal that management thinks they are less than competent which in tum has deleterious effects on their commitment and motivation mein & Ralls, 1997). In g e n d the goals communicated to the trainees by the iastTuctional methods are a critical influence on lcaming and deserve careful attention. Again, it is imporbat to keep in mind that the training objectivca are directed at the training designer and arc conceptually distinct h m the goals presented to the train= by the instructional methods.
Feedback There are thousands of citations in the feedback literature and it is a venerable topic in training and instruction. Traditionally, feedback has been seen as having both motivational and informational properties and instructional methods should provide lots of it; that is, feedback is good. Relative to occupational traiuing there have been three influential reviews of feedback research and theory (Baiza, Doherty & Conncr, 1989; Ilgen, Fisher & Taylor, 1979; Kluger & DeNisi, 1996), 'which have.provided an increasingly differentiated view of the role feedback plays in instruction. Feedback can come h m several s o w (Blum & Naylor, 1968). It can be intrinsic (information generated internally such as when the adjustment to a control 'feels right') or extrinsic (infoxmation h m external sources); and if extrinsic, it can be primary (based on the individual's self evaluation of external events) or secondary (based on someone else's evaluation' of the trainee's performance). The general findings have been that.feedback has significant effects on behavior to tbe extent that all appropriate sources are used (e.g., does the htmctional method allow people to monitor their own performance), it is relatively fresuent, and it is not delayed. In addition, feedback must be accurate, and accepted as accurate: Acceptance is a function of a number of factors (Ilgen et al., 1979) such as the perceived expertise, reliability, power, and attractiveness of the source. Also, the efkcts of positive and negative feedback are not equal (positive feedback is g e n d y better). However, negative feedback can have positive effects and positive feedback can have negative effects. For example, if feedback is too fiequent it can decrease
.
Individual and Tenm IRoining
the perceived validity of'the feedback (e.g., the individual begins to question the reasons for all the attention). The latest review of the feedback li&ture (JUugcr & DeNisi 1996), and its associated m&a analysis of feedback effects on performance, made some provocative hypotheses in an attempt to explain why (a) the effects of feedback were negative in ahnost 4003 of the controlled studies,(b) feedback sign was not a significant moderator of feedback effects, and (c) verbal praise was worse than no feedback at all. In the Ktuger and &Nisi review, the critical parameters seemed to be (1) whether increased effort or more knowledge and skill were the critical determinants of perfonmxe increments,(2) whether the content of the feedback was a general statement tied to the overall outcome versus being focused on specific performance dctambnts that could be changed (whicb they called cue feedback), and (3) the codidcnce or selfefficacy of the fbedback receiver. In general, they suggest that overall outcome feedback @articularly verbal praise) docs not work eitha because it directs attention away h m the task and toward global egocentered self evaluations or because perfonnance cannot be increased by a simple increase in effort V i the only conditions under whicb general positive feedback might han positive effects on performance aze when eeort level is the primary determinant of performance and the learner suffers &om low selfconfidence; which may be a relatively rare set of conditions for occupational training. It follows that feedback should a1ways.h informational (it really has no useful incentive or reward fimction in the training context) and directed at the specific knowledge and skill determinants of performance. Consequently, negative fetdback can contain valuable information that will lead to performance improvement, if the individual is committed to the trainingperformance goals. Goal commitment itself must come h m other sources.
Practice Practice, like f-4 is a venerated learning principle that has generated a large experimental research literature k d which has also undergone some rethinkiug. Perhaps the extreme position is taken by Ericggon and his colleagues (Ericsson & Charness, 1994, Ericsson & Smith, 1991) who argue that the primary determinant of a very high level p a t ' i c c is many y m of deliberate, gujded practice, and not basic aptitudes, which can get one only so far. Delihrate practice is very goal oriented (e.g., Jack Nicklaus esserts that he nevw made a swing'on the golf practice range without a specific goal in mind) and virhlany always uudcr . expert guidance. In Kluger a d DeNisi's tcrms,the specific cue oriented feedback must be from the
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most knowledgeable source possible. This asnution has sparked much debate (Howe, Davidsoa & Sloboda, 1998) over the efficacy of stable traits vr. training and experience as the fundamental drtsrminants of expertise. One element that seem less controversial is the recognition that an important determinant of expert perfonnance o h involves the development of critical sldlls to automaticity via appropriate amounts of practicx (Rogers et al., 1997). This again raises the issue of what &mance detemnhants represent combtent skills and how much practice it taka to reach automaticity. The research cited by Exicsson and Chamem to support the value of deliberate guided practice dea& primarily with expertise in athletics, the pdixming arts, or very specialized cognitive &ills. It is not clear whether the payoff would be equally large fix occupations which are made up of large sets of complex tasks that require very controlled processing. The type and duration of practice can a h be dircussed in the context of performance in the training setting vmus performance in the actual job (&enskin et al., 1997). Schmidt and Bjork (1992) argue that very frequent practice in the h h b g environment may not be optimal, and could even be harmful for retention and transfer if every ptrrctiOe trial deals with exactly the same task and feedback conditions, and exactly the same context (Arthur et al., 1997; Shute & Gawlick, 1995). Tbis might promote the highest level of skill acquisition in the shortest possible time in the training environmcat but it could h a m retention and transfer for several reasom. First, kquent repetition of the same set of responses to the same task requirmcnts in ths trahiug environment may not represeat tht relevant variation in the transfer situation. Negative traneftr could occur. Second, making things 'easy' fOT the trainee w ill reduce processing demand8 during training and detract fimn the 'prodwion effort' that facilitates long-tam storage and mention. Third, providing continual external feedback &om secondary sources makes it less likely that individualswillleamtocomctlyinteqmtthcirownpsrf-ce errors and provide their own feedback, which they must do in the transfer environmsnt. Consequently, practice sessions in training should vary the frequency of feedback, the conditians under which practice trials are conducted, and tfw: nature of the task requirements themselva. Racticc also consumes training time and there i6 always a trade off between time devoted to practico and time devoted to presenting additional t r a i d q content (Carroll, 1997). FinaUy, designing practice to minimize leamer errors may not be optimnl for complex ski& that are dominated by controlled processing. Here, emrm and the steps taken to correct them can be vuy beneficial for both retention and taansfer (Carlson, Lundy & Schneider, 1992; Frese et al., 1991; Ivancic & Hesketh, 199511996),
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In sum, the use of goals, feedback, and practiceto optimize tbe effects of instructional metboda on acquisition, retention, end transfer incorporates a complex set of contingencies that must be taken into account. Under a broad set of ckcumstmceq certain kinds of goals, feedback strategies, or practice proixdurts can do more hann than good
ALTERNATIVE MODELS FOR TRAINING DESIGN
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Although we thinlr of the previous list of design parameters as a set of universals, others might see it as something that can be cast against altemaths. Most discussions of alternative 'models' for training design begin with pointed criticism of either the 'traditional approach' (h4cGehce & Thaycr, 1961) or the Instructional Systems Development (ISD) model (Vinebcrg & Joyner, 1980)' which are in fact the primary antecedents of our list of universals. The traditional approach is said to view the training function as a m w l y wnstraincd micro enterprise that focuses only on the individual trainee as a unit of analysis and only on individual endof'corn & as the critaicm of interest. It dog not view the training function as a subsystem within a larger and very wmpla organizational system. which a obviously is ( ~ ~ ~ z l o w s kSalas, i 1997; Noc, 1999). Instructional Systems Development which grew out of the earlier work of Gagnt (1962), Gagnt and Briggs (1979). and Glam (1976) has had its most intense applications in the military services (Vinebag & Joyner, 1980). In its worst light, it is described as based on overly detailed task and nceds analyses, specification of a large number of very namnv and specific training objectives that compulsively follow a certain format, and rigid prescriptions for trainiag content. Presumably this prasents a very structured experience for the trainee who must move through it in lockstep fashion. According to tbe cumnt critics (e.g., Gordon & Zcmkc, 2000), ISD is counter pmductive in today's world because (1) It is too slow and clumsy and bogs the design process down in almost endlc88 detailed prescriptim; (2) It is not a true sciencebased t&hnolow and &t make unambiguous substantivepresaiptiions for how training should be designed. 1t-is .&ly a long detailed sd of dural steps to follaw; (3) It produca bad training designs bccauac it' concclltratea on pmccdud chedhts,. loses sight of the original bushes8 purpose (i.e.. the critical training needs to be met), a d is geared to the slowest and mod ignorant tmimos, nsulting in courses that aie dull and and (4) The ISD world view is that the training design= is all knowing and authoritative while the trainee is totally resourceless and needs to be
guided every step of the way; and this f l k in the face of the obvious fact that many trainm are
veryresourcefirlandmaMgetbcirownlcaIning quite
mn
There is probably uome truth and much dsaving caricahae rcpmted in these cumntcriticisms. Prawat and Wonhhgkm (1998) have pointed out that a not uncommon sequence in ducational and inslructional theoly developmet is that new innovations,while they may be well dastood by the original pcpcmkq an usually not specified well enough for subsequent usaa to prevent all mannu of misapplication. This will usually generate enough bad examples to energize the critics and generate a backlash. However, 'evidmce' for the shof the traditional modeb UIUally consist of vaguely d c s c r i i exampla hat most would agree repnsent bad practice. A6 6 ru we know,no systematic evidunx or doammtatiolr exists to substantiate the criticienlu. The altcmatives to the traditional models almost always consist of a very sketchily h ic~cceu story implanented by the critic. Howevar, not dl critics of LSD and the traditional model arc totally self-serving. Over the past 10-20 year& the mort recognizable and thoughtful. altcmatiw positianr regarding training design seem to be the followiag.
Design by Partldpatlon This model arguca that the most effechve t d h g design results h m the f i ~ Uparticipation of traiwn and ~ c c on s questians of specifying tddq needs and objectives, designing course content, and choosing instructional methods. Argyrir (1977, 1992) has been the primary advocate of this podtion which he calls double loop learning. Participation should lead to higher trainee motivation, more relevant specifications of training needs, and more relevant mining content. Traditional mod& am simply too authoritarb or 'theory X'. Participation as a model of training design is also advocated by Wodkowski (1985) and Knowla (1984). However, Baldwin and Magjulca (1997) argue, on the byir of their own review, that the available evidence simply does not support the central tenets of the tion.modeL That is, it docs not cansisttntly 1 d . b higher achicvem& of more relevanttrain& go&; and participation may evtn create expccmions tlut
Self-Design,Constructivism, and the Minimalist Position Many of the criticisms of ISD in the organizational training literatun assert that most individualn rrt
highlymotivatcdtoimprovetbeiiskilluandwill seek training &tics to do no virtually 0x1 a continuous basis (Carroll, 1997). That is, givco hslf
