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Abstract

This chapter reviews the training research literature reported over the past decade. We describe the progress in five areas of research including training theory, training needs analysis, antecedent training conditions, training methods and strategies, and posttraining conditions. Our review suggests that advancements have been made that help us understand better the design and delivery of training in organizations, with respect to theory development as well as the quality and quantity of empirical research. We have new tools for analyzing requisite knowledge and skills, and for evaluating training. We know more about factors that influence training effectiveness and transfer of training. Finally, we challenge researchers to find better ways to translate the results of training research into practice.
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Annu. Rev. Psychol. 2001. 52:471–99
Copyright
c
2001 by Annual Reviews. All rights reserved
THE SCIENCE OF TRAINING: A Decade
of Progress
Eduardo Salas
1
and Janis A. Cannon-Bowers
2
1
Department of Psychology and Institute for Simulation & Training,
University of Central Florida, Orlando, Florida 32816-1390;
e-mail: esalas@pegasus.cc.ucf.edu
2
Naval Air Warfare Center Training Systems Division, Orlando,
Florida 32826-3224; e-mail: Cannon-BowJA@navair.navy.mil
Key Words training design, training evaluation, distance training, team training,
transfer of training
Abstract This chapter reviews the training research literature reported over the
pastdecade. Wedescribetheprogressinfiveareasofresearchincludingtrainingtheory,
trainingneedsanalysis, antecedenttrainingconditions, trainingmethodsandstrategies,
and posttraining conditions. Our review suggests that advancements have been made
that help us understand better the design and delivery of training in organizations, with
respect to theory developmentas well as the quality and quantity of empirical research.
We have new tools for analyzing requisite knowledge and skills, and for evaluating
training. We know more about factors that influence training effectiveness and transfer
of training. Finally, we challenge researchers to find better ways to translate theresults
of training research into practice.
CONTENTS
INTRODUCTION ................................................ 472
Initial Observations
............................................. 473
Theoretical Developments
......................................... 473
TRAINING NEEDS ANALYSIS
..................................... 475
Organizational Analysis
.......................................... 475
Job/Task Analysis
............................................... 476
ANTECEDENT TRAINING CONDITIONS
............................. 477
Individual Characteristics
......................................... 478
Training Motivation
............................................. 479
Training Induction and Pretraining Environment
......................... 480
TRAINING METHODS AND INSTRUCTIONAL STRATEGIES
.............481
Specific Learning Approaches
...................................... 482
Learning Technologies and Distance Training
........................... 483
Simulation-Based Training and Games
................................ 484
Team Training
................................................. 485
0066-4308/01/0201-0471$14.00
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POST-TRAINING CONDITIONS .................................... 486
Training Evaluation
............................................. 487
Transfer of Training
............................................. 488
FINAL OBSERVATIONS, CONCLUSIONS, AND THE FUTURE
.............489
INTRODUCTION
Inthe 30yearssincethefirstreviewoftraining intheAnnualReviewofPsychology,
things have progressed dramatically in terms of both the science and practice
of training. On the practice side, socio-cultural, technological, economic, and
politicalpressureshave all combined to force modern organizationsto take a closer
look at their human capital in general, and training in particular (Thayer 1997,
Howard 1995). In fact, now more than ever, organizations must rely on workplace
learning and continuous improvement in order to remain competitive (London &
Moore 1999). In addition, organizations have shifted their views about training
from a separate, stand-alone event to a fully integrated, strategic component of the
organization. New training-related approaches, including action learning, just-in-
time training, mentoring, coaching, organizational learning, and managing skill
portfoliosareallcurrentlybeingexplored.Finally, modernorganizationsmustcope
withtrainingneedsassociatedwiththechangingdemographicsofthepopulation—
both an older and more diverse workforce can be expected as we move into the
new millennium.
It is important to note that improved training comes at a cost. Recent esti-
mates suggest that the investment in training activities in organizations ranges
from $55.3 billion to $200 billion annually (Bassi & Van Buren 1999, McKenna
1990), an investment that has not only created a growing interest in training, but
also in learning technologies and performance-improvement processes, practices,
and services. In fact, there is an increasing concern in organizations that the in-
vestment made in training must be justified in terms of improved organizational
performance—increased productivity, profit, or safety; reduced error, enhanced
market share (e.g. Huselid 1995, Martocchio & Baldwin 1997, Salas et al 2000).
The past 30 years have also witnessed tremendous growth in training research.
This trend has been so pronounced that we are led to conclude that there has
been nothing less than an explosion in training-related research in the past 10
years. More theories, models, empirical results, reviews, and meta-analyses are
available today than ever before. Whether this research is having an effect on
training practice—that is, a meaningful impact on how organizations actually
train—is a matter of some debate. We return to this issue after documenting the
many advances in training research over the past decade.
This is the sixth review of training and development to appear in the Annual
Review of Psychology(see Campbell 1971, Goldstein 1980, Wexley 1984, Latham
1988, Tannenbaum & Yukl 1992). In this review, we focus on research published
from1992 to January 2000. Similar to Tannenbaum & Yukl’s (1992), our reviewis
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THE SCIENCE OF TRAINING 473
selectiveanddescriptive. We focusprimarilyontheresearchthatisconcernedwith
the design, delivery, and evaluation of training in work organizations. Our review
is organized as follows. We first discuss the recent theoretical advancements in
training over the past decade. Then we address the relevant research on training
needs analysis, including organization, job/task, and person analysis. Following
this, we address antecedent training conditions (i.e. pretraining variables) that
may enhance or disrupt learning. Next we turn our discussion to research on
training methods and instructional strategies. In this section we discuss recent
developments in simulation-based training, learning approaches, team training,
and the influence of technology on training research and practice. Finally we
review the research on post-training conditions. This includes a discussion of
training evaluation and transfer of training. Wherever appropriate we point out
the research needs and gaps. We conclude with a few observations and trends
and a word about the future. Consistent with previous reviews, this review does
not cover basic issues involved in skill acquisition and learning, organizational
development, socialization in organizations, or educational psychology. We also
do not synthesize the large literature in practitioner-oriented publications.
Initial Observations
Our first observation is that in 30 years of documenting the progress of training
research, it is clear that the field, by any measure, has changed dramatically—
for the better. Now, as recent reviews have documented, training-related theories
abound. As noted earlier, there is also more empirical training-related research
going on—in the field as well as in the lab—than ever before. Researchers are
adopting a systems view of training and are more concerned with the organiza-
tionalcontext. Thereare newmodels, constructs(e.g.opportunitytoperform), and
influences(e.g. technology)inthereported research.Thetraditional trainingevalu-
ationparadigmhasbeen expanded,andtherearemoreevaluationsbeingconducted
and reported. There are better tools with which to conduct training evaluations,
and better (and more practical) experimental designs have emerged. The field can
now offer sound, pedagogically based principles and guidelines to practitioners
and instructional developers. Furthermore, the impact of training on performance
and organizational effectiveness is being felt (Bassi & Van Buren 1999), although
clearly more documentation is needed here. Finally, there are more books and
textbooks related to training (e.g. Ford et al 1997, Quinones & Ehrestein 1997,
Noe 1999, Wexley & Latham 2000, Goldstein 1993; plus over 20 more that have
been reviewed in Personnel Psychology). The science of training has progressed
and matured—it is now truly an exciting and dynamic field. As evidence for this
assertion, we summarize some of the latest theoretical advancesinthenextsection.
Theoretical Developments
There have been some influential theories developed about training since 1992.
In fact, the past decade has offered us myriad new and expanded theoretical
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frameworks, as well as concepts and constructs. This thinking is deeper, richer,
more comprehensive, and more focused. More importantly, the field has been
energized by these developments so that empirical work has followed. Some of
these frameworks and concepts are broad, general, and integrating. For example,
Tannenbaum and colleagues provided an integrative framework for all the vari-
ablesthatinfluencethedesignanddelivery of training (see Tannenbaum et al 1993,
Cannon-Bowers et al 1995). The framework outlines in detail the pretraining and
during-training conditions that may influence learning, as well as the factors that
may facilitate the transfer of skills after training. Kozlowski & Salas (1997), draw-
ing from organizational theory, discussed the importance of characterizing the
factors and processes in which training interventions are implemented and trans-
ferred in organizations. Moreover, Kozlowski and colleagues (Kozlowski et al
2000) consider organizational system factors and training design issues that influ-
ence the effectiveness of vertical transfer processes. Vertical transfer refers to the
upwardpropagationofindividual-leveltraining outcomesthatemergeasteam-and
organizational-leveloutcomes.Thisissuehasbeenlargelyneglectedbyresearchers
yet is suggested to be crucial to training effectiveness. Similarly, researchers have
beguntounderstandandoutlinethebarriersandmythsthatexistinorganizationsas
they implement training (Dipboye 1997, Salas et al 1999). In other work,
Kraiger et al (1993) provided new conceptualizations of learning and evalua-
tion theory, approaches, and measurement. These authors expanded Kirkpatrick’s
(1976) evaluation typology by incorporating recent notions in cognitive
psychology.
Other conceptual developments are more focused. For example, Ford et al
(1997) invoked “the opportunity to perform” construct as a way to understand
the transfer of training process. Colquitt et al (2000) summarized (qualitatively
and quantitatively) the literature on training motivation and offered a new, inte-
grative model. Cannon-Bowers & Salas (1997) proposed a framework for how to
conceptualize performance measurement in training. Thayer & Teachout (1995)
developed a model to understand the climate for transfer in organizations, as well
as in-training conditions that enhance transfer. Cannon-Bowers et al (1998) ad-
vanced a number of conditions, concepts, and interventions that may enhance
practice. Ford and colleagues have looked at individual differences and learner
control strategies (e.g. Ford et al 1998). Training researchers have also examined
variablessuchasthe pretraining context(e.g. Baldwin&Magjuka1997, Quinones
1995), conscientiousness and training outcomes(e.g. Colquitt & Simmering 1998,
Martocchio&Judge1997),individualand situational characteristicsthatinfluence
training motivation (e.g. Facteau et al 1995, Mathieu& Martineau 1997), and par-
ticipation in developmental activities(e.g. Noe & Wilk 1993, Baldwin & Magjuka
1997), just to name a few.
In sum, these theoretical advancements have provided a much needed forum in
which to discuss, debate, analyze, and understand better the design and delivery of
training in organizations. Moreover, they have provided an organized framework
in which systematic research could be couched. Our conclusion is that training
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THE SCIENCE OF TRAINING 475
research is no longer atheoretical as charged by our predecessors. We believe the
field is healthier because of these influences and all the empirical work that has
followed.
TRAINING NEEDS ANALYSIS
It is well acknowledged that one of the most important steps in training develop-
mentisconductingatrainingneedsanalysis. Thisfirststepintrainingdevelopment
focuses on the process of deciding who and what should be trained. A training
needs analysis is primarily conducted to determine where training is needed, what
needs to be taught, and who needs to be trained (Goldstein 1993). This phase
has several outcomes. One is the specification of learning objectives, which in
turn shape the design and delivery of training, as well as the process of criterion
development. Consistent with Tannenbaum & Yukl (1992), we found a limited
amount of empirical work on training needs analysis. In this section we discuss
twocomponents: organizationalanalysisandjob/taskanalysis. We briefly address
the third phase—person analysis.
Organizational Analysis
Thepurpose of an organizational analysis is to outline thesystemwide components
of the organization that may affect the delivery of a training program (Goldstein
1993). That is, it focuses on thecongruence between training objectives with such
factors as organizational goals, available resources, constraints, and support for
transfer. Unfortunately, many training programs fail to reach their goals because
of organizational constraints and conflicts, which could have been identified and
ameliorated before training was implemented. Hence, conducting an organiza-
tional analysis is an important first-step training design. The best treatment of this
topic can be found in Goldstein (1993).
Only recently have training researchers begunto pay attention to organizational
analysis. One study, conducted by Rouiller & Goldstein (1993) in a chain of
fast food restaurants, demonstrated that organizational climate (e.g. situational
cues, consequences) was a powerful predictor of whether trainees transferred the
learned skills. A second study conducted in a chain of supermarkets by Tracey
et al (1995)showedthat organizational climate and culture were directly related to
posttraining behaviors. Clearly, these two studies illustrate howpowerful an effect
the organizational environment can have on whether newly acquired knowledge,
skills, and attitudes (KSAs) are applied on the job (see also the transfer of training
section).
As job requirements change, so does the need to help organizations plan
their human resources activities. A number of issues have emerged that are re-
lated to organizational analysis. For example, authors have expressed the need
to understand how the organizational context influences human resources strate-
gies (e.g. Tannenbaum & Dupuree-Bruno 1994) to enhance continuous learning
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environments (e.g. London & Moore 1999), to manage knowledge effectively
(e.g. Tannenbaum 1997), and to determine the best organizational strategy
(e.g. who is responsible for training?) for learning and training (e.g. Martocchio &
Baldwin 1997). Obviously, organizational analysis is crucial for ensuring the
success of a training program. However, more research is needed to develop
practical and diagnostic tools to determine the organizational context relative to
training.
Job/Task Analysis
Historically, job/task analysis has been used to identify the information necessary
to create the learning objectives (Goldstein 1993). A job/task analysis results in a
detaileddescriptionoftheworkfunctionstobeperformedonthejob,theconditions
underwhichthejobistobeperformed, andtheKSAsneededtoperformthosetasks.
Inthepastdecade,newresearchaimedatdevelopingsolidmethods, approaches,
and knowledge elicitation techniques for determining training needs and require-
ments have emerged. For example, Arvey et al (1992) explored the use of task
inventories to forecast skills and abilities necessary for a future job. The results
indicated that such forecasting predictions could represent useful information to
training analysis. Wilson & Zalewski (1994) tested an expert system program as
a way to estimate the amount of 11 abilities required for performing a job. The
results indicated that most incumbents preferred the expert system method to the
traditional ability rating scales.
Some attention has been given to better understanding the job/task analysis
process for training purposes. For example, Ford et al (1993) examined the impact
of task experience and individual factors on task ratings of training emphasis. Re-
sults indicated that as experience (and self-efficacy) increased over time, it tended
to cause trainees to increase their ratings of training emphasis. Also, a few re-
searchershave focusedon outlining a task analysis procedure for teams (e.g. Baker
et al 1998, Bowers et al 1993, Blickensderfer et al 2000). Much more research is
needed, however. Specifically, we need to design and develop a methodology that
helps instructional designers and analysts uncover the team-based tasks and their
related KSAs.
Cognitive Task Analysis Cognitive task analysis refers to a set of procedures for
understandingthementalprocessingandmentalrequirementsforjobperformance.
It has received much attention recently (see Dubois et al 1997/1998, Schraagen
et al 2000), fueled primarily by interest in understanding how trainees acquire and
develop knowledge, and how they organize rules, concepts, and associations (see
Zsambok & Klein 1997). In addition, research aimed at uncovering the nature of
expertise and how experts make decisions in complex natural environments has
led to the development of tools such as cognitive task analysis (see Salas & Klein
2000, Schraagen et al 2000).
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Cognitive task analysis is based on techniques (e.g. verbal protocols) used by
cognitive scientists to elicit knowledge from subject matter experts. Products of a
cognitive task analysis includeinformation-generating templates for mental model
development, cues for fostering complex decision-making skills, cues for devel-
oping simulation and scenarios used during training, and information for design-
ing performance measurement and feedback protocols. For example, Neerincx &
Griffoen (1996) applied a cognitive task analysis to assess the task load of jobs
and to provide indicators for the redesign of jobs. This new application showed
its benefits over the “old” task load analysis.
A cognitive task analysis can complement existing (behavioral) forms of
training-need analysis. For example, research on meta-cognition suggests that
through continued practice or experience, individuals automatize complex behav-
iors, thus freeing up cognitive resources for monitoring and evaluating behavior
(Rogers et al 1997). By determining trainees’ current complex cognitive skills,
instructional designers can gain insight into trainees’ capacity for proficiency and
diagnose performance deficiencies. Thus, training-needs analysis should identify
not only the requisite knowledge and skills to perform tasks, but also the cues and
cognitions that enable trainees to know when to apply those skills. By incorporat-
ingthedevelopmentoftheseskillsintotraining,instructionaldesignerscanprovide
trainees with valuableself-help tools. Cognitivetask analysis is becoming a useful
toolbutneeds more development. Specifically, atheoreticallydriven methodology
that clearly outlines the steps to take and how to analyze the data is needed.
We found no empirical work regarding the third phase of training-needs
analysis—person analysis. However, the emerging literature of 360
feedback
may be relevant. This approach identifies individual strengths and weaknesses,
but perhaps more importantly, provides suggestions for improvement that often
revolve around training and development activities.
In summary, it is interesting to note that whereas most training researchers
believe and espouse that training-needs analysis is the most important phase in
training, this phase remains largely an art rather than a science. We need more
research that would enable us to develop a systematic methodology to determine
the training needs of organizations.
ANTECEDENT TRAINING CONDITIONS
Events that occur before training can be as important as (and in some cases more
important than) those that occur during and after training. Research has shown
that activities that occur prior to training have an impact on how effective training
turns out to be (Tannenbaum et al 1993). These factors fall into three general
categories: (a) what trainees bring to the training setting, (b) variables that engage
the trainee to learn and participate in developmental activities, and (c) how the
training can be prepared so as to maximize the learning experience. Each of these
is described in more detail below.
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Individual Characteristics
Cognitive Ability Research aimed at understanding how characteristics the
trainee brings to the training environment influence learning has proliferated in
the past decade. For example, Ree et al (1995) developed a causal model show-
ing the role of general cognitive ability and prior job knowledge in subsequent
job-knowledge attainment and work-sample performance during training. The
resulting model showed that ability influenced the attainment of job knowledge
directly, and that general cognitive ability influenced work samples through job
knowledge. Similar findings have been obtained by Ree & Earles (1991), Colquitt
et al (2000), Randel et al (1992), Quick et al (1996), Kaemar et al (1997), Warr &
Bunce (1995), and many others. Therefore, it is safe to conclude, based on this
body of evidence (and others as well, e.g. Hunter 1986), that g (general intelli-
gence) is good—it promotes self-efficacy and performance, and it helps a great
deal with skill acquisition. Clearly, those who havehigh cognitive ability (all other
things being equal) will likely learn more and succeed in training.
At this point, we probably need to look more closely at low-ability trainees and
conduct research on how to optimize learning for them. Also, it might be worth-
while to examine in more depth concepts such as tacit knowledge and practical
intelligence (Sternberg 1997) and their relation to on-the-job learning. Finally, it
is worth noting that cognitive ability is a viable predictor of training performance
(i.e. learning), but not necessarily performance on the job. Many jobs have re-
quirements that go beyond cognitive ability (e.g. psychomotor demands), and/or
depend on other factors (e.g. motivation) for success. Therefore, it is important
to understand the nature of the job to determine whether cognitive ability will be
a valid predictor of training transfer.
Self-efficacy This construct has been widely studied in this past decade. The
findings are consistent: Self-efficacy, whether one has it before or acquires it
during training, leads to better learning and performance. Self-efficacy (the be-
lief that one can perform specific tasks and behaviors) is a powerful predictor of
performance, as has been shown time and time again (e.g. Cole & Latham 1997
Eden & Aviram 1993, Ford et al 1998, Mathieu et al 1993, Martocchio 1994,
Martocchio & Webster 1992, Mathieu et al 1992, Quinones 1995, Mitchell et al
1994, Phillips & Gully 1997, Stevens & Gist 1997, Stajkovic & Luthans 1998).
Self-efficacy also mediates a number of personal variables including job satisfac-
tion,organizationalcommitment, intentiontoquit thejob,therelationship between
training and adjustment in newcomers (Saks 1995), and the relationship between
conscientiousness and learning (Martocchio & Judge 1997). Self-efficacy has also
beenshown to havemotivationaleffects(e.g. Quinones1995), to influence training
reactions (Mathieu et al 1992), and to dictate whether trainees will use training
technology (Christoph et al 1998).
In sum, it is well established that self-efficacy enhances leaning outcomes and
performance. A research need that still remains is to expand what we know about
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THE SCIENCE OF TRAINING 479
self-efficacy at the team level. Whereas a few studies have investigated collective
efficacy in training (e.g. Guzzo et al 1993, Smith-Jentsch et al 2000), considerably
more work is needed to better understand the mechanisms of collective efficacy in
learning as a means to raise team performance. In addition, it might be useful to
considertheuseofself-efficacyasadeliberatetrainingintervention(i.e.developing
training targeted at raising self-efficacy), as well as a desirable outcome of training
(i.e. as an indicator of training success).
Goal Orientation Goal orientation has received considerable attention in recent
years (e.g. Brett & VandeWalle 1999, Ford et al 1998, Phillips & Gully 1997).
This construct is broadly conceptualized as the mental frameworkused by individ-
uals to interpret and behave in learning- or achievement-oriented activities. Two
classes of goal orientation have been identified (Dweck 1986, Dweck & Leggett
1988): (a) mastery (or learning) goal orientation, whereby individuals seek to
develop competence by acquiring new skills and mastering novel situations, and
(b) performance goal orientation, whereby individuals pursue assurances of their
own competence by seeking good performance evaluations and avoiding negative
ones. There is a debate in the literature as to whether goal orientation is a disposi-
tion, a state, or both (e.g. Stevens & Gist 1997), whether it is a multidimensional
construct (e.g. Elliot & Church 1997, VandeValle 1997), or whether these two
goal strategies are mutually exclusive (Buttom et al 1996). Although continued
research will bring more conceptual clarity, recent studies have shown, in general,
that goal orientation influences learning outcomes and performance. For example,
Fisher & Ford (1998) found that a mastery orientation was a strong predictor of a
knowledge-based learning outcome. Ford et al (1998) showed that mastery orien-
tationwaspositively relatedtothe meta-cognitiveactivityofthetrainee. Phillips&
Gully (1997) demonstrated that mastery goal orientation was positively related to
self-efficacy. All these results are promising. More research is needed to de-
termine how goal orientation is developed. Specifically, it must be determined
whether goal orientation is a relatively stable trait, or if it can be modified prior to
training. Should the latter be true, then efforts to move trainees toward a mastery
orientation should be developed.
Training Motivation
Training motivation can be conceptualized as the direction, effort, intensity, and
persistence that trainees apply to learning-oriented activities before, during, and
after training (Kanfer 1991, Tannenbaum & Yukl 1992). Recently, several studies
have found (and confirmed) that trainees’ motivation to learn and attend train-
ing has an effect on their skill acquisition, retention, and willingness to apply
the newly acquired KSAs on the job (e.g. Martocchio & Webster 1992, Mathieu
et al 1992, Quinones 1995, Tannenbaum & Yukl 1992). Whereas the literature is,
in general, clear about the influence of training motivation on learning outcomes,
it has lacked some conceptual precision and specificity, and has been somewhat
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piecemeal. An exception is a recent effort by Colquitt et al (2000) that has shed
light on the underlying processes and variables involved in understanding training
motivation throughout the training process. Their integrative narrative and meta-
analytic review suggest that training motivation is multifaceted and influenced
by a set of individual (e.g. cognitive ability, self-efficacy, anxiety, age, conscien-
tiousness) and situational (e.g. climate) characteristics. This effort provides the
beginnings of an integrative theory of training motivation—a much needed syn-
thesis and organization.
Anumberofimportantimplicationsforresearchandpracticecanbedrawnfrom
Colquitt et al’s review. For example, they point out the need to assess trainee’s
personalityduringtraining-needsanalysis, amuchneglectedorignoredassessment
during person analysis. In fact, Colquitt et al also called for the need toexpand the
kindofpersonalityvariableswehaveexaminedinrecentyearstoincludeemotions,
adaptability, traitgoalorientation,andotherBigFivevariables. Anotherimportant
implication is the link they found between age and motivation to learn—older
workers showed lower motivation, learning, and post-training efficacy. However,
it may be prudent to consider this conclusion carefully because a host of other
issues must be considered when discussing the training needs of older workers
(including the design of training itself). Clearly, in an era of technology-driven
instructionandanagingworkforce,achallengeforinstructionaldeveloperswillbe
todesignlearningenvironments where older trainees can be trained(andretrained)
with ease.
In the future, we also need to continue gaining a deeper understanding of train-
ing motivation because it is crucial for learning and has direct implications for
the design and delivery of training. Future work should consider those factors
that influence training motivation for job development activities and for situa-
tions in which workers acquire new skills through informal learning mechanisms.
Longitudinal studies are also needed.
Training Induction and Pretraining Environment
Considerable research has gone into understanding which factors help trainees to
optimize the benefits of training. These are usually interventions employed before
training to ensure that the trainee gets the most out of the learning experience.
Prepractice Conditions It is well documented that practice is a necessary con-
dition for skill acquisition. However, all practice is not equal. In fact, the precise
nature of practice and its relationship to learning outcomes has been largely igno-
red or misunderstood. Recent thinking and research is beginning to suggest that
practice may be a complex process, not simply task repetition (e.g. Ehrenstein
et al 1997, Shute & Gawlick 1995, Schmidt & Bjork 1992); we address this
issue further in Specific Learning Approaches. For example, Cannon-Bowers et al
(1998) provided a frameworkfor delineating the conditions that might enhance the
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THE SCIENCE OF TRAINING 481
utility and efficacy of practice in training. They drew from the literature a number
of interventions (e.g. meta-cognitive strategies, advanced organizers, and prepara-
tory information) that can be applied before actual practice as a way to prepare
the trainee for training. Empirical verification of these interventions needs to be
conducted.
The Pretraining Environment and Climate Can pretraining contextual factors
also affect learning outcomes? Recent research suggests that the manner in which
theorganizationframesthetrainingandthenatureoftrainees’previousexperiences
intrainingdoinfluencelearningoutcomes. Forexample,Quinones(1995) demon-
stratedthat the manner in which training wasframed (i.e. as advancedor remedial)
influenced training motivation and learning (see also Quinones 1997). Martocchio
(1992), who labeled the training assignment as an “opportunity, showed similar
findings. Smith-Jentsch et al (1996a) demonstrated that trainees’ previous expe-
riences with training (e.g. prior negative events) affected learning and retention.
Baldwin & Magjuka (1997) explored the notion of training as an organizational
episode and laid out a framework of other pretraining contextual factors (e.g. vol-
untaryversusmandatoryattendance)that mayinfluencemotivationto learn. These
studies suggest that experience with training (both task-based and event-based) is
important to subsequent training outcomes. This is certainly a neglected area, and
one in which much more work is needed. Specifically, we need to know how
these experiences shape self-efficacy, expectations about the training, motivation
to learn and apply skills on the job, and learning.
TRAINING METHODS AND INSTRUCTIONAL
STRATEGIES
Instructional strategies are defined as a set of tools (e.g. task analysis), methods
(e.g. simulation), and content (i.e. required competencies) that, when combined,
create an instructional approach (Salas & Cannon-Bowers 1997). Most effective
strategies are created around four basic principles: (a) They present relevant in-
formation or concepts to be learned; (b) they demonstrate the KSAs to be learned;
(c) they create opportunities for trainees to practice the skills; and (d) they pro-
vide feedback to trainees during and after practice. Because there is no single
method to deliver training, researchers continue to address how to best present tar-
geted information to trainees. Specifically, researchers are seeking cost-effective,
content-valid, easy-to-use, engaging, and technology-based methods (e.g. Baker
et al 1993, Bretz & Thompsett 1992, Steele-Johnson & Hyde 1997). In the next
section, we review research related to instructional strategies in several major
categories. First, we review specific learning approaches and learning technolo-
gies and distance training. Next, we cover simulation-based training and games,
followed by a review of recent work in team training.
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Specific Learning Approaches
Traditionally, training researchers have investigated how to optimize learning
and retention by manipulating feedback, practice intervals, reinforcement sched-
ules, and other conditions within the learning process itself. In this regard, Fisk,
Kurlik, and colleagues (Kirlik et al 1998) improved performance in a complex
decision-making task by training consistently mapped components of the task to
automaticity (i.e. so that they could be performed with little or no active cognitive
control). Along these same lines, Driskell et al (1992) conducted a meta-analysis
oftheeffectsof overlearningonretention. The results of their analysis showedthat
overlearning produces a significant effecton retention which, in turn, is moderated
by the degree of overlearning, length of retention period, and type of task.
Attention has also been focusedon developing collaborative training protocols.
This is distinguished from team training (see below) by the fact that team training
applies to training competencies that are required for performance of a team task.
Collaborative learning, on the other hand, refers to situations where trainees are
trained in groups, but not necessarily to perform a team task. The idea is that there
are features of group interaction that benefit the learning process (e.g. the oppor-
tunity for vicarious learning or interaction with peers). For example, Arthur et al
(1997) provided strong support and justification for the ongoing use of innovative
dyadic protocols (i.e. training two trainees at once) for the training of pilots and
navigators in both military and nonmilitary settings. However, Arthur et al (1996)
showed that the comparative effectiveness of dyadic versus individual protocols
for computer-based training is moderated by trainees’ level of interaction anxiety,
with only low interaction anxiety trainees benefiting from dyadic protocols (see
also Arthur et al 1997). Collaborative protocols have also been shown to reduce
required instructor time and resources by half (Shebilske et al 1992), and to pro-
vide observational learning opportunities that compensate for hands-on practice
efficiently and effectively, as predicted by social learning theory (Shebilske et al
1998).
Researchers have also studied the conditions of practice as they relate to learn-
ing. Forexample,Goettletal(1996)comparedanalternatingtaskmoduleprotocol,
which alternated sessions on video game–like tasks and algebra word problems,
with a massed protocol, which blocked sessions on the tasks. The findings showed
that alternating task modules provided an advantage in learning and retention in
both the video games and algebra word problems.
Along these lines, Bjork and colleagues (Schmidt & Bjork 1992, Ghodsian
etal 1997) haveprovided an interesting reconsideration offindings regardingprac-
tice schedules. These authors argued that introducing difficulties for the learner
during practice will enhance transfer (but not necessarily immediate posttraining
performance). By reconceptualizing interpretation of data from several studies,
Schmidt & Bjork (1992) provided a compelling case for a new approach to arrang-
ing practice. This approach includes introducing variation in the way tasks are
orderedforpractice,inthenatureandschedulingoffeedback,andintheversionsof
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the task to be practiced, and also by providing less frequent feedback. In all cases,
the authors argue that even though acquisition (i.e. immediate) performance may
be decreased, retention and generalization are enhanced owing to additional—
and most likely deeper—information processing requirements during practice.
Shute&Gawlick(1995)supported thisconclusioninan investigationofcomputer-
based training for flight engineering knowledge and skill.
In other work, Driskell and colleagues (Driskell & Johnston 1998, Johnston &
Cannon-Bowers 1996; JE Driskell, E Salas, JH Johnston, submitted) have inves-
tigated the use of stress-exposure training (SET) as a means to prepare trainees
to work in high stress environments. SET, which is based on clinical research
into stress inoculation, has several phases. In the first phase, trainees are provided
with preparatory information that includes a description of which stressors are
likely to be encountered in the environment and what the likely impact of those
stressors on the trainee will be. The second phase—skill acquisition—focuses on
behavioral- and cognitive-skills training designed to help trainees cope with the
stress. In the final phase, application and practice of learned skills is conducted
under conditions that gradually approximate the stress environment. Results of
investigations of this protocol have indicated that SET is successful in reducing
trainees’subjective perception of stress, whileimprovingperformance. Moreover,
the effects of SET generalized to novel stressors and tasks.
Learning Technologies and Distance Training
There is no doubt that technology is shaping how training is delivered in organiza-
tions. While still relying heavily on classroom training, organizations have begun
to explore technologies such as video conferencing, electronic performance sup-
portsystems, videodiscs,andon-lineInternet /Intranetcourses.Indeed,Web-based
training may make “going-to” training obsolete. It is being applied in education,
industry, andthe militaryatanalarming rate(thesedays,onecanget aPhDthrough
the Web). What is probably more alarming is that this implementation is happen-
ing without much reliance on the science of training. Many issues about how
to design distance learning systems remain open. Theoretically-based research
is needed to uncover principles and guidelines that can aid instructional design-
ers in building sound distance training. A few have begun to scratch the surface
(e.g. Schreiber & Berge 1998) of this topic, but a science of distance learning and
training needs to evolve. Specifically, it must be determined what level of inter-
action is needed between trainees and instructors. Moreover, the nature of such
interaction must be specified. For example, do instructors need to see trainees in
order to conduct effective instruction? Do trainees need to see instructors or is it
better for them to view other material? What is the best mechanism for address-
ing trainee questions (e.g. through chat rooms or e-mail)? Should learners have
control over the pace and nature of instruction [some evidence from studies of
computer-based training support the use of learner control (see Shute et al 1998),
but the extent of its benefits for distance learning is not known]? These and
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other questions must be addressed as a basis to develop sound distance-training
systems.
Advances in technology are also enabling the development of intelligent tu-
toring systems that have the potential to reduce or eliminate the need for human
instructors for certain types of learning tasks. Early indications are that intelli-
gent software can be programmed to successfully monitor, assess, diagnose, and
remediate performance in tasks such as computer programming and solving alge-
bra problems (e.g. see Anderson et al 1995). As this technology becomes more
widely available (and less costly to develop), it may provide organizations with a
viable alternative to traditional computer-based or classroom training.
Simulation-Based Training and Games
Simulation continues to be a popular method for delivery training. Simulators are
widely used in business, education, and the military (Jacobs & Dempsey 1993).
In fact, the military and the commercial aviation industry are probably the biggest
investors in simulation-based training. These simulations range in cost, fidelity,
and functionality. Many simulation systems (including simulators and virtual en-
vironments) have the ability to mimic detailed terrain, equipment failures, motion,
vibration, and visual cues about a situation. Others are less sophisticated and have
less physical fidelity, but represent well the KSAs to be trained (e.g. Jentsch &
Bowers 1998). A recent trend is to use more of these low-fidelity devices to train
complex skills. There is also more evidence that skills transfer after training
that uses these simulations (e.g. MT Brannick, C Prince, E Salas, unpublished
manuscript; Gopher et al 1994). For example, some researchers are studying the
viability of computer games for training complex tasks. Gopher et al (1994) tested
the transfer of skills from a complex computer game to the flight performance of
cadets in the Israeli Air Force flight school. They arguedthat the context relevance
of the game to flight was based on a skill-oriented task analysis, which used in-
formation provided by contemporary models of the human processing system as
the framework. Flight performance scores of two groups of cadets who received
10 hours of training in the computer game were compared with a matched group
with no game experience. Results showed that the groups with game experience
performed much better in subsequent test flights than did those with no game ex-
perience. Jentsch & Bowers (1998) and Goettl et al (1996) have reported similar
findings.
Precisely why simulation and simulators work is not well known. A fewstudies
have provided preliminary data (e.g. Bell & Waag 1998, Jentsch & Bowers 1998,
Ortiz 1994), but there is a somewhat misleading conclusion that simulation (in and
of itself) leads to learning. Unfortunately, most of the evaluations rely on trainee
reaction data and not on performance or learning data (see Salas et al 1998). More
systematic and rigorous evaluations of large-scale simulations and simulators are
needed. Nonetheless, the use of simulation continues at a rapid pace in medicine,
maintenance, law enforcement, and emergency management settings. However,
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some have noted (e.g. Salas et al 1998) that simulation and simulators are being
used without much consideration of what has been learned about cognition, train-
ing design, or effectiveness. There is a growing need to incorporate the recent
advances in training research into simulation design and practice. Along these
lines, some have argued for an event-based approach to training with simulations
(Cannon-Bowers et al 1998, Oser et al 1999, Fowlkes et al 1998). According
to this perspective, simulation-based training should be developed with training
objectives in mind, and allow for the measurement of training process and out-
comes, and provisions for feedback (both during the exercise and for debriefing
purposes).
In related work, Ricci et al (1995) investigated the use of a computer-based
game to train chemical, biological, and radiological defense procedures. In this
case, the game was not a simulation (as discussed above), but a computer-based
slot machine that presented trainees with questions about the material. Trainees
earned points for correct answers, and received corrective feedback for incorrect
ones. The authors argued that motivation to engage in this type of presentation
(over text-based material) would result in higher learning. Results indicated that
reactions and retention (but not immediate training performance) were higher for
the game condition.
Behavior role modeling is another type of simulation-based training that has
received attention over the years. Recently, Skarlicki & Latham (1997) found
that a training approach that included role-playing, and other elements of behav-
ior modeling, was successful in training organizational citizenship behavior in a
labor union setting. Similarly, Smith-Jentsch et al (1996b) found that a behav-
ior modeling approach emphasizing practice (i.e. role playing) and performance
feedback was superior to a lecture only or lecture with demonstration format for
training assertiveness skills. Also studying assertiveness, Baldwin (1992) found
that behavioral reproduction (i.e. demonstrating assertiveness in a situation that
was similar to the training environment) was best achieved by exposing trainees
only to positive model displays. Conversely, the combination of both positive and
negative model displays was most effective in achieving behavioral generalization
(i.e. applying the skill outside of the training simulation) four weeks later.
Team Training
As noted by Guzzo & Dickson (1996) and Tannenbaum & Yukl (1992), teams are
heavily used in industry, government, and the military. Therefore, these organiza-
tions have invested some resources in developing teams (Tannenbaum 1997). A
number of theoretically-driven team training strategies has emerged. These in-
cludecross-training (Blickensderferetal 1998), teamcoordinationtraining(Prince
& Salas 1993), team leadership training (Tannenbaum et al 1998), team self-
correction (Smith-Jentsch et al 1998), and distributed team training (Dwyer et al
1999). All of these have been tested and evaluated with positive results (see
Cannon-Bowers & Salas 1998a).
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The aviation community has arguably been the biggest advocate and user of
team training (see Helmreich et al 1993). The airlines and the military have ex-
tensively applied a strategy labeled crew resource management (CRM) training.
This strategy has a 20-year history in the aviation environment. It is used as a
tool to improve teamwork in the cockpit but, more importantly, to reduce human
error, accidents, and mishaps (Helmreich & Foushee 1993). CRM training has
gonethroughseveralevolutions(Helmreichetal1999)andismaturing. Systematic
proceduresfordesigninganddevelopingCRMtraininghavebeendeveloped(Salas
et al 1999), and the evaluation data are encouraging (see Leedom & Simon 1995,
Salas et al 1999, Stout et al 1997). CRM training seems to work by changing the
crew’s attitudes toward teamwork, and by imparting the relevant team competen-
cies. Crew that have received CRM training exhibit more teamwork behaviors in
the cockpit (Salas et al 1999, Stout et al 1997). However, more and better eval-
uations are needed. Most of the evaluations conducted have been in simulation
environments. Only recently have evaluations begun to determine the transfer of
this training to the actual cockpit.
Other research in team training has focused on developing strategies to train
specific competencies (see also Cannon-Bowers & Salas 1998b). For example,
Smith-Jentsch et al (1996b) examined determinants of team performance–related
assertiveness in three studies. These studies concluded that, whereas both attitu-
dinally focused and skill-based training improved attitudes toward team member
assertiveness, practiceandfeedbackwereessentialtoproducingbehavioraleffects.
In addition, Volpe et al (1996) used shared mental model theory (Cannon-Bowers
et al 1993) as a basis to examine the effects of cross-training and workload on per-
formance. The results indicated that those who received cross-training were more
effective in teamwork processes, communication, and overall team performance.
In sum, the literature has begun to provide evidencethat team training works. It
workswhen the training is theoretically driven, focused on required competencies,
and designed to provide trainees with realistic opportunities to practice and receive
feedback. Also, guidelines for practitioners have emerged (e.g. Swezey & Salas
1992, Salas & Cannon-Bowers 2000), tools for designing team training strategies
havesurfaced(e.g. Bowersetal1993), anda numberofstrategiesarenowavailable
for team training (e.g. Cannon-Bowers & Salas 1997). In the future, we need
to know more about how to diagnose team cognitions during training. We also
need better and more rigorous measurement protocols to assess shared knowledge.
In addition, we need to understand better the mechanisms by which to conduct
effective distributed team training.
POST-TRAINING CONDITIONS
Events that occur after training are as important as those that occur before and
during training. Therefore, recent research has focused on improving the methods
andprocedures weuseto evaluatetraining, andonexaminingtheeventsthatensure
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transfer and application of newlyacquired KSAs. In examining this body of work,
we get a clear sense that it is in these two areas where we probably have made the
most significant progress. There are theoretical, methodological, empirical, and
practical advances. All the issues have not been solved, but meaningful advances
have been made in the last decade. This is very encouraging. We first look at
training evaluation and then at transfer of training.
Training Evaluation
Kirkpatrick’s Typology and Beyond Kirkpatrick’s typology (Kirkpatrick 1976)
continues to be the most popular framework for guiding evaluations. However,
recent work has either expanded it or pointed out weaknesses, such as the need to
develop more diagnostic measures. For example, Kraiger et al (1993) proposed
a multi-dimensional view of learning, implying that learning refers to changes in
cognitive, affective, and/or skill-based outcomes. The proposed taxonomy can be
used to assess and document learning outcomes. In a meta-analysis of studies em-
ploying Kirkpatrick’s model, Alliger et al (1997) noted that utility-type reaction
measures were more strongly related to learning and performance (transfer) than
affective-typereaction measures. Surprisingly, they also found that utility-type re-
action measures are more predictive of transfer than learning measures. Kraiger &
Jung (1997) suggested several processes by which learning outcomes can be de-
rived from instructional objectives of training. Goldsmith & Kraiger (1997) pro-
posed a method for structural assessment of an individual learner’s knowledge and
skill in a specific domain. This model has been used with some success in several
domains (e.g. Kraiger et al 1995, Stout et al 1997).
Clearly, Kirkpatrick’s typology has served as a good foundation for training
evaluation for many decades (Kirkpatrick 1976). It has been used, criticized, mis-
used, expanded, refined, adapted, and extended. It has served the training research
community well—but a richer, more sophisticated typology is needed. Research
needs to continue finding better, more diagnostic and rigorous assessments of
learning outcomes. The next frontier and greatest challenge in this area is in de-
signing, developing, and testing on-line assessments of learning and performance.
As we rely more on technology for training delivery, we need better and more
protocols to access learning not only after but also during training (e.g. Ghodsian
et al 1997).
Evaluation Design Issues Training evaluation is one of those activities that is
easier said than done. Training evaluation is labor intensive, costly, political, and
many times is the bearer of bad news. We also know that it is very difficult to
conduct credible and defensible evaluations in the field. Fortunately, training re-
searchers have derived and tested thoughtful, innovative and practical approaches
to aid the evaluation process. For example, Sackett & Mullen (1993) proposed
other alternatives (e.g. posttesting-only, no control group) to formal experimental
designswhenanswering evaluation questions. They suggested that those questions
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(e.g.Howmuchchangehasoccurred? Whattargetperformancehasbeenreached?)
should drive the evaluation mechanisms needed, and that each requires different
designs. Haccoun & Hamtiaux (1994) proposed a simple procedure for estimating
effectivenessoftrainingin improving trainee knowledge—theinternalreferencing
strategy. This situation tests the implicit training evaluation notion that training-
relevant content should show more change (pre-post) than training-irrelevant con-
tent. An empirical evaluation using internal referencing strategy versus a more
traditional experimental evaluation indicated that the internal referencing strategy
approach might permit inferences that mirror those obtained by the more complex
designs.
The costs of training evaluation have also been addressed recently. Yang et al
(1996) examined two ways to reduce costs. The first method is by assigning dif-
ferent numbers of subjects into training and control groups. An unequal group
size design with a larger total sample size may achieve the same level of statistical
power at lower cost. In a second method, the authors examined substituting a less
expensive proxy criterion measure in place of the target criterion when evaluat-
ing the training effectiveness. Using a proxy increases the sample size needed to
achieve a given level of statistical power. The authors described procedures for
examining the tradeoff between the costs saved by using the less expensive proxy
criterion and the costs incurred by the larger sample size. Similar suggestions have
been made by Arvey et al (1992).
Evaluations It is refreshing to see thatmore evaluations are being reported in the
literature; we hope this trend continues. It is only by drawing lessons learned from
past evaluations that the design and delivery of training will continue to progress.
Several field training evaluations have been reported in team training settings
(e.g. Leedom & Simon 1995, Salas et al 1999), sales training (e.g. Morrow et al
1997), stress training (e.g. Friedland & Keinan 1992), cross-cultural management
training (e.g. Harrison 1992), transformational leadership training (e.g. Barling
etal1996), careerself-managementtraining(e.g. Kosseketal1998),workforcedi-
versity training (e.g. Hanover & Cellar 1998) and approaches tocomputer training
(e.g. Simon & Werner 1996). All suggest that training works. However, an exam-
ination of evaluations where training did not work is also needed (and we suspect
there are many). Some important lessons can be learned from these types of
evaluations as well.
Transfer of Training
Transfer of training is conceptualized as the extent to which KSAs acquired in a
training program are applied, generalized, and maintained over some time in the
job environment (Baldwin & Ford 1988). There has been a plethora of research
and thinking in the transfer of training area (see Ford & Weissbein 1997). This
emerging body of knowledge suggests a number of important propositions and
conclusions. For example, (a) the organizational learning environment can be reli-
ably measured and varies in meaningful ways across organizations (Tannenbaum
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THE SCIENCE OF TRAINING 489
1997); (b) the context matters (Quinones 1997)—it sets motivations, expectations,
and attitudes for transfer; (c) the transfer “climate” can have a powerful impact on
the extent to which newly acquired KSAs are used back on the job (e.g. Tracey
et al 1995, Thayer & Teachout 1995); (d) trainees need an opportunity to perform
(Ford et al 1992, Quinones et al 1995); (e) delays between training and actual
use on the job create significant skill decay (Arthur et al 1998); (f ) situational
cues and consequences predict the extent to which transfer occurs (Rouiller &
Goldstein 1993); (g) social, peer, subordinate, and supervisor support all play a
central role in transfer (e.g. Facteau et al 1995, Tracey et al 1995); (h) training can
generalize from one context to another (e.g. Tesluk et al 1995); (i) intervention
strategiescanbedesignedtoimprovetheprobabilityoftransfer(e.g. Brinkerhoff&
Montesino 1995, Kraiger et al 1995); (j) team leaders can shape the degree
of transfer through informal reinforcement (or punishment) of transfer activities
(Smith-Jentsch et al 2000); (k) training transfer needs to be conceptualized as a
multidimensionalconstruct—itdiffers depending onthetypeoftrainingand close-
ness of supervision on the job (Yelon & Ford 1999).
As noted by Ford & Weissbein (1997), much progress has been made in this
area. Therearemorestudiesusingcomplextaskswithdiversesamplesthatactually
measure transfer overtime. However, much more is needed. Specifically, weneed
more studies thatactually manipulate thetransfer climate (e.g. Smith-Jentsch et al
2000). The measurement problems remain. Most studies still use surveys as the
preferred method for measuring transfer. Other methods need to be developedand
used. Finally, we need to assume that learning outcomesattheindividuallevelwill
emerge to influence higher level outcomes. Vertical transfer of training is the next
frontier. Vertical transfer may be a leverage point for strengthening the links bet-
ween learning outcomes and organizational effectiveness (see Kozlowski et al
2000).
Takentogether, these studies validatethe importance of the organizationalenvi-
ronment in training. In the future we need to continue to determine which factors
affect transfer so that we can maximize it.
FINAL OBSERVATIONS, CONCLUSIONS,
AND THE FUTURE
In closing, we draw on the extensive literature just reviewed to offer the
following observations:
1. As Tannenbaum & Yukl (1992) predicted, the quality and quantity of
research has increased. We truly have seen an explosion of theoretical,
methodological, and empirical work in training research, and we do not see
an end to this trend. This is very encouraging, and we believe this body of
work will pay off as we learn more about how to design and deliver
training systems. Therefore, we contend that training research is here to
stay and prosper.
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2. The progress in theoretical development, especially the attention given to
cognitive and organizational concepts, is revolutionizing the field. These
new developments promise to change how we conceptualize, design,
implement, and institutionalize learning and training in organizations. In
the future, we will need a deeper understanding of these concepts, we must
strive for more precision and clarity of constructs, and our methods must
be more rigorous.
3. The body of literature generated over the past decade suggests that the
field does not belong to any single discipline anymore. In the past,
industrial/organizational and educational psychologists primarily
conducted training research. A closer look at the literature now suggests
that cognitive, military, engineering, human factors, and instructional
psychologists are involved in training research to an equal degree. In fact,
computer scientists and industrial engineers are also researching learning,
training technology, and training systems. As many others have observed,
we need more cross-fertilization, collaboration, and dialogue among
disciplines. To start, we need to read each other’s work, and leverage each
other’s findings, ideas, and principles.
4. Technology has influenced—and will continue to do so for the foreseeable
future—the design and delivery of training systems. Whether we like it or
not, technology has been embraced in industrial, educational, and military
institutions as a way to educate and train their workforces. Technology
may, or may not, have instructional features because it is often employed
without the benefit of findings from the science of training. However, as
we learn more about intelligent tutoring systems, modeling and simulation,
multimedia systems, learning agents, Web-based training, distance
learning, and virtual environments, this state of affairs may change. It is
encouraging that basic and applied research is currently going on to
uncover how these technologies enhance learning and human performance
(e.g. Cannon-Bowers et al 1998). More research is needed, and the
prospects of it happening are very promising. Specifically we need to
know more about how to best present knowledge over the Internet, how
and when to provide feedback, which instructional strategies are best for
Web-based applications, what role instructors and trainees play in these
modern systems, and how effectiveness can best be evaluated.
5. The distinction between training effectiveness and training evaluation is
much clearer. Kraiger et al (1993) provided the seed for this important
distinction. Training effectiveness is concerned with why training works
and it is much more “macro” in nature. That is, training effectiveness
research looks at the training intervention from a systems
perspective—where the success of training depends not only on the method
used but on how training (and learning) is positioned, supported, and
reinforced by the organization; the motivation and focus of trainees; and
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what mechanisms are in place to ensure the transfer of the newly acquired
KSAs to the job. Training evaluation on the other hand, examines what
works and is much more “micro” (i.e. focused on measurement). It looks
at what was learned at different levels and is the basis for determining the
training effectiveness of a particular intervention. This distinction has
made some significant contributions to practice possible and, more
importantly, is helping avoid the simplistic view of training (i.e. that
training is just a program or curriculum rather than the complex interaction
of many organizational factors). More research aimed at uncovering why
training works is, of course, desirable.
6. Much more attention has been given to discussing training as a system
embedded in an organizational context (e.g. Dipboye 1997, Kozlowski &
Salas 1997, Kozlowski et al 2000, Tannenbaum & Yukl 1992). This is
refreshing and welcome. For many decades, training researchers have
ignored the fact that training cannot be isolated from the system it
supports. In fact, the organizational context matters (e.g. Quinones 1997,
Rouillier & Goldstein 1993) and matters in a significant way. Research
aimed at studying how organizations implement training and why even the
best-designed training systems can fail is encouraged.
7. Research has begun to impact practice in a more meaningful, and it is to be
hoped, quantifiable way. We can offer principles and guidelines to organi-
zations regarding how to analyze, design, develop, implement, and evaluate
training functions. Much needs to be done, but it is only through mutual
reciprocity—scienceandpractice—thatrealprogresswillbemade(seeSalas
et al 1997). As already stated, we have seen more evaluations conducted,
there are more guidelines for designers and practitioners, and viable
strategies that seem to impact organizational outcomes and the link between
learningandperformancearemoretangibletoday(Bassi&VanBuren1999).
A number of training issues need considerable attention in the next few years
(in addition to the ones we have noted throughout this chapter). In particular, we
need research that helps us get a better understanding of what, how, and when on-
the-job training works. On-the-job training is a common practice in organizations,
butfewprinciples and guidelines exist on how to optimize this strategy. We need a
deeper understanding of how to build expertise and adaptability through training.
Although some work has started (e.g. Kozlowski 1998, Smith et al 1997), longi-
tudinal studies in the field are desirable. How learning environments are created
and maintained in organizations needs to be researched and better understood. A
related issue is how, and under what circumstances, individuals and teams learn
from informal organizational activities. In addition, as organizations become older
and more diverse, more attention must be paid to the special training needs of
nontraditional workers (especially given the anticipated reliance on high-tech
systems). Moreover, as organizations allow more flexibility in how work is
accomplished (e.g. telecommuting), training practices must keep pace. In fact,
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organizations will increasingly depend on workers who can develop, maintain,
and manage their own skills, requiring attention to the challenge of how to de-
velop and attract self-directed learners. Finally, as noted, training researchers
need to embrace and investigate new technologies. We know that organizations
will; we hope that new developments in training are driven by scientific findings
rather than the band wagon.
In conclusion, we are happy to report that, contrary to charges made by our
predecessors over the years, training research is no longer atheoretical, irrelevant,
or dull. Exciting advances in all areas of the training enterprise have been real-
ized. Training research has also been called faddish, a characteristic we hope is
beginning to fade as well. However, we wonder whether there is compelling evi-
dence to suggest that training practitioners in organizations are actually applying
what has been learned from the research. This brings us back to a question we
raised at the beginning of this chapter; namely, to what degree does the science
of training affect organizational training practices? In other words, can we find
evidencethat organizationsareimplementing the lessons being learned from train-
ing research (especially the work reviewed here), or are practitioners still prone
to latch on to the latest training craze? The answer is, quite simply, that we just
do not know. This is due, at least in part, to the fact that detailed records doc-
umenting training practices (and more importantly, the rationale that went into
developing them) are not typically available. However, one thing seems clear:
The stage for the application of training research is set. We say this because, as
noted, organizations are beginning to question the value-added of human resource
activities (including training), and to pay more attention to human capital. Sim-
ply put, organizations want to know what the return is on their training inves-
tment.
Assuming this trend continues, it should force training professionals to turn to
thescienceoftrainingforempiricallyverifiedguidelinesregardinghowtooptimize
training outcomes (including transfer), and how to evaluate whether training has
been effective in reaching organizational goals. As the pressure grows to show
an impact on the bottom line, training practitioners will do well to employ sound
principles,guidelines,specifications, andlessonslearnedfromthe literature, rather
than relying on a trial-and-error approach. For this reason, webelieve a new era of
training has begun—one in which a truly reciprocal relationship between training
research and practice will be realized.
ACKNOWLEDGMENTS
We thank Clint Bowers, Ken Brown, Irv Goldstein, Steve Kozlowski, Kevin Ford,
John Mathieu, Ray Noe, Paul Thayer, Scott Tannenbaum, and Will Wooten for
their valuable comments and suggestions on earlier drafts of this chapter. We
were aided in the literature review by two doctoral students in the Human Factors
program from the University of Central Florida: Katherine Wilson and Shatha
Samman.
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THE SCIENCE OF TRAINING 493
Visit the Annual Reviews home page at www.AnnualReviews.org
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... Emulating from a role model is an important aspect in interpersonal skills development and training. More generally, effective training is composed of four phases, namely information about the desired behaviours to be learnt, demonstration of the desired behaviours by a role model, trainees' practice of the desired behaviours, and feedback to the trainees related to the practice phase [2,3]. In the present research, we focus on the effect of role models for learning and training. ...
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