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Learning is the engagement in mental processes resulting in the acquisition and retention of knowledge, skills, and/or affect over time and applied when needed. Building on this definition, we integrate the science of training and the science of learning to propose a new science of workplace instruction, linking the design of instructional events to instructional outcomes such as transfer and job performance through the mediating effects of learner events and learning outcomes. We propose three foundational elements: the learner, instructional principles, and training delivery (methods and media). Understanding and applying instructional principles are the primary methods for enhancing training effectiveness; thus, we detail 15 empirically supported principles. We then discuss the erroneous pursuit of aptitude-by-treatment interactions under the guise of learner styles and age-specific instruction. Finally, we offer suggestions for future research that draw on the foundation of instructional principles to optimize self-directed learning and learning in synthetic learning environments. Expected final online publication date for the Annual Review of Organizational Pscyhology and Organizational Behavior, Volume 8 is January 21, 2021. Please see for revised estimates.
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Annual Review of Organizational Psychology and
Organizational Behavior
The Science of Workplace
Instruction: Learning and
Development Applied to Work
Kurt Kraiger1and J. Kevin Ford2
1Department of Management, University of Memphis, Memphis, Tennessee 38152, USA;
2Department of Psychology, Michigan State University, East Lansing, Michigan 48824, USA;
Annu. Rev. Organ. Psychol. Organ. Behav. 2021.
The Annual Review of Organizational Psychology and
Organizational Behavior is online at
Copyright © 2021 by Annual Reviews.
All rights reserved
training and development, learning, instruction, training effectiveness,
instructional principles
Learning is the engagement in mental processes resulting in the acquisition
and retention of knowledge, skills, and/or affect over time and applied when
needed. Building on this denition, we integrate the science of training and
the science of learning to propose a new science of workplace instruction,
linking the design of instructional events to instructional outcomes such
as transfer and job performance through the mediating effects of learner
events and learning outcomes. We propose three foundational elements:
the learner, instructional principles, and training delivery (methods and me-
dia). Understanding and applying instructional principles are the primary
methods for enhancing training effectiveness; thus, we detail 15 empirically
supported principles. We then discuss the erroneous pursuit of aptitude-by-
treatment interactions under the guise of learner styles and age-specic in-
struction. Finally, we offer suggestions for future research that draw on the
foundation of instructional principles to optimize self-directed learning and
learning in synthetic learning environments.
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Workplace training is a systematic approach to learning and development to improve individ-
ual, team, and organizational effectiveness.Industrial and organizational (I-O) psychologists have
played various roles relevant to improving the quality and effectiveness of training, including re-
search on learning and transfer, development of training evaluation measures, enhancement of
methods for training design and delivery, and the positioning of the training function within or-
ganizations. Research has focused on theoretical perspectives of what is meant by learning (Kraiger
et al. 1993) and transfer (Baldwin & Ford 1988) as well as investigating factors that affect learning
and the transfer of training to the job (Ford & Kraiger 1995).
Training research as a reection of existing training practices and a stimulus for innovation has
undergone three major cycles in the past century (Bell et al. 2017). First, between approximately
1920 and 1950, research tested and developed theories of learning and skill acquisition. Second,
research between approximately 1950 and 1980 focused on training methods and institutionaliz-
ing training events within a larger organizational context, e.g., development of methods for needs
assessment to determine training needs and evaluation practices to demonstrate training impact
(Kraiger & Ford 2007). As such, the research at that time was described as “nonempirical, non-
theoretical, poorly written, and dull” as well as “fadish (sic) to an extreme” (Campbell 1971). With
regard to the third cycle, researchers went both narrower and broader in focus: The application of
cognitive science to understand changes in the learner (e.g., Ford & Kraiger 1995, Kraiger et al.
1993) and broader systems perspectives to understand organizational inuences on training effec-
tiveness (e.g., Colquitt et al. 2000) fueled several decades of interest and activity. At the individual
level, Kraiger et al. (1993) claried that learning occurs not only when trainees can do something
they were not able to do before, but also when there are changes in affective and cognitive states
as well. This signicantly impacted how training researchers have evaluated training (Aguinis &
Kraiger 2009, Salas et al. 2012). At a systems level, Colquitt et al. (2000) and others tested models
of training effectiveness identifying how individual and system-level inuences affect the extent to
which knowledge and skills are learned, retained, and transferred to appropriate situations. Salas
& Cannon-Bowers (2001) dubbed the term the science of training, characterizing it as an “exciting
and dynamic eld,” and challenged the eld to nd new ways to inuence practice via theory and
best practiceoriented research.
Twenty years after that declaration—anecdotally and based on observations of manuscripts we
(the authors) review, conferences we attend, as well as the content of scientic and practitioner
journals—there has been a diminishing interest in learning and development within I-O psychol-
ogy.At the same time, basic research on learning in cognitive science and educational psychology
continue to evolve (e.g., Cotton 1976, Glaser & Bassok 1989) and have expanded rapidly over
the past twenty years (e.g., Mayer 2019). Although training research has beneted greatly from
individual (learning) and organizational (systems) perspectives, training as an applied science is
in danger of drifting from both its roots in learning theory and its potential for impacting the
building up of human capital in organizations.
Just as there was value in clarifying what is learned (Kraiger et al. 1993), there is value today
in focusing on how we learn and applying this knowledge to optimize learning. The purpose of
this article is to direct attention to how individuals learn in order to organize what we know and
need to know about maximizing training effectiveness. We link the science of learning to the
science of training to identify and more fully understand the factors affecting learning outcomes.
In particular, the science of workplace instruction is the application of evidence-based principles
that have been found to help individuals learn knowledge, skills, and attitudes that impact job
performance and organizational effectiveness. We extend the “science of instruction” found in
. Kraiger Ford
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educational contexts (e.g., Mayer 2011a, 2019) to the workplace to account for the complexities
introduced by different training content (e.g., greater focus on skills and task completion) and
greater variability in learning contexts (e.g., easy versus difcult sales clients).
The article is organized into three parts. In the rst section, we provide a framework of possible
areas of inquiry with respect to learning and training. In the second section, we describe the core
elements of the science of workplace instruction, emphasizing instructional principles, as mecha-
nisms for improving training practice and stimulating training research. In the third section, we
explore the advancement of the science of workplace instruction by examining the intersection of
instructional principles with training delivery and discussing two emerging trends in training—
self-directed learning and synthetic learning environments.
To understand how effective instruction promotes learning in participants, we rst must be clear
on what we mean by learning. Ford (2021) recently presented and compared many popular deni-
tions of learning from both the cognitive science and training domains. There were three common
characteristics across most denitions: (a) change in knowledge, skill, and/or affect; (b) relative per-
manency of the change; and (c) that it is inferred from observed changes in the learner.These core
characteristics highlight that learning in an organizational context must be at some level inten-
tional and lasting.
So, what is learning? One helpful denition is from Quinn (2018), who dened learning as the
retention (of knowledge, skills, and affect) over time until needed and transferred to appropriate
situations. The elaboration in parentheses is ours and reects the views of educational scholars
(e.g., Anderson et al. 2001) and the current authors (see also Kraiger 2002, Kraiger et al. 1993)
that learning is multidimensional. Building from this, we dene learning as engagement in men-
tal processes—learning events—that result in the acquisition and retention of knowledge, skills,
and/or affect over time and until needed, along with the capacity to identify conditions of perfor-
mance and respond appropriately. More colloquially, learning is an increased capacity to do the
right thing at the right time.
Instructional Outcomes
Figure 1 presents an organizing framework to guide our discussion of training system components
relevant to learning. Instructional (or training) outcomes are observable and measurable criteria
that occur as a result of learning. These outcomes are the foci of most training reviews over the
past 50 years. Prior reviews have concentrated on whether, when, and why training transfers to the
job (Ford et al. 2018), individual performance improves (Aguinis & Kraiger 2009, Salas et al. 2012),
and organizational effectiveness increases (Aguinis & Kraiger 2009). The main question addressed
in these reviews is whether (or which) instructional events lead to instructional outcomes of value
to learners or the organization.
System Components
The lower half of Figure 1 shows dynamic changes in the learner as a result of instructional events.
Learning events and learning outcomes mediate the relationship between instructional events and
instructional outcomes but receive only sporadic attention from training researchers.
Learning outcomes. Learning outcomes are constructs that change as a result of learning events.
Kraiger et al. (1993) examined learning taxonomies from educational and cognitive science The Science of Workplace Instruction .
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Instructional events:
• Principles
• Methods
• Media
Instructional outcomes:
• Transfer
• Performance
• Eectiveness
Learning events:
• Encoding
• Organizing
• Retrieving
Learning outcomes:
• Aective
• Knowledge
• Skills
Figure 1
Organizing framework linking instructional events and learning events to learner outcomes and instructional
disciplines (e.g., Bloom 1994) and developed a conceptually based classication scheme of learn-
ing outcomes that included three major learning outcome categories: cognitive, skill-based, and
affective. Cognitive learning outcomes included verbal knowledge, knowledge organization, and
cognitive strategies. Skill-based outcomes included issues of compilation and automaticity. Affec-
tive outcomes included issues of attitude change and motivational shifts in terms of mastery goals,
self-efcacy, and goal direction. Although not addressed in this article, training evaluation is the
practice of assessing the achievement of learning outcomes (Kraiger 2002) and using that data to
drive decisions that improve the learner and the training system (Surface & Kraiger 2018).
Instructional events. Figure 1 distinguishes between instructional events and learning events
but links the two. Instructional events are observable and typically initiated by the organization
to trigger learning events within individuals. These events include training strategies to build in-
dividual capabilities from novice to expert, develop team members to enhance team effectiveness
by developing team knowledge and skills, and facilitate the progression of leadership excellence.
There are a variety of other forms of instruction that take place outside of formal training, such
as informal eld-based learning (Wolfson et al. 2018), self-directed learning (Clardy 2000), men-
toring (Kraiger et al. 2019), and coaching (Grifths & Campbell 2009). We focus primarily on
formal instructional events but recognize that the same components of instructional events that
drive learning in formal, structured environments will be effective in these less structured envi-
ronments as well.
Learning events. Learning events refer to individual actions of encoding, organizing, and re-
trieving new content presented from instructional events. Learning is what we do when presented
situations and content that are outside our current states of affect, knowledge, and skill capac-
ities. Learning can be incidental but largely results from instructional events. Multiple theories
of learning center on the processes by which learners capture environmental stimuli, act upon it,
then store it in ways that make it accessible for later application. Mayer’s (2008) cognitive theory
of multimedia learning describes how learners actively coordinate and monitor selecting relevant
. Kraiger Ford
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words and images from a multimedia message, building connections among them to create co-
herent personally meaningful models, and then integrating those models with prior knowledge to
facilitate storage and recall. More generally, learning from instruction requires cognitive processes
of encoding, organizing, and retrieving, as well as the importance of building connections in an
active, intentional way among these processes.
Encoding is the process by which learners select content into working memory. We are con-
stantly encoding and making conscious and unconscious decisions of what to attend to and what to
let pass. Organizing occurs during consolidation and is the process by which learners build person-
ally meaningful representations of training content. Examples include building task sequences—
the steps necessary to generate a budget report online—and if-then responses—such as “if my
direct report gets defensive when I am giving feedback, then I respond by ___.” Finally, retrieval
is the act of successfully recalling and applying acquired knowledge, skill, and affect when needed.
Retrieval also helps strengthen the connections in memory around what has been learned.
Advancing Training Research and Practice
The science of training has largely focused on how effective training is and the individual and
organizational factors that predict or moderate training effectiveness (see Figure 1, upper right).
The science of learning focuses principally on the upper left corner: What are the most effective
instructional events given what we know about how people learn? The proposed science of work-
place instruction considers this framework as a whole—linking instructional events to meaningful
outcomes through an understanding of learning events in people and desired learning outcomes
of organizations.
To advance the science of workplace instruction and reinvigorate training research, we need
a better understanding of how instructional events affect outcomes through learning events. To
advance training practice, we need greater guidance on implementing instructional events in ways
that affect learning events, improve learning outcomes, and lead to lasting positive instructional
How Effective Instruction Works
Referring to educational contexts, Herb Simon stated “Learning results from what the student
does and thinks and only from what the student does and thinks. The teacher can advance learn-
ing only by inuencing what the student does to learn” (quoted in Ambrose et al. 2010). This
statement highlights the appropriate focus on what a learner does to learn and how to build ef-
fective workplace instruction to better facilitate the process of learning.
In general, effective workplace instruction facilitates encoding by improving learner engage-
ment, directing attention to key material, and drawing connections between new content and what
the learners already know or need to know to perform their jobs. It facilitates organization by pro-
viding an overarching structure to the content, providing sufcient time for organization and con-
solidation to occur, and helping learners understand connections between content elements and
between those elements and the work context. Retrieval processes occur during and after training.
In particular,effective instruction facilitates retention and retrieval by having learners practice re-
trieval during learning, ensuring that the content is well ingrained (overlearning), incorporating
physical, functional, psychological, and social delity between training and performance environ-
ments, and preparing learners to generalize or adapt newly acquired knowledge or skills to novel
contexts. In the next section, we describe steps toward building a science of workplace instruction
around instructional events and learning events. The Science of Workplace Instruction .
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Learners ×
Principles ×
Learners ×
Figure 2
Primary elements of the science of workplace instruction: learners, instructional principles, and delivery.
Figure 2 shows the three primary elements of the science of workplace instruction: the learner,
instructional principles, and instructional delivery. As the goal of instruction is to facilitate change
in the learner, research and practice should be considered from this perspective. In this section,
we clarify the role of the learner, present core instructional principles for workplace instruction,
and differentiate delivery methods and media from principles. This discussion sets the standard
for understanding the intersections among learners, principles, and delivery—intersections we
believe should be at the forefront of future training research.
The Learner
Learning occurs when individuals retain new knowledge, skills, and affect over time and apply
these changes to appropriate situations. Learners are directly referenced as a foundational element
of the science of workplace instruction in Figure 2 and are implicit in Figure 1 within learning
events (how change occurs) and learning outcomes (what change looks like). Because the science
of workplace instruction builds on both the science of learning and the science of training, it is
instructive to understand how individuals—and the concept of individual variability—are treated
in these paradigms.
With its roots in experimental psychology, the science of learning progresses by ignoring or
controlling for individual differences. Individual variability contributes to within-cell variability,
such that main effects for,say, an instructional principle can be assumed to generalize across learn-
ers. In other words, empirically supported instructional principles are effective for most learners.
With its roots in applied psychology, the science of training seeks to identify individual difference
variables, e.g., motivation to learn or goal orientation, that are predictors or moderators of the
learning during training or transfer after it. This paradigm leads to empirically supported conclu-
sions about the relative importance of these individual factors but provides little guidance as to
how to use this knowledge to build better training.
To the extent that knowledge of individual variability matters in applied contexts, it must trigger
the design of interventions to reduce pretraining variability among learners. For example, the
effects of how training is framed can inuence trainee attitudes (e.g., Hicks & Klimoski 1987)
. Kraiger Ford
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by maintaining the status quo in learners who already have positive attitudes about training and
improving attitudes (e.g., motivation-to-learn) in trainees with initially lower attitudinal levels
(e.g., Cox & Beier 2009).
By designating the learner as a primary element in the science of workplace instruction, we not
only establish the learner is the point of instruction but also highlight the importance of examining
how variability among learners (during learner events) interacts with instructional principles and
methods. Although there will be variance in cognitive ability, job knowledge, and trainee motiva-
tion, the science of learning reveals that learning events are relatively intransient across learners
and are facilitated by the same set of empirically supported instructional principles.
Instructional Principles
The science of workplace instruction is the application of evidence-based principles that have
been found to help individuals learn knowledge, skills, and attitudes related to job performance
and organizational effectiveness. Instructional principles are empirically supported propositions
that guide the design and delivery of effective training. Instructional principles can affect both
instructional events (how training is structured and designed) and learning events (how learn-
ers interact with material) and lead to learning particular outcomes. The search for generalizable
principles of learning has a long history in psychology, for example Thorndike & Woodworth
(1901) advocated for the use of identical elements to improve transfer of learning. I-O psychol-
ogists and instructional psychologists were instrumental in developing foundational instructional
principles in the 1950s and 1960s, typically in the quest to improve efciency in and transfer of
military training. These principles included recommendations for increasing task difculty (e.g.,
Briggs & Naylor 1962), stimulus variability (e.g., Ellis 1965), and distributed practice (e.g., Briggs
& Naylor 1962). More recently, the identication and validation of instructional principles have
been conducted primarily by cognitive scientists and educational psychologists with the intent of
improving formal education (e.g., Dunlosky et al. 2013, Halpern et al. 2007, Mayer 2008, National
Research Council 2012), with minimal attention to workplace training (but see Plott et al. 2014).
Not all empirically supported principles are useful for the science of workplace instruction.
A useful principle must be actionable, resulting in instructional design or learning events that
result in knowledge/acquisition and retention. Statements (offered as principles) such as “prior
knowledge can help or hinder learning” may be true but are not prescriptive. Other principles
may be too narrowly focused on primary or secondary education. Because we believe instructional
principles are the bedrock of effective workplace instruction, we provide a brief summary of those
that are both actionable and the most relevant to adult learners and workplace training. We return
to these principles later in the article when we talk about their relationship to learners and training
Core instructional principles. Table 1 shows ve core instructional principles, each with two
to three specic principles or instructional strategies. A core principle is an empirically supported
approach to facilitating learning that can be accomplished in multiple ways, including the specic
principles nested under each. To conserve space, those principles are dened in the table along
with examples and citations to key explanatory texts and/or reviews providing empirical evidence
on effectiveness.
For each specic principle or instructional strategy, Table 1 provides the following: a def-
inition, one or more examples (with citations when helpful), references of primary sources for
more in-depth understanding, and one or more key ndings. A denitive review of each principle
or strategy is beyond the scope of this review. However, the table directs researchers or training The Science of Workplace Instruction .
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Tabl e 1 Empirically supported instructional principles
Principles Characteristics Examples Sources Key ndings
1. Organize content
a. Coherence Ensure there is a
representation of the
essential ideas to be learned.
Delete interesting but nonessential
Design course materials such that main
points are prominent and easy to
Halpern et al.
2007, Moreno
Mayer & Fiorella (2014) found a median
effect size of 0.86 across 23 studies for
concise presentation of material.
b. Contiguity Present knowledge, principles,
and ideas that are closely
associated in space and time.
Explain a learning event close in time
to when it is depicted.
Place onscreen text near corresponding
graphics (e.g., illustrations placed
next to the paragraph describing it)
and put words from the paragraph in
the illustration.
Halpern et al.
2007, Mayer
A meta-analysis of 29 studies showed a mean
d=0.80 for effects of contiguity, with a
similar effect for spatial and temporal
contiguity (Ginns 2006).
Mayer & Fiorella (2014) found a median
effect size of 1.10 across 22 studies.
c. Advanced
Present introductory
(pretraining) material that
provides the learner with a
structure, schema, or example
of what is to be covered in
Short pretraining video demonstrating
behavior to be trained.
Cues in the form of outlines, diagrams,
and concept maps.
Mayer 1979,
Moore et al.
Preiss & Gayle (2006) reported a mean d=
0.46 (K=20, N=1,937) for providing
advanced organizers.
Stone (1983) reported a mean d=0.66 (K=
112), with larger effect sizes for written or
illustrated advanced organizers and with
advanced organizers that bridge with
material to be learned.
2. Optimize sequencing of material
a. Scaffolding Provide learner assistance in
the early phases of
instruction to help focus
attention; start with the
simple aspects of the task and
then build in task complexity.
Expert advice and support that fades
over time.
Design into the plan of instruction
appropriate support mechanisms by
anticipating learner needs.
Metcalfe &
Kornell 2005,
Reiser 2004
Meta-analytic results showed a large (60%)
benet for scaffolding (TR =1.58,
g=0.46) (Plott et al. 2014).
Another meta-analysis revealed mean effects
of g=0.46, 0.54, and 0.63 for concept,
principles, and application outcomes,
respectively (Belland et al. 2015).
. Kraiger Ford
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Tabl e 1 (Continued)
Principles Characteristics Examples Sources Key ndings
b. Adaptive
Allocate the optimal time for
mastery and increase the level
of task difculty over the
time of the instruction based
on mastery of earlier phases.
Trainee performance below
expectations leads to reducing task
difculty, whereas performance
above expectations leads to
increasing task difculty.
McDermott et al.
2013, Metzler-
Baddeley &
Baddeley 2009
Meta-analytic results show that increasing
difculty adaptively for the learner led to
greater learning (TR =1.36, K=21,
g=0.75, K=7) than xed increasing in
difculty and constant difculty control
conditions (Wickens et al. 2013).
In a meta-analysis of simulation-based
medical training, variation in task
difculty or complexity produced a mean
d=0.68 (K=20) (Cook et al. 2013).
c. Interleaving Implement a practice schedule
that mixes different kinds of
problems or training content.
Alternating practicing long shots and
short shots at the driving range.
Having customer service
representatives alternate practice
with technical and interpersonal
Bjork & Bjork
Dunlosky et al.
A meta-analysis revealed an overall
signicant effect g=0.42 (K=59,238
effect sizes) for interleaving (Brunmair &
Richter 2019).
3. Engage learner in own learning
a. Generative
Help learners integrate and
elaborate on new knowledge
by making personal
connections between the new
knowledge and the existing
After lecture, prompt learners to
generate an original example or
apply the concept to a real-world
situation (Gingerich et al. 2014).
Have learners explain or demonstrate a
concept to others.
Fiorella & Mayer
et al. 2014
A meta-analysis showed an overall signicant
effect (g=0.55, K=69, N=5,917) for
prompting or directing learners to
self-explain content (Bisra et al. 2018).
Each of eight unique generative learning
strategies yielded median effect sizes of
d=0.40 or higher across studies (Fiorella
& Mayer 2016).
b. Prompts/
Facilitate the self-regulation of
learning by the learner
through questioning of
learning strategies.
Have learners provide
self-explanations that connect the
material to what they already know.
Azevedo &
Cromley 2004,
Kruger &
Dunning 1999
“The benets on learning of meta-cognitive
prompts in younger learners is consistent
(across) a large number of ... studies”
(Kraiger et al. 2020, p. 49).
c. Retrieval (aka
the testing
Have learners recall
information that is now
consolidated in long-term
Frequent repeated testing on concepts
while in training.
Dunlosky et al.
2013, Roediger
& Karpicke
Retrieval practice has been found to be a
powerful mnemonic enhancer producing
large gains in long-term retention relative
to repeated studying (Roediger & Butler
In a review of 120 studies, testing effects
have been demonstrated across a large
range of practice-test formats and material
(Dunlosky et al. 2013).
(Continued) The Science of Workplace Instruction .
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Tabl e 1 (Continued)
Principles Characteristics Examples Sources Key ndings
4. Conduct effective practice
a. Variability of
Ensure learners have
opportunities to apply skills
across different tasks, people,
and job-relevant situations.
Change the software programs used to
address the same task, or change the
functions available for use.
Randomly assign tasks to be learned.
Shea & Wulf
2005, Wulf &
Shea 2002
“There is strong evidence that such
variability of practice is important for
achieving transfer of learning—both for
relatively simple tasks ... and highly
complex real-life tasks” (Kirschner & Van
Merriënboer 2008).
For a meta-analysis of simulation-based
medical training, clinical variation
produced a mean d=0.20 (K=16) (Cook
et al. 2013).
b. Spaced
The implementation of a
training or practice schedule
in which learning events are
spread over time as opposed
to setting schedules with
practice sessions close in
Medical residents are given one day of
training to learn four procedures—
2 h per procedure—or given 2 h of
training each week for four weeks
(Moulton et al. 2006).
Dunlosky et al.
2013, Moulton
et al. 2006
A meta-analysis revealed an overall mean
weighted effect size of 0.46 (K=112) for
effectiveness of spaced practice (Donovan
& Radosevich 1999).
For a meta-analysis of simulation-based
medical training, distributed practice
produced a mean
d=0.66 (K=6) (Cook et al. 2013).
c. Identical
Have learners produce
responses within an accurate
representation of the
operational system,
equipment, and
environmental context.
Job-relevant tasks with events
unfolding in a realistic manner.
Place learner in situations that
incorporate what it feels like (time
pressures, challenges) when
completing the task on the job.
Mirror the collaborative nature for
tasks on the job.
Baldwin & Ford
1988, Marlow
et al. 2017
Issenberg et al. (2005) reviewed 109
published studies in medical journals and
found it “clear that high-delity medical
simulations facilitate learning among
trainees when used under conditions
...[that include]... curriculum integration
...and simulator validity” (p. 24).
In a meta-analysis of simulation-based
nursing education, high- and
moderate-delity simulations resulted in
greater learning than did low-delity
simulations (Kim et al. 2016).
. Kraiger Ford
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Tabl e 1 (Continued)
Principles Characteristics Examples Sources Key ndings
5. Develop past initial mastery
a. Knowledge of
Whether a response is correct
or incorrect [knowledge of
results (KR)]; what the
correct response is
[knowledge of correct
response (KCR)].
A learner is only able to go forward in
a language app if they provide the
correct answer.
Shute 2008, Van
der Kleij et al.
In a meta-analysis of computer-based
instruction (Van der Kleij et al. 2015), KR
produced an effect size of 0.05 and KCR
produced an effect size of 0.33.
b. Feedback and
Information communicated to
the learner that is intended to
modify his or her thinking or
behavior for the purpose of
improving learning (Shute
Identify and possibly correct
inaccurate skills, misconceptions
(errors of commission), and missing
information (errors of omission).
Kluger & DeNisi
1996, Shute
In a meta-analysis of 53 studies of
computer-based instruction, elaborative
feedback produced an effect size of 0.49
(Van der Kleij et al. 2015).
van de Ridder et al. (2015) conducted a
meta-review of 22 meta-analyses and 24
narrative reviews on feedback. She
concluded the following: “The main effect
of the provision of feedback is that
feedback is effective and improves
performance such as in safety-related
performance, work productivity,
judgement abilities, learners’ goal-setting
abilities, and clinicians’ and physicians’
performance. The impact of feedback is
often small to moderate” (p. 664).
c. Overlearning Continued practice on a task
after some criterion of
mastery on that task has been
Repeated practice.
Refresher training.
Driskell et al.
1992, Wang
et al. 2013
A meta-analysis showed a mean d=0.63
(K=88), with stronger effects for
cognitive than physical tasks. Furthermore,
the greater the overlearning, the stronger
the effect (Driskell et al. 1992).
Variables: d,the difference between a treatment and comparison group in standard units; g, Hedges’ effect size estimate; K, number of studies or number of effect sizes if larger; N, total number
of participants across studies; TR, ratio of the performance of the treatment group to the control group (e.g., TR =1.4 means treatment group performed 40% higher on the outcome measure). The Science of Workplace Instruction .
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professionals who want to learn more about each one. Examining the table is benecial, as the
remainder of the article references many of these specic principles and strategies.
The Key Findings column of Table 1 warrants clarication. When there are meta-analytic
ndings of studies relevant to workplace instruction, we provide a summary effect size. When there
are no meta-analyses but there are widely recognized narrative reviews (e.g., the testing effect), we
provide a summary statement. When there is no narrative review (e.g., prompts/meta-cognition),
we provide a recent quote summarizing the extant research. These ndings are provided to as-
sure the reader that these are empirically supported principles. The summaries also provide some
evidence of the level of effectiveness for each principle/strategy. In providing this, we admit to
oversimplifying what are sometimes complex research questions. For example, although Belland
et al. (2015) report moderately large effect sizes for scaffolding, they also cite prior meta-analyses
in which the mean effect sizes (d) range from 0.02 for multimedia instruction to 0.96 for dynamic
To illustrate the relevance of these principles to effective workplace instruction, we briey
discuss the ve core principles. One key to effective instruction is to organize content in ways
that are meaningful and helpful to learners. Adult learners function best when they are presented
with clear objectives and see a connection to current or future work (Noe & Colquitt 2002).
Learning events are organized logically, and extraneous details that waste cognitive resources are
reduced (Mayer 2008). For example, graphics that illustrate key concepts should be placed in close
proximity to the concept, and explanations for an event should be provided as soon as the event
occurs, not days after.
A second key is to optimize the sequencing of training content. Effective training presents con-
tent relevant to the learner’s level of expertise, ensuring that learners master requisite knowledge
before attempting complex skills, providing learning support for more difcult content, ordering
learning tasks in increasing difculty, and, when appropriate, decomposing and learning complex
tasks or skills before combining them. For example, early in language training, foreign words
might be paired with visual cues, but the cues are removed as learners develop their vocabulary.
Interestingly,although part-whole training has been a long recommended instructional principle,
meta-analyses suggest that part-training either produces negative effects or only produces positive
effects under fairly narrow conditions (e.g., Fontana et al. 2015, Wickens et al. 2013). This is in
part due to the inability of learners to practice time-sharing skills in managing task subcompo-
nents (Wickens et al. 2013).On the basis of this evidence, we did not include this popular principle
in Table 1.
By extension, the more that the instruction actively engages learners in the learning event, the
better the outcomes. Engagement means more than interest and takes the form of the learner
responding to and acting on training content by restating, generating answers or explanations,
consciously monitoring, and evaluating progress toward learning goals. The benets of encour-
aging deliberate practice are irrefutable (although claims of the number of hours to become an
expert are questionable). In an exemplar study, Gingerich et al. (2014) examined two groups: One
group of students was prompted to generate their own personal examples of a concept dened
by the instructor, whereas the other group was given the examples by the instructor. The study
showed that the personal example group retained the information more from the prompts, which
helped them integrate the new concept with their existing knowledge base, thus making it easier
to retain and access when needed.
Effective practice leads to the acquisition of new knowledge or skills by requiring attention,
rehearsal, and repetition in the learner (Campitelli & Gobet 2011). However, not all practice con-
ditions are equal, and learning and retention are improved to the extent learners (a) encounter
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practice conditions similar to performance conditions, (b) are exposed to problems or conditions
that vary trial to trial, and (c) encounter practice trials that are distributed over time. In a classic
study involving British postal workers, Baddeley & Longman (1978) compared the speed of acqui-
sition and retention of learning to the type of new postal codes for workers depending on whether
trainees practiced for several hours a day or had their practice distributed over longer intervals. As
is now commonly found in such studies, postal workers who practiced all at once required fewer
hours of training to reach the desired criterion, but the workers who spaced their practice retained
their new skills longer.
Finally, as any parent discovers with a baby’s rst words or rst steps, initial mastery does not
ensure uid subsequent performance, such that instruction is necessary to facilitate development
past initial mastery. Continued repetition leads to overlearning, but greater mastery also results
from reinforcement that declarative knowledge, mental models, and skills are correct, as well as
constructive feedback of how to rene further knowledge or skills. Common examples of over-
learning come from public speaking or acting. It has been estimated that TED Talk presenters
practice on average 70 h for a 15-min talk, ensuring that they will remember their points even if
they feel stressed in the moment.
Delivery represents the processes by which instructional events are designed and shared to facili-
tate learning events. Delivery consists primarily of instructional methods and training media.
Methods. Instructional methods refer to theoretically sound approaches to structuring learn-
ing events. Examples of instructional methods include behavioral modeling training, error-
management training, adaptive guidance training, and intelligent tutoring systems. Theoretically
sound is a key attribute of a method, in part because it differentiates a broad, systemic approach
to instruction from the use of tools or submethods such as PowerPoint. A theory-driven instruc-
tional method is more likely to be effective because it is built on sound theory of human behavior.
One instructional method is not inherently better than another, but any method is likely to re-
sult in achieving learning outcomes when it is theoretically sound, is suitable for the intended
learning outcomes (e.g., hands-on skills versus mental models), and incorporates evidence-based
instructional principles.
Media. Training media or channels refer to the materials and physical means that are used to con-
vey content to learners. Examples of media are job aids, workbooks, classroom lectures, podcasts
and webinars, technology-distributed instruction (TDI) (e.g., online learning), and technology-
enabled instructional systems (e.g., virtual reality and serious games). Media are not explicitly
called out because effective instructional methods are equally effective regardless of channel. This
point was made forcibly more than 25 years ago by educational psychologist Richard Clark (1994),
who argued that “media will never inuence learning” (p. 21).Clark’s argument is that any empir-
ical evidence of the superiority of one medium over another is due to the inclusion of attributes
of instructional design in one but not the other; when design principles are held constant, media
should be equally effective. This is illustrated in a meta-analysis by Sitzmann et al. (2006), who
found that across all studies, web-based instruction was more effective than classroom instruction
on the acquisition of declarative knowledge. However, when they controlled for the presence or
absence of training attributes (e.g., feedback and practice), the effectiveness of both methods was
equivalent. The Science of Workplace Instruction .
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The effectiveness of methods should be invariant over forms of media. As we discuss below,
effectiveness can be enhanced not by changing the medium (e.g., incorporating virtual reality) but
by successfully incorporating instructional design principles into the training.
Intersection of Learners and Methods
Building from these three foundational elements, the science of workplace instruction can advance
training practice and guide future training research through the areas of intersection in Figure 2:
learners and delivery, instructional principles and delivery, and learners and instructional princi-
ples. In the case of the latter two areas, training researchers are uniquely positioned to contribute
to the science of workplace instruction by investigating the interactive nature of principles and
methods and principles and learners in the context of rich organizational cultures and a wide range
of learners with different needs. In the case of the former area, progress will occur more rapidly
when training researchers and professionals reject a long-standing tradition in training orthodoxy
to group participants on some individual difference variable and offer different training methods
for each group. As we show below, this practice is not supported by empirical research and runs
opposite of our assertion (and meta-analytic ndings) that effective training works for all.
Aptitude-treatment interactions. The study and promotion of aptitude-treatment interactions
(ATIs) have been called for in several different domains in psychology and education, including
training research (Aguinis & Kraiger 2009, Salas & Cannon-Bowers 2001). This has occurred
despite the early insistence of Cronbach & Snow (1977) that ATIs are complex and difcult to
demonstrate reliably and that no particular ATI effect is sufciently understood to stand as the
basis for instructional practice. Cronbach (1975) admitted, “Snow and I have been thwarted by
the inconsistent ndings coming from roughly similar inquiries. Successive studies employing the
same treatment variable nd different outcome-on-aptitude slopes” (p. 119).
Although the methodological and statistical challenges in validating ATIs—if they exist—are
beyond the scope of this article, in the educational domain there is now more than 50 years of
research that has struggled to nd replicable, substantive, and theoretically meaningful ATIs (e.g.,
Bracht 1970, Preacher & Sterba 2019). In training research, Kowollik et al. (2010) conducted a
meta-analysis to test a popular ATI hypothesis that lower general mental ability (GMA) learners
benet from greater training structure: Across 51 studies, Kowollik et al. found that although there
was evidence for small interactions, there was not consistent support for the commonly purported
disordinal ATIs across outcome criteria. They concluded that the small instructional gains from
designing a GMA-structure ATI approach would not outweigh implementation costs and that
ATIs imported from educational psychology are somewhat of a “received doctrine” (Kowollik
et al. 2010) in the training literature. In short, training research and training practice are better
served by linking instructional principles to training delivery for all learners and not by chasing
ATI effects. The disruptive effects of chasing ATIs can be seen in two lines of research: learning
styles and age-specic learning.
Learning styles. Tailoring instruction to match individual learning styles is a long-standing in-
structional practice not empirically supported by research. The approach assumes individuals have
innate, measurable learning styles. The meshing hypothesis states that if learners are provided in-
struction in their preferred modality (e.g., visual versus kinesthetic versus auditory), they will learn
better than if given a different modality (Pashler et al. 2008). However, there is no consensus as to
what constitutes a learning style; one review reported more than 50 distinct learning style theories
(Coeld et al. 2004).
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Pashler et al. (2008) conducted an extensive review of the learning styles research applying
strict screening criteria to the inclusion of studies. They were unable to nd any evidence using
their study criteria supporting the learning styles hypothesis and found several reputable studies
providing evidence that contradicted the effect. Thus, Pashler et al.concluded that although there
is ample evidence that when asked, individuals will state a preference for one learning modality
over another, there is little evidence that catering to these preferences leads to superior learning
outcomes. Pashler et al. did not report the number of studies they reviewed or overall effect size;
however, Aslaksen & Lorås (2018) recently conducted a meta-analysis of the same literature and
found “still no replicable statistical evidence for enhanced learning outcome by aligning instruc-
tion to modality-specic learning styles” (p. 1).
Empirical support for styles instruction is undoubtedly hampered by several methodological
issues, including low statistical power for detecting moderation. Nonetheless, the promise of the
ultimate customization of learning to individuals is not supported empirically and runs counter to
our proposition that well-designed instruction works for all learners. In the words of Pashler et al.
(2008), “it is undeniable that the instruction that is optimal for a given [learner] will often need to
be guided by the aptitude, prior knowledge, and cultural assumptions that [the learner] brings to a
learning task. However, assuming that people are enormously heterogeneous in their instructional
needs may draw attention away from the body of basic and applied research on learning that
provides a foundation of principles and practices that can upgrade everybody’s learning” (p. 117).
Age-specic learning. A second ATI application is the design of age-specic instruction. This
practice stems from research conrming age-related differences in cognitive skills such as pro-
cessing speed (Kraiger 2017, Wolfson et al. 2014) as well as meta-analytic evidence that adults on
average learn less and take longer to complete training compared to younger learners (Kubeck
et al. 1996). Advocates of age-specic training call for unique training interventions that account
for known age-related decits of older learners (e.g., Mead & Fisk 1998, Truxillo et al. 2015).
For example, training design could remove practice variability to support older adults with slower
cognitive processing speeds.
In contrast, age-inclusive training proposes that empirically based instructional principles
should be benecial to all age groups (Van Gerven et al. 2006, Wolfson et al. 2014). There may be
treatment by age interactions, but of a different form. Because older adults may need more learn-
ing support, it may be the case that well-designed training works for all but especially for older
learners. This includes the use of instructional principles (discussed above) as well as fundamental
submethods such as clear learning objectives, job-relevant exercises, instructional aids to organize
encoding and recall, and timely feedback (Kraiger 2017). Many studies in fact demonstrate that
implementing validated instructional principles signicantly improves outcomes in learners of all
ages (e.g., Kornell et al. 2010, van Gerven et al. 2006, Wolfson & Kraiger 2014). Well-designed
training works for all.
At the outset of this article, we noted a diminishing interest in training research; we are also aware
that the learning and development industry continues to change—perhaps not always with a solid
foundation in empirically supported principles and methods (Rynes et al. 2007). We contend that
training research can be reinvigorated and training practice advanced by applying the framework
shown in Figure 1 and understanding how theoretically based instructional methods incorporat-
ing empirically supported principles facilitate effective learner events, leading to targeted, multi-
dimensional learning outcomes that ultimately result in positive changes at the learner, job, and
organizational levels. The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
A specic and important research need is to examine how to optimize learning in work con-
texts through a focus on instructional principles. We recommend three areas for further research.
First, research should focus on how to optimize theoretically based training methods by incorpo-
rating relevant instructional principles. Second, given the move to self-directed learning,research
should determine direct ways to support learners through known instructional principles. Third,
the rapid adaptation of technology-enhanced instructional methods can be supported through the
integration of instructional design with these synthetic environments.
Intersection of Instructional Principles and Training Methods
At the outset of their review,Salas et al. (2012) asserted “(a) properly designed training works, and
(b) the way training is designed, delivered and implemented can greatly inuence its effectiveness”
(p. 74). Meta-analytic evidence conrms the rst assertion, but there is important work to be
done to understand precisely how training design and implementation affect the achievement of
training objectives in organizational contexts. Specically, meta-analyses reveal the effectiveness
of many training methods, including behavioral modeling (Taylor et al. 2005) and error-based
framing (Keith & Frese 2008). However, meta-analyses such as these typically report large
heterogeneity in effects across studies, suggesting the possibility of study-level moderators. For
example, crew resource management training is a specic training method that is informed by
several theories of shared cognition and root causes of errors (Salas et al. 1999). O’Connor et al.
(2008) conducted a meta-analysis on crew resource management training and found overall pos-
itive effects on attitudes, knowledge, and behaviors but noted “substantial variation in effect sizes
across these studies” (p. 361). Similarly, cross-cultural training encompasses a variety of training
practices that are grounded in theories of social learning and culture shock, as well as the dynamics
of adjustment (Littrell et al. 2006). In their meta-analysis of cross-cultural training, Morris &
Robie (2001) found signicant main effects for training on performance and adjustment. How-
ever,effect size variance attributed to statistical artifacts was small, again suggesting the possibility
of substantive factors moderating study outcomes. Observing this study-to-study variability in
the effectiveness of cross-cultural training, Littrell et al. speculated that this variability could have
been due to variance in training rigor. Similarly, Mattingly & Kraiger (2019) conducted a meta-
analysis of emotional intelligence training. They reported a signicant main effect for training,
but substantial heterogeneity across studies. Using regression analysis, they found that variance
in training properties including practice and feedback explained signicant variance in training
One implication is that although theory-based training works, its effectiveness is likely mod-
erated by characteristics of effective instruction that are generalizable across all forms of training
(Noe & Colquitt 2002). (This is not to discount other moderators of training effectiveness but
to stress that variation in effects due to design characteristics has been understudied and likely
underrecognized.) Researchers have begun to explore the intersection of training methods and
instructional principles through meta-analysis by coding for the absence of principles in source
studies, as Mattingly & Kraiger (2019) demonstrate. Several meta-analyses provide direct evidence
for the moderating effects of design characteristics generally and instructional principles speci-
cally on relationships between methods and learning outcomes. For example, Taylor et al. (2005)
found that behavioral modeling training (method) is more effective with spaced versus massed
practice (instructional principle). Kalinoski et al. (2013) also reported that distributed practice led
to stronger effects for diversity training on cognitive and affective outcomes compared to massed
practice. Finally, Keith & Frese (2008) reported that the clarity of task feedback moderated the ef-
fectiveness of the error-based training methods. Collectively, these reviews show that empirically
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supported training methods are more effective when accompanied by the use of sound instruc-
tional principles and training design.
Latham & Saari’s (1979) classic study of behavioral modeling illustrates nicely the benets of
pairing empirically supported instructional principles with theoretically sound training methods.
Drawing on social learning theory, they found strong evidence for the effectiveness of behavioral
modeling on knowledge, skill, and transfer outcomes. The study demonstrated the value of so-
cial reinforcement to enhance motivation and use of demonstration and practice to aid retention
and reproduction. However, a closer examination reveals how multiple time-tested instructional
principles facilitated encoding, organization, and retrieval processes. Consistent with the principle
of distributed practice, Latham & Saari spaced their 18 h of instruction over nine 2-h sessions.
Consistent with the principle of practice variability, the training presented nine different practice
scenarios based on a prior job analysis. The generative effect was implemented by asking trainees
to recreate actual situations that had happened to them and role-play that event. After each in-
structional session, learners were given a performance aid listing the key behaviors and instructed
to practice the skills learned immediately with an employee, illustrating practice effects (Dunlosky
et al. 2013) and opportunity to perform (Ford et al. 1992). Theory-based training interventions
are effective, but they work better when they incorporate sound instructional principles.
Because workplace training interventions and instructional principles have been proposed and
tested in different disciplines, less is known about the intersection of the two. From a research
perspective, the greatest progress will come not from designing new methods or from testing ad-
ditional instructional principles, but from examining how best to integrate instructional principles
into effective training methods. For example, how important are identical elements for the effec-
tiveness of error management training? Does the length of spacing matter for behavioral modeling
training? What are the best ways to implement generative learning into adaptive guidance plat-
forms? In the science of workplace instruction, questions such as these can be pursued within the
rich context of providing job-relevant training within organizational contexts.
Enhancing Self-Directed Learning
Self-directed learning refers to “learners’ active and volitional approach to conceptualize, design,
conduct, and evaluate a learning project” (Noe et al. 2014, p. 249). Organizations increasingly are
encouraging employees to stay on top of their career by identifying learning needs and manag-
ing their own discrete learning events. In this way, incumbents can reduce the potential for skill
obsolescence or gain new skills for other more sustainable types of jobs within the organization.
Properly executed, self-directed learning can increase learning efciency and enhance individ-
ual performance. Organizations may also save costs by shifting the responsibility for learning to
members of their workforce.
As with various forms of formal training, there is cumulative evidence that self-directed learn-
ing is effective. One variation of self-directed learning is informal learning. Informal learning
typically occurs on the job and without organizational oversight, for example, when learners ask
a coworker for help or search the Internet for job-relevant information. A recent meta-analysis
showed positive effects for informal learning behaviors on outcomes such as knowledge/skill ac-
quisition and job performance (Cerasoli et al. 2018).
The problem. With self-directed learning, the learner assumes greater control in the planning,
scheduling, and executing of learning events than during formal training. Accordingly, the
effectiveness of self-directed learning is limited by how well learners manage these events. Ef-
fective self-management requires two broad skill sets—the monitoring of learning processes and The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
outcomes and the regulation of the affective, cognitive, and behavioral processes that promote
learning (Sitzmann & Ely 2011). Evidence suggests learners are challenged in both respects.
Monitoring and regulating are implicit in the construct of learner control—how the training
system allows learners to make decisions that alter the learning environment (Landers & Reddock
2017). Landers & Reddock proposed a nine-dimensional framework of objective learner con-
trol consisting of instructional control (skipping content, supplementing content, and managing
the sequence, pace, practice, and guidance control of content), style control (control of aesthetic
training characteristics), and scheduling control (time and location control). Their meta-analysis
showed that these dimensions of instructional control generally had small but positive effects on
skill outcomes. However, for training reactions and knowledge outcomes, effects were smaller
and inconsistent across specic dimensions (e.g., practice control versus supplement control). Al-
though these ndings show some support for providing learner control, the researchers cautioned
that multiple dimensions were so frequently confounded within single studies that it may be mis-
leading to conclude that any one dimension is effective.
The limitations of learner control revealed by research can be explained by the predictable
errors trainees make in regulating their learning behaviors. Bjork et al. (2013) reviewed research
in educational psychology on learner self-regulatory behavior and concluded the following: “Al-
though individual differences occur in effective strategy use, with some students using effective
strategies that contribute to their achievement, many students not only use relatively ineffective
strategies (e.g., rereading), but believe that they are relatively effective” (p. 423). Specically, learn-
ers generally (a) mistakenly believe that blocked or massed practice is more effective than spacing,
(b) erroneously believe that rereading content is more effective for learning than being tested
on it, (c) fail to overlearn to enable mastery, and (d) are overcondent in their mastery or poor
judges of whether they have retained newly learned content. Furthermore, the use of ineffective
self-regulatory behaviors can be difcult to extinguish. For example, in Kornell & Bjork’s (2008)
study, students rated their learning as superior using massed study practices even when they were
given feedback that they perform better using spacing. Bjork et al. suggested that this may be a
metacognitive illusion because massed practice is perceived to be easier than spacing.
The sum effect of these judgments and biases is that providing instructional control may un-
dermine learning due to suboptimal decisions during the learning event (Kraiger & Jerden 2007).
Thus, the problem is that learners are being given more responsibility for guiding their own learn-
ing, whereas research demonstrates that they are awed executors of the necessary skills to do so.
Research to enhance self-directed learning. The science of workplace instruction allows us to
view the problem of suboptimal self-directed learning from the broader perspective of workplace
training. For example, prior training research has shown positive effects for accountability on
instructional outcomes, but there is no research on how it affects learning events. Accountability
refers to the perceived need to justify one’s action to an audience with sanction or reward power
(Frink & Klimoski 1998). Accountability manipulations or perceptions of trainee accountability
have been shown to positively impact learning during training (DeMatteo et al. 1997) and
transfer (Saks & Belcourt 2006). But there is little research on how accountability affects learning.
DeMatteo et al. found a stronger effect for an accountability manipulation before training rather
than after training—but before transfer was measured. They also reported that increased account-
ability resulted in greater notetaking by participants during training. Thus, it appears that holding
learners accountable may increase their engagement during learning events and their efforts to
encode or organize information. However, this needs to be established empirically. Additionally,
it would be useful to investigate the extent to which specic instructional principles interact
with increased accountability. For example, cognitive prompts are brief queries inserted into
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OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
training for purposes of encouraging meta-cognitive, elaboration, or active processing (Kraiger
et al. 2020). Cognitive prompts have a demonstrated impact on learning outcomes (Sitzmann
et al. 2009). Would prompts that remind learners of the need to justify their actions increase
accountability and promote improving encoding and organizing processes? Is the need to apply
training content to the job sufcient to create accountability, or does there need to be a threat of
post-training evaluation? What are effective prompts that increase perceived accountability but
do not distract from the learning task?
The necessity of providing job-related learning and development leads to other solutions for
organizations. Kraiger & Jerden (2007) speculated that many learners either expect or prefer
that the learning and development enterprise structure their training experiences—determining
what should be learned when. They further distinguished between objective and perceived learner
control, with the former managed by the training system and the latter by personal perception.
Landers & Reddock (2017) found that objective control is related to learning outcomes and per-
ceived control is related to training reactions. Together, these propositions create opportunities
to mitigate negative effects of learner control by minimizing what aspects of training the trainees
can affect. To improve training, multiple instructional principles could be added to the system and
learners given guidance as to which principles are activated and how they are operationalized. For
example, trainees could be given freedom to choose the timing or space between learning trials
and specic transfer tasks from within a broader population of potential, varied tasks. In these
ways, training could be structured to enhance perceived control and agency, but with sufcient
design features “baked in” to support learning. Using scaffolding, as learners progress, the system
would provide more choice in designing learning and transfer trials. Tactics to support learner
decision making have been common for a long time in technology-enhanced environments such
as intelligent tutoring systems (Ma et al. 2014). They are also consistent with adaptive guidance
training (Bell & Kozlowski 2002), which has been found to have a positive effect on trainees’ study
and practice, self-regulation, knowledge acquisition, and performance. Determining the optimal
balance of objective and perceived control, as well as how that balance is affected by individual,
job-related, and organizational factors, is an important applied research problem.
Another research area worth pursuing is the investigation of optimal instructional principles
to guide self-directed learning as a function of the developmental stage of the learner. Kanfer &
Ackerman (1989) demonstrate how in early stages of skill acquisition, learners must devote greater
attentional resources to a task, and learning events require less attention as skills are compiled
and automatized. Thus, it stands to reason that instructional principles that facilitate attentional
control (e.g., coherence and contiguity) would be more effective early in self-directed learning
events, and principles that demand less attention (e.g., metacognition and practice variability)
would be more effective later. Although these effects have not been closely studied in the self-
directed learning literature, we know from studies of formal instruction that the utility of some
principles has been dependent on the stage of learner acquisition. As one example, the principle of
part-whole training holds that part-task instruction has greater utility early in skill acquisition, but
whole-task instruction is more useful at later stages (Plott et al. 2014). Similarly, research on the
development of motor skills shows that constant, blocked practice schedules are benecial early
in training to enable the acquisition of basic skills, but more variable practice is more benecial
later to promote ne-tuning and generalization (e.g., Lai et al. 2000).Because much of this is basic
research on discrete knowledge or skills, additional research is needed to determine the extent to
which such effects generalize to self-directed workplace learning.
Research in educational contexts conrms that students frequently endorse and practice inef-
fective study methods (Dunlosky et al. 2013). To support self-directed learning in organizational
contexts, it would be benecial to know what instructional principles adult learners routinely use The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
to monitor and guide their learning. We also need to better understand how effective each instruc-
tional principle is with respect to self-directed learning and how to help learners adopt the most
effective strategies. Given our tendency to overestimate our own abilities (Kruger & Dunning
1999), we may be prone to resist efforts to improve our capacity to learn. Thus,research will need
to explore ways to overcome these tendencies so as to best guide individuals during self-directed
Enhancing Synthetic Learning Environments
Synthetic learning environments refer to technology-enabled training media that augment, create,
and/or manage learning events in a world characterized by both realistic context and embedded
instruction (Cannon-Bowers & Bowers 2010). Common examples are simulations (e.g., Hays et al.
1992), serious games (Susi et al. 2007), and virtual reality (Howard & Marshall 2019). There is
considerable overlap in the denitions and operationalizations of these three forms of media, as
all involve the creation of technology-enabled interactive and articial environments that facilitate
the development of job-related knowledge, skills, and affect. Serious games stand somewhat apart,
because although they also employ a synthetic environment for purposes of training or education,
they add elements associated with most forms of games such as immersion, conict/challenge,
rules/goals, and human interaction (Bedwell et al. 2012).
Meta-analytic evidence supports the effectiveness of both simulations and virtual reality.Many
of these investigations are specic to an industry, job, or function. For work simulators, there is
evidence of building skills in contexts such as medical education (Issenberg et al. 2005) and ying
(Hays et al. 1992). Meta-analytic evidence for the effectiveness of virtual reality training includes
areas of laparoscopic surgery (Alaker et al. 2016) and social skills (Howard & Gutworth 2020).
However, the level of effectiveness of synthetic learning environments may depend on the type
of learning tasks. A recent meta-analysis found support for virtual and augmented reality training
for physical tasks, but null effects for cognitive tasks (Kaplan et al. 2020). Additionally, the efcacy
of serious games remains in question. Mixed or null effects have been typically reported for large-
scale reviews of serious games in both education contexts (Lamb et al. 2018) and training and
education when the comparison group had similar activity levels as the test group (Sitzmann 2011).
Despite several decades of empirical research on games for training purposes, even these results
should be viewed with caution, as there is a relatively small percentage of rigorous investigations
(e.g., Clark 2007).
The problem. Synthetic learning environments are increasingly popular in industry and educa-
tion (e.g., Gasparevic 2018) and are being driven largely by the availability of ubiquitous, device-
enabled, high-bandwidth distribution channels. Capability is only increasing as 5G becomes more
prevalent. As others have noted (e.g., Bedwell et al. 2012, Gunter et al. 2006), advancements are
being implemented by software providers without evidence that the platforms facilitate learning
(e.g., Mayer 2011b). Thus, the risk here is that we are building and propagating high-speed, data-
rich instructional tools that do not take advantage of what is known about how people learn (the
science of learning) or how to best facilitate learning (the science of instruction). Just as we have
advocated for greater integration of instructional principles into theoretically supported training
methods, we see the value in understanding which instructional principles best lend themselves to
synthetic learning environments and how these principles can be incorporated.
Research to enhance synthetic learning environments. In the short run, there is a need for
theory-based papers that marry scientic principles with technology-based training (e.g., Gunter
et al. 2006; Mayer 2008, 2019). However, such work needs to appear in forums that are available to
. Kraiger Ford
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developers who in turn see value in implementing scientically sound instruction. In the long run,
research is needed to understand optimal conditions for implementing instructional principles in
various forms of TDI.
As have others before us (Clark 1994, 2007; Mayer 2011b; Sitzmann et al. 2006), we contend
that the medium is much less important than sound instructional design—incorporating empir-
ically based instructional principles improves learning regardless of the medium. That said, we
also believe that some principles can be more easily and more effectively implemented in syn-
thetic learning environments. For example, the instructional principle of identical elements states
that transfer is enhanced to the extent to which the stimuli and responses during learning events
are identical to those in the actual work environment (Saks & Belcourt 2006). As one example
of this, Libin et al. (2010) trained customer support staff in healthcare settings by showing them
realistic videos of scenarios in which they are confronted with actual patient problems and must
make real-time decisions and then see the consequences of their actions. Practice variability can
be easily implemented by varying the situations and problems that trainees must confront, and in
more sophisticated software, generative learning can be supported by enabling learners to author
their own scenarios from problems previously faced.
With baseline knowledge of the effectiveness of each instructional principle (see Table 1)and
sound theories underlying synthetic learning environments as instructional events (e.g., Howard &
Marshall 2019, Landers et al. 2019), researchers can select and test the principles that are expected
to be more effective in these contexts. Thus, referring to Figure 2, we expect some instructional
principles to be differentially relevant for facilitating learning when enacted in certain synthetic
learning environments. There may well be ordinal interactions of some principles with different
environments. For example, although generative learning is generally effective in all contexts, it
may be more effective in virtual reality training where learners may be more used to exploratory
The past three decades has seen tremendous growth in theory and research on learning and devel-
opment in organizations. The development and testing of models of training systems embedded
in organizational contexts have demonstrated both the overall impact of training and role of in-
dividual and organizational factors as antecedents and moderators of that effectiveness. From this
we understand that instructional events lead to instructional outcomes. Less clear are the ways in
which learning events and outcomes mediate that relationship. To reinvigorate training research,
we proposed the learner, instructional principles, and training delivery as the elements of the sci-
ence of workplace instruction. By delineating the critical role of instructional principles in work-
place training and by exploring the intersections of those principles with learners and emerging
training technologies, we hope to inform the next decade of research on workplace instruction.
1. The science of workplace instruction postulates that instructional events managed by
the organization lead to learning events and learning outcomes within individuals which
are manifested as instructional outcomes at the organizational level.
2. The science of workplace instruction postulates that learning is facilitated by active pro-
cessing of the learner and sound application of instructional principles and delivery. The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
3. Five core instructional principles have empirical support and can be applied in multiple
ways to facilitate learning.
4. The most effective instructional methods are rooted in sound theories of human behav-
ior and incorporate evidence-based instructional principles.
5. The relative impact of different training media or channels is substantially less important
than the use of theory-based methods and empirically supported instructional principles.
6. There is little to no evidence to support matching instruction to individual learning
styles; effective instruction results from the use of theory-based methods and empirically
supported instructional principles.
7. There is little to evidence to support varying instruction based on learner age; effec-
tive instruction results from the use of theory-based methods and empirically supported
instructional principles.
1. Include information on the incorporation of instructional principles in training research
reports even if not the primary focus of the study or training.
2. Examine the moderating inuence of instructional principles on training effectiveness
in meta-analyses of training methods and training effectiveness.
3. Determine the value of including specic empirically supported instructional principles
when combined with effective, theoretically based training methods.
4. Investigate the impact of the organizational context on learners’ disinclination to effec-
tively monitor and regulate their own learning, as demonstrated in educational contexts.
5. Specify and test the effectiveness of instructional principles that are most likely to opti-
mize learning in synthetic learning environments.
The authors are not aware of any afliations, memberships, funding, or nancial holdings that
might be perceived as affecting the objectivity of this review.
Aguinis H, Kraiger K. 2009. Benets of training and development for individuals and teams, organizations,
and society. Annu. Rev. Psychol. 60:451–74
Alaker M, Wynn GR, Arulampalam T. 2016. Virtual reality training in laparoscopic surgery: a systematic
review meta-analysis. Int. J. Surg. 29:85–94
Ambrose SA, Bridges MW, DiPietro M, Lovett MC, Norman MK. 2010. How Learning Works. San Francisco:
Anderson LW, Krathwohl DR, Airiasian W, Cruikshank KA, Mayer RE, Pintrich PR. 2001. A Taxonomy for
Learning, Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Outcomes: Complete Edition.
New York: Longman
. Kraiger Ford
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Aslaksen K, Lorås H. 2018. The modality-specic learning style hypothesis: a mini-review. Front. Psychol.
Azevedo R, Cromley JG. 2004. Does training on self-regulated learning facilitate students’ learning with hy-
permedia. J. Educ. Psychol. 96:523–35
Baddeley AD, Longman DJA. 1978. The inuence of length and frequency of training session on the rate of
learning to type. Ergonomics 21:627–35
Baldwin TT, Ford JK. 1988. Transfer of training: a review and directions for future research. Pers. Psychol.
Bedwell WL, Pavlas D, Heyne K, Lazzara EH, Salas E. 2012. Toward a taxonomy linking game attributes to
learning: an empirical study. Simul. Gaming 43:729–60
Bell BS, Kozlowski SW. 2002. Adaptive guidance: enhancing self-regulation, knowledge, and performance in
technology-based training. Pers. Psychol. 55:267–306
Bell BS, Tannenbaum SI, Ford JK, Noe RA, Kraiger K. 2017. 100 years of training and development research:
what we know and where we should go. J. Appl. Psychol. 102:305–23
Belland BR, Walker AE, Olsen MW, Leary H. 2015. A pilot meta-analysis of computer-based scaffolding in
STEM education. J. Educ. Technol. Soc. 18:183–97
Bisra K, Liu Q, Nesbit JC, Salimi F, Winne PH. 2018. Inducing self-explanation: a meta-analysis. Educ. Psychol.
Rev. 30:703–25
Bjork RA, Bjork EL. 1992.A new theory of disuse and an old theory of stimulus uctuation. In From Learning
Processes to Cognitive Processes: Essays in Honor of William K. Estes, Vol. 2, ed. A Healy, S Kosslyn, R Shiffrin,
pp. 35–67. Hillsdale, NJ: Erlbaum
Bjork RA, Dunlosky J, Kornell N. 2013. Self-regulated learning: beliefs, techniques, and illusions. Annu. Rev.
Psychol. 64:417–44
Bloom BS. 1994. Bloom’s Taxonomy. Chicago: Univ. Chicago Press
Bracht GH. 1970. Experimental factors related to aptitude-treatment interactions. Rev. Educ. Res. 40:627–45
Briggs GE, Naylor JC. 1962. The relative efciency of several training methods as a function of transfer task
complexity. J. Exp. Psychol. 64:505–12
Brunmair M, Richter T. 2019. Similarity matters: a meta-analysis of interleaved learning and its moderators.
Psychol. Bull. 145:1029–52
Campbell JP. 1971. Personnel training and development. Annu. Rev. Psychol. 22:565–602
Campitelli G, Gobet F. 2011. Deliberate practice: necessary but not sufcient. Cur r. Dir. Psy cho l. S ci. 20:280–85
Cannon-Bowers J, Bowers C. 2010. Synthetic learning environments: on developing a science of simulation,
games, and virtual worlds for training. In Learning, Training, and Development in Organizations,ed.SWJ
Kozlowski, E Salas, pp. 229–63. New York: Routledge
Cerasoli CP, Alliger GM, Donsbach JS, Mathieu JE, Tannenbaum SI, Orvis KA. 2018. Antecedents and out-
comes of informal learning behaviors: a meta-analysis. J. Bus. Psychol. 33:203–30
Clardy A. 2000. Learning on their own: vocationally oriented self-directed learning projects. Hum. Resour. Dev.
Q. 11:105–25
Clark RE. 1994. Media will never inuence learning. Educ. Technol. Res. Dev. 42:21–29
Clark RE. 2007. Learning from serious games? Arguments, evidence, and research suggestions. Educ. Technol.
Coeld F, Moseley D, Hall E, Ecclestone K. 2004. Learning Styles and Pedagogy in Post-16 Learning: A Systematic
and Critical Review. London: Learn. Skills Res. Cent.
Colquitt JA, LePine JA, Noe RA. 2000. Towards an integrative theory of training motivation: a meta-analytic
path analysis of 20 years of research. J. Appl. Psychol. 85:678–807
Cook DA, Hamstra SJ, Brydges R, Zendejas B, Szostek JH, et al. 2013. Comparative effectiveness of instruc-
tional design features in simulation-based education: systematic review and meta-analysis. Med. Teach.
Cotton JW. 1976. Models of learning. Annu. Rev. Psychol. 27:155–87
Cox CB, Beier ME. 2009. The moderating effect of individual differences on the relationship between the
framing of training and interest in training. Int. J. Train. Dev. 13:247–61
Cronbach LJ. 1975. Beyond the two disciplines of scientic psychology. Am. Psychol. 30:116–27 The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Cronbach LJ, Snow RE. 1977. Aptitudes and Instructional Methods: A Handbook for Research on Aptitude-Treatment
Interactions. New York: Irvington
DeMatteo JS, Dobbins GH, Lundby KM. 1997. The effects of accountability on training effectiveness. Trai n .
Res. J. 3:39–57
Donovan JJ, Radosevich DJ. 1999. A meta-analytic review of the distribution of practice effect: Now you see
it, now you don’t. J. Appl. Psychol. 84:795–805
Driskell JE, Willis RP, Copper C. 1992. Effect of overlearning on retention. J. Appl. Psychol. 77:615–22
Dunlosky J, Rawson KA, Marsh EJ, Nathan MJ, Willingham DT. 2013. Improving students’ learning with
effective learning techniques promising directions from cognitive and educational psychology. Psychol.
Sci. Public Interest 14(1):4–58
Ellis HC. 1965. The Transfer of Learning. New York: Macmillan
Fiorella L, Mayer RE. 2016. Eight ways to promote generative learning. Educ. Psychol. Rev. 28:717–41
Fontana FE, Furtado O Jr., Mazzardo O, Gallagher JD. 2015. Whole and part practice: a meta-analysis. Pe rcept.
Mot. Skills 109:517–30
Ford JK. 2021. Learning in Organizations: An Evidence-Based Approach. New York: Routledge. In press
Ford JK, Baldwin TT, Prasad J. 2018. Transfer of training: the known and the unknown. Annu. Rev. Organ.
Psychol. Organ. Behav. 5:201–25
Ford JK, Kraiger K. 1995. The application of cognitive constructs and principles to the instructional systems
model of training: implications for needs assessment, design, and transfer. International Review of Industrial
and Organizational Psychology, Vol. 10, ed. C Cooper, IT Robertson, pp. 1–48. Hoboken, NJ: Wiley
Ford JK, Quiñones MA, Sego DJ, Sorra JS. 1992. Factors affecting the opportunity to perform trained tasks
on the job. Pers. Psychol. 45:511–27
Frink D, Klimoski R. 1998.Toward a theory of accountability in organizations and human resources manage-
ment. In Research in Personnel and Human Resources Management, Vol. 16, ed. GR Ferris, pp. 1–51. Bingley,
UK: Emerald Publ.
Gasparevic D. 2018.Why virtual-reality training for employees is catching on. SHRM, Aug. 14. https://www.
Gingerich KJ, Bugg JM, Doe SR, Rowland CA, Richards TL, et al. 2014. Active processing via write-to-learn
assignments: learning and retention benets in introductory psychology. Teach. Psychol. 41:303–8
Ginns P. 2006. Integrating information: a meta-analysis of the spatial contiguity and temporal contiguity ef-
fects. Learn. Instr. 16:511–25
Glaser R, Bassok M. 1989. Learning theory and the study of instruction. Annu. Rev. Psychol. 40:631–66
Grifths KE, Campbell MA. 2009. Discovering, applying and integrating: the process of learning in coaching.
Int. J. Evid.-Based Coach. Mentor. 7:16–30
Gunter G, Kenny RF, Vick EH. 2006. A case for a formal design paradigm for serious games. J. Int. Digit.
Media Arts Assoc. 3:93–105
Halpern DF, Graesser A, Hakel M. 2007. 25 Learning principles to guide pedagogy and the design of learning
environments. Assoc. Psychol. Sci. Taskforce Life Long Learn. Work Home, Washington, DC. https://les/featured/halpern/25-principles.pdf
Hays RT, Jacobs JW, Prince C, Salas E. 1992. Flight simulator training effectiveness: a meta-analysis. Mil.
Psychol. 4:63–74
Hicks WD, Klimoski RJ. 1987. Entry into training programs and its effects on training outcomes: a eld
experiment. Acad. Manag. J. 30:542–52
Howard M, Gutworth MB. 2020. A meta-analysis of virtual reality training programs for social skill develop-
ment. Comp. Educ. 144:103707
Howard MC, Marshall CJ. 2019. Virtual reality training in organizations. In The Cambridge Handbook of Tech-
nology and Employee Behavior, ed. RN Landers, pp. 347–83. Cambridge, UK: Cambridge Univ. Press
Issenberg BS, McGaghie WC, Petrusa ER, Gordon LD, Scalese RJ. 2005. Features and uses of high-delity
medical simulations that lead to effective learning: a BEME systematic review. Med. Teach. 27:10–28
Kalinoski ZT, Steele-Johnson D, Peyton EJ, Leas KA, Steinke J, Bowling NA. 2013. A meta-analytic evaluation
of diversity training outcomes. J. Organ. Behav. 34:1076–104
. Kraiger Ford
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Kanfer R, Ackerman PL. 1989. Motivation and cognitive abilities: an integrative/aptitude-treatment interac-
tion approach to skill acquisition. J. Appl. Psychol. 74:657–90
Kaplan AD, Cruit J, Endsley M, Beers SM, Sawyer BD, Hancock PA. 2020. The effects of virtual reality,
augmented reality, and mixed reality as training enhancement methods: a meta-analysis. Hum. Factors.
Keith N, Frese M. 2008. Effectiveness of error management training: a meta-analysis. J. Appl. Psychol. 93:59–69
Kim J, Park JH, Shin S. 2016. Effectiveness of simulation-based nursing education depending on delity: a
meta-analysis. BMC Med. Educ. 16:152
Kirschner PA, Van Merriënboer JJG. 2008. Ten steps to complex learning: a new approach to instruction and
instructional design. In 21st Century Education: A Reference Handbook, ed. TL Good, pp. 244–53. Thousand
Oaks, CA: Sage
Kluger AN, DeNisi A. 1996. The effects of feedback interventions on performance: a historical review, a
meta-analysis, and a preliminary feedback intervention theory. Psychol. Bull. 119:254–84
Kornell N, Bjork RA. 2008. Learning, concepts, and categories: Is spacing the “enemy of induction?” Psychol.
Sci. 19:585–92
Kornell N, Castel AD, Eich TS, Bjork RA. 2010. Spacing as the friend of both memory and induction in young
and older adults. Psychol. Aging 25:498–503
Kowollik V, Day EA, Wang X, Arthur W Jr., Schuelke MJ, Hughes MG. 2010. The interaction between ability
and training structure: a meta-analysis. Poster presented at the 25th Annual Conference of the Society for
Industrial and Organizational Psychology, Atlanta, Aug. 6–10
Kraiger K. 2002. Decision-based evaluation. In Creating, Implementing, and Managing Effective Training and
Development, ed. K Kraiger, pp. 331–75. San Francisco: Jossey Bass
Kraiger K. 2017. Designing effective training for older workers. In The Palgrave Handbook of Age Diversity and
Work , ed. E Parry, J McCarthy, pp. 639–67. London: Palgrave Macmillan
Kraiger K, Cavanagh TM, Willis CM. 2020. Why do cognitive prompts hurt learning in older adults? Int. J.
Train. Dev. 24:40–56
Kraiger K, Finkelstein LM, Varghese LS. 2019. Enacting effective mentoring behaviors: development and
initial investigation of the cuboid of mentoring. J. Bus. Psychol. 34:403–24
Kraiger K, Ford JK. 2007. The expanding role of workplace training: themes and trends inuencing training
research and practice. In Historical Perspectives in Industrial and Organizational Psychology, ed. LL Koppes,
pp. 281–309. Mahwah, NJ: Lawrence Erlbaum
Kraiger K, Ford JK, Salas E. 1993. Application of cognitive, skill-based, and affective theories of learning
outcomes to new methods of training evaluation. J. Appl. Psychol. 78:311–28
Kraiger K, Jerden E. 2007. A new look at learner control: meta-analytic results and directions for future re-
search. In Where is the Learning in Distance Learning? Towards a Science of Distributed Learning and Training,
ed. SM Fiore, E Salas, pp. 65–90. Washington, DC: APA Books
Kruger J, Dunning D. 1999. Unskilled and unaware of it: how difculties in recognizing one’s own incompe-
tence lead to inated self-assessments. J.Pers.Soc.Psychol.77:1121–34
Kubeck JE, Delp ND, Haslett TK, McDaniel MA. 1996. Does job-related training performance decline with
age? Psychol. Aging 11:92–107
Lai Q, Shea CH, Wulf G, Wright DL. 2000. Optimizing generalized motor program and parameter learning.
Res. Q. Exerc. Sport 71:10–24
Lamb RL, Annetta L, Firestone J, Etopio E. 2018. A meta-analysis with examination of moderators of stu-
dent cognition, affect, and learning outcomes while using serious educational games, serious games, and
simulations. Comput. Hum. Behav. 80:158–67
Landers RN, Auer EM, Helms A, Marin S, Armstrong MB. 2019. Gamication of adult learning: gamifying
employee training and development. In The Cambridge Handbook of Technology and Employee Behavior,ed.
RN Landers, pp. 271–95. Cambridge, UK: Cambridge Univ. Press
Landers RN, Reddock CM. 2017. A meta-analytic investigation of objective learner control in web-based
instruction. J. Bus. Psychol. 32:455–78
Latham GP, Saari LM. 1979. Application of social-learning theory to training supervisors through behavioral
modeling. J. Appl. Psychol. 64:239–46 The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Libin A, Lauderdale M, Millo Y, Shamloo C, Spencer R, et al. 2010. Role-playing simulation as an educational
tool for health care personnel: developing an embedded assessment framework. Cyberpsychol. Behav. Soc.
Netw. 13:217–24
Littrell LN, Salas E, Hess KP, Paley M, Riedel S. 2006. Expatriate preparation: a critical analysis of 25 years
of cross-cultural training research. Hum. Res. Dev. Rev. 5:355–88
Ma W, Adesope OO, Nesbit JC, Liu Q. 2014. Intelligent tutoring systems and learning outcomes: a meta-
analysis. J. Educ. Psychol. 106:901–18
Marlow SL, Lacerenza CN, Reyes D, Salas E. 2017. The science and practice of simulation-based training in
organizations. In The Cambridge Handbook of Workplace Training and Employee Development, ed. KG Brown,
pp. 256–77. New York: Cambridge Univ. Press
Mattingly V, Kraiger K. 2019. Can emotional intelligence be trained? A meta-analytical investigation. Hum.
Res. Manag. Rev. 29:140–55
Mayer RE. 1979. Twenty years of research on advance organizers: assimilation theory is still the best predictor
of results. Instr. Sci. 8:133–67
Mayer RE. 2008. Applying the science of learning: evidence-based principles for the design of multimedia
instruction. Am. Psychol. 63:760–69
Mayer RE. 2011a. Applying the Science of Learning. Boston: Pearson
Mayer RE. 2011b. Multimedia learning and games. In Computer Games and Instruction, ed. S Tobias, JD
Fletcher, pp. 281–305. Greenwich, CT: Inf. Age
Mayer RE. 2019. Thirty years of research on online learning. Appl. Cogn. Psychol. 33:152–59
Mayer RE, Fiorella L. 2014. Principles for reducing extraneous processing in multimedia learning: coher-
ence, signaling, redundancy, spatial contiguity, and temporal contiguity. In The Cambridge Handbook of
Multimedia Learning, ed. RE Mayer, pp. 279–315. New York: Cambridge Univ. Press. 2nd ed.
McDermott PL, Gronowski MR, Carolan T. 2013. Adaptive training strategies: Considering the decision
process as well as the outcome. In Proceedings of the Interservice/Industry Training, Simulation, and Education
Conference (I/ITSEC), National Defense Industrial Association,Arlington, VA, Proc. Pap. 13127
Mead S, Fisk AD. 1998. Measuring skill acquisition and retention with an ATM simulator: the need for age-
specic training. Hum. Factors 40:516–23
Metcalfe J, Kornell N. 2005. A region of proximal learning model of study time allocation. J. Mem. Lang.
Metzler-Baddeley C, Baddeley RJ. 2009. Does adaptive training work? Appl. Cogn. Psychol. 23:254–66
Moore JP, Venters C, Carbonetto T. 2017. The retention and usefulness of concept maps as advance organiz-
ers. In Proceedings of the Annual ASEE Conference & Exposition,Columbus, OH, Proc. Pap. 18072
Moreno R. 2006. Learning in high-tech and multimedia environments. Cur r. D i r. Psy chol . S ci. 15(2):63–67
Morris MA, Robie C. 2001.A meta-analysis of the effects of cross-cultural training on expatriate performance
and adjustment. Int. J. Train. Dev. 5:112–25
Moulton CAE, Dubrowski A, MacRae H, Graham B, Grober E, Reznick R. 2006. Teaching surgical skills:
What kind of practice makes perfect? A randomized, controlled trial. Ann. Surg. 244:400–9
National Research Council. 2012. Improving Adult Literacy Instruction: Options for Practice and Research.
Washington, DC: Natl. Acad. Press
Noe RA, Clarke ADM, Klein HJ. 2014. Learning in the twenty-rst-century workplace. Annu. Rev. Organ.
Psychol. Organ. Behav. 1:245–75
Noe RA, Colquitt JA. 2002. Planning for training impact: principles of training effectiveness. In Creating,
Implementing, and Managing Effective Training and Development, ed. K Kraiger, pp. 53–79. San Francisco:
O’Connor P, Campbell J, Newton J, Melton J, Salas E, Wilson K. 2008. Crew resource management training
effectiveness: a meta-analysis and some critical needs. Int. J. Aviat. Psychol. 18:353–68
Pashler H, McDaniel M, Rohrer D, Bjork R. 2008. Learning styles: concepts and evidence. Psychol. Sci. Public
Interest 9(3):105–19
Plott B, McDermott PL, Archer S, Carolan TF, Hutchins S, et al. 2014. Understanding the impact of training on
performance. Tech. Rep. 1341, Res. Inst. Behav. Soc. Sci., US Army, Fort Belvoir, VA
Preacher KJ, Sterba SK. 2019. Aptitude-by-treatment interactions in research on educational interventions.
Except. Child. 85:248–64
. Kraiger Ford
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Preiss RW,Gayle BM. 2006.A meta-analysis of the educational benets of employing advanced organizers. In
Classroom Communication and Instructional Processes: Advances Through Meta-Analysis, ed. BM Gayle, RW
Preiss, N Burrell, M Allen, pp. 329–44. Mahwah, NJ: Lawrence Erlbaum
Quinn CN. 2018. Millennials, Goldsh & Other Training Misconceptions: Debunking Learning Myths and Supersti-
tions. Alexandria, VA: Assoc. Talent Dev.
Reiser BJ. 2004. Scaffolding complex learning: the mechanisms of structuring and problematizing student
work. J. Learn. Sci. 13:273–304
Roediger HL III, Butler AC 2011. The critical role of retrieval practice in long-term retention. Trends Cogn.
Sci. 15:20–27
Roediger HL III, Karpicke JD. 2006. The power of testing memory: basic research and implications for edu-
cational practice. Perspect.Psychol.Sci.1:181–210
Rynes SL, Giluk TL, Brown KG. 2007. The very separate worlds of academic and practitioner periodicals
in human resource management: implications for evidence-based management. Acad. Manag. J. 50:987–
Saks AM, Belcourt M. 2006. An investigation of training activities and transfer of training in organizations.
Hum. Res. Manag. 45:629–48
Salas E, Cannon-Bowers JA.2001. The science of training: a decade of progress.Annu. Rev. Psychol. 52:471–99
Salas E, Prince C, Bowers CA, Stout RJ, Oser RL, Cannon-Bowers JA. 1999. A methodology for enhancing
crew resource management training. Hum. Factors 41:161–72
Salas E, Tannenbaum SI, Kraiger K, Smith-Jentsch KA. 2012. The science of training and development in
organizations: what matters in practice. Psychol. Sci. Public Interest 13(2):74–101
Shea CH, Wulf G. 2005. Schema theory: a critical appraisal and reevaluation. J. Mot. Behav. 37:85–101
Shute VJ. 2008. Focus on formative feedback. Rev. Educ. Res. 78:153–89
Sitzmann T.2011. A meta-analytic examination of the instructional effectiveness of computer-based simulation
games. Pers. Psychol . 64:489–528
Sitzmann T, Bell BS, Kraiger K, Kanar AM. 2009. A multilevel analysis of the effect of prompting self-
regulation in technology-delivered instruction. Pers. Psychol. 62:697–734
Sitzmann T, Ely K. 2011. A meta-analysis of self-regulated learning in work-related training and educational
attainment: what we know and where we need to go. Psychol. Bull. 137:421–42
Sitzmann T, Kraiger K, Stewart D, Wisher R. 2006. The comparative effectiveness of web-based and classroom
instruction: a meta-analysis. Pers. Psychol. 59:623–64
Stone CL. 1983. A meta-analysis of advance organizer studies. J. Exp. Educ. 51:194–99
Surface AE, Kraiger K. 2018.Two fundamental questions L&D stakeholders should answer to improve learn-
ing. Train. Ind. Mag. 12(6):36–38
Susi T, Johannesson M, Backlund P. 2007. Serious games—an overview. Tech. Rep. HS-IKI-TR-07–001, Sch.
Hum. Inf., Univ. Skövde, Skövde, Sweden
Taylor PJ, Russ-Eft DF, Chan DWL. 2005. A meta-analytic review of behavior modeling training. J. Appl.
Psychol. 90:692–709
Thorndike EL, Woodworth RS. 1901. The inuence of improvement in one mental function upon the ef-
ciency of other functions. (I). Psychol. Rev. 8:247–61
Truxillo DM, Cadiz DM, Hammer LB. 2015. Supporting the aging workforce: a review and recommendations
for workplace intervention research. Annu. Rev. Organ. Psychol. Organ. Behav. 2:351–81
van de Ridder JM, McGaghie WC, Stokking KM, ten Cate OT. 2015. Variables that affect the process and
outcome of feedback, relevant for medical training: a meta-review. Med. Educ. 49:658–73
Van der Kleij FM, Feskens RC, Eggen TJ. 2015. Effects of feedback in a computer-based learning environment
on students’ learning outcomes: a meta-analysis. Rev. Educ. Res. 85:475–511
Van Gerven PWM, Paas F, Tabbers HK. 2006. Cognitive aging and computer-based instructional design:
Where do we go from here? Educ. Psychol. Rev. 18:141–57
Wang X, Day EA, Kowollik V, Schuelke MJ, Hughes MG. 2013. Factors inuencing knowledge and skill decay
after training. In Individual and Team Skill Decay: The Science and Implications for Practice,ed.WArthurJr.,
EA Day, W Bennett Jr., AM Portrey, pp. 68–116. New York: Routledge
Wickens CD, Hutchins S, Carolan T, Cumming J. 2013. Effectiveness of part-task training and increasing-
difculty training strategies: a meta-analysis approach. Hum. Factors 55:461–70 The Science of Workplace Instruction .
OP08CH04_Kraiger ARjats.cls October 20, 2020 16:59
Wolfson MA, Tannenbaum SI, Mathieu JE, Maynard MT. 2018. A cross-level investigation of informal eld-
based learning and performance improvements. J. Appl. Psychol. 103:14–37
Wolfson NE, Cavanagh TM, Kraiger K. 2014. Older adults and technology-based instruction: optimizing
learning outcomes and transfer. Acad. Manag. Learn. Educ. 13:26–44
Wolfson NE,Kraiger K. 2014. Cognitive aging and training: the role of instructional coherence and advance
organizers. Exp. Aging Res. 40:164–86
Wulf G, Shea CH. 2002. Principles derived from the study of simple skills do not generalize to complex skill
learning. Psychon. Bull. Rev. 9:185–211
. Kraiger Ford
... However, we see value in using organizational psychology theories of self-regulation, learning, and development to inform why, when, and how individuals engage in CBL in a rewarding and safe manner. To do so, we draw on theories of learning and development (Beier, 2021;Hall & Mirvis, 1995;Kraiger & Ford, 2021;Maurer, 2002;Mayer, 2008), self-regulation (Beal et al., 2005;Kanfer & Ackerman, 1989;Lord et al., 2010), multiple goal pursuit (Schmidt & DeShon, 2007;Vancouver et al., 2010), and multiple resources (Wickens, 2002) to identify the nature, antecedents, and outcomes of CBL. This multidisciplinary review is informed by advances in learning theory and practice, which is increasingly self-guided, informal, broad, and technology-supported (Ackerman & Kanfer, 2020;Sitzmann & Ely, 2011;Tannenbaum & Wolfson, 2022), as well as our systematic review of the transportation literature to identify relevant activities and commuting characteristics. ...
... As defined above, Commute-Based Learning (CBL) refers to the mental events and activities that support the development of new knowledge and skills with the aim of developing professional expertise and personal interests. Our definition builds on Kraiger and Ford's (2021) summary view from the cognitive, educational, and I-O psychology perspectives that learning is multidimensional, useful, and relatively permanent. Yet, CBL is also distinct from other forms of learning such as informal field-based learning (Tannenbaum & Wolfson, 2022) or broader self-regulated learning (Sitzmann & Ely, 2011) in key ways. ...
... Third and finally, the process of CBL is selfregulated. Our definition highlights the mental events and activities engaged in CBL, including encoding, organizing, and retrieval (Kraiger & Ford, 2021). Mayer (2008) introduces these core learning processes as sequential in three steps: (1) selection, (2) organization, and (3) integration. ...
Though commuting is often seen as a source of stress, commuters may take advantage of travel time to pursue learning and developmental goals—a concept we refer to as Commute-Based Learning (CBL). We draw on self-regulation and learning and development theories to define CBL in terms of its context, content, and process and present the findings of a systematic review of multitasking activities in the transportation literature. This review demonstrates that (a) travel-based activities related to learning are increasingly prevalent, (b) people enjoy being productive during their commutes, and (c) commute mode and environmental characteristics impact multitasking and evaluations of the commute itself. We then integrate these review findings with psychological theories to propose a framework specifying the predictors of CBL, its benefits, and drawbacks, and the commute mode's moderating influence. These efforts yield several practical implications and future research directions to increase CBL's potential benefits while reducing potential harm. Plain Language Summary Although commuting can often be a source of stress, commuters may also take advantage of travel time to pursue learning and developmental goals—a concept we refer to as Commute-Based Learning (CBL).To emphasize the potential benefits and pitfalls of CBL, we draw on learning and development, self-regulation, multiple-goal pursuit, and multiple resource theories to define CBL in terms of its context (where it occurs), content (what it includes), and process (how it is done). Next, we present the findings of a systematic review ofresearch on multitasking activities in the transportation literature to gather evidence of the learning activities that commuters may pursue.This review demonstrates that (a) travel-based activities related to learning are increasingly prevalent, (b) people enjoy being productive during their commutes, and (c)commute mode and environmental characteristics impact multitasking and evaluations of the commute itself. We then integrate these findings from the transportation literature with psychological theories to propose a framework specifying the predictors of CBL (individual and organizational), its benefits (expertise, need satisfaction and well-being) and drawbacks (depletion, reduced safety), and the critical role of commute mode as a moderator of these relationships. Our definition and framework of CBL inform practical implications for improving the benefits of learning during the commute, while mitigating its potential drawbacks. We also present suggested directions for future multidisciplinary research. We hope this review provides insight into the state of the literature on commute-based learning and a clear research agenda for advancement in this broadly important, yet underdeveloped area.
... Selanjutnya, menurut Kraiger & Ford (2021) dasar dalam proses pelatihan adalah penyampaian informasi dan keterampilan tertentu dari sekelompok orang peserta pelatihan melalui metode kontak pelatihan. Tujuan proses pelatihan adalah untuk memberikan pengetahuan, keterampilan, dan mempengaruhi perilaku individu untuk meningkatkan tingkat produktivitas di tempat kerja (Kraiger & Ford, 2021). ...
... Selanjutnya, menurut Kraiger & Ford (2021) dasar dalam proses pelatihan adalah penyampaian informasi dan keterampilan tertentu dari sekelompok orang peserta pelatihan melalui metode kontak pelatihan. Tujuan proses pelatihan adalah untuk memberikan pengetahuan, keterampilan, dan mempengaruhi perilaku individu untuk meningkatkan tingkat produktivitas di tempat kerja (Kraiger & Ford, 2021). Selain itu, menurut Al-qout (2017) pelatihan memiliki beberapa efek pada kinerja karyawan seperti meningkatkan kinerja karyawan, memperkuat hubungan karyawan, meningkatkan sikap karyawan, pengembangan rasa memiliki, loyalitas terhadap organisasi, mengurangi tingkat ketidakhadiran, dan pergantian karyawan. ...
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This study aims to determine the effect of training and work motivation on employee performance at Perum Damri Yogyakarta Branch Office. This type of research uses quantitative methods using the Statistical Package for the Social Sciences (SPSS) program. Methods of obtaining data using questionnaires and interviews. The sample used was 48 employees of the Perum Damri Yogyakarta Branch Office. The tests used are validity tests, reliability tests, multiple linear regression analysis, and statistical tests. Based on the results of statistical tests, training and work motivation positively influence the performance of employees of the Perum Damri Yogyakarta Branch Office.
... Organizational training and employee development is an important HR practice focused on providing employees the skills to do their jobs and to envision future roles associated with continuous career development; organizational training and development practices are also critical activities by which organizations invest in, and retain, talented employees (see Kraiger & Ford, 2021 for a recent review). In addition to organizationally focused training and development programs aimed at providing skills and retaining talent, a person-centric focus on training and development typically supports an individual's desire to increase their own skills to either remain productive in the same/similar role or to enable a change to a new role. ...
... Despite the importance of organizational training and employee development, research on this topic in general has actually decreased in recent years (Kraiger & Ford, 2021) and thus age-focused research in this area is even less likely. Organizations, however, are continuing to develop new approaches to both organizational training and individual instruction that capitalize on new technologies and the eagerness of workers to update their knowledge and skills. ...
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As populations in the US and around the world continue to age, it has become increasingly important to understand how organizations can create working conditions that attract, support, and retain workers across the lifespan. In this paper, we provide a primer on current theory and research on age in the workplace. We briefly describe lifespan theories that have guided recent advances in the field, discuss the implications of these theories for an aging workforce, and provide an overview of current research streams that address the work and non‐work factors affecting performance, well‐being, and workforce participation among mature workers. Based on this review, we provide recommendations for future research and for practice. This article is protected by copyright. All rights reserved
... Weitere Informationen beispielsweise zu lerntransferförderlichen Faktoren des Arbeitsumfelds und der Person sind bei Mehner und Kauffeld (2022) einsehbar.Die Unterstützung der Kompetenzentwicklung der Schulungsteilnehmenden muss nicht mit Abschluss der Schulung enden, sondern kann anschließend niederschwellig in den jeweiligen Unternehmen fortgeführt werden. Im Projekt IN-DIG-O wird daher ein digitales Tool -das LeWiT-Tool -entwickelt, das den Lerntransfer nach Schulungen in den Arbeitsalltag und die Weitergabe neuen Wissens im Kollegium begleitet.Technologien, die stetig neue Prozesse bedingen, wie beispielsweise BIM, das Prozesse ermöglicht, die stark vom traditionellen Vorgehen im Baugewerbe abweichen, erfordern von Unternehmen und ihre Mitarbeitenden kontinuierliches Lernen, um sich an die Veränderungen im Markt und den technologischen Fortschritt anzupassen(Cascio & Montealegre, 2016;Kraiger & Ford, 2021). In den letzten Jahrzehnten haben sich daher entwickelt, das bei der Verbindung von formellem mit informellem Lernen ansetzt, indem es nach Weiterbildungen den Lerntransfer und die Weitergabe von Wissen im Kollegium unterstützt. ...
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Zusammenfassung Das Baugewerbe wird – wenn auch langsam – digitalisiert (Telekom, 2020; Thonipara, Höhle, Proeger & Bizer, 2020). Betroffen sind Prozesse im Gebäudebau, bei denen mehrere Gewerke involviert sind. So sollen diese Prozesse beispielsweise durch Building Information Modeling (BIM) präziser, akkurater und schneller werden (Jacobsson & Merschbrock, 2018). Kleine und mittlere Unternehmen (KMU) verfügen jedoch häufig nicht über die benötigten Ressourcen und Kompetenzen, um adäquat an dieser Entwicklung zu partizipieren. Das Projekt IN-DIG-O unterstützt KMU zweifach: Es stellt mit dem digitalen Tool namens Koop-3D eine Ressource zur interdisziplinären Zusammenarbeit in der Planungs- und Ausführungsphase im Hausbau zur Verfügung, die sich an BIM orientiert und von Subunternehmen keine IT-Kenntnisse zur 3D-Planung erfordert. Parallel begleitet ein zweites digitales Tool – das LeWiT-Tool – gezielt die Optimierung des Kompetenzerwerbs der Mitarbeitenden von KMU. Mittels der Grundprinzipien der entwicklungsorientierten Evaluation (Kauffeld & Paulsen, 2018) fördert es eine offene Feedbackkultur sowie selbstbestimmte Zielsetzungen. Dadurch wird der Lerntransfer nach Weiterbildungen in den Arbeitsalltag und die Weitergabe des neuen Wissens an das Kollegium verbessert. Der vorliegende Beitrag gibt einen Einblick in die beiden digitalen Tools, stellt Evaluationsergebnisse vor und zieht Learnings aus der Entwicklungs- und Erprobungsphase der digitalen Tools.
... One possible reason coaching is so effective is because it is inherently aligned with many of the most enduring findings in psychology and behavioral science more broadly. 6 As an example, return to our metaphor of giving a man a fish versus teaching him to fish so that he can eat forever. If the man can show up every day and get his meal handed to him, does it matter that he doesn't know how to fish for himself? ...
Purpose Research has established that 38%, 56% and 66% of training is not transferred to work immediately, six months and 12 months after training, respectively. This has led scholars to advocate the continuous examination of factors that enhance training transfer to have a comprehensive understanding of the factors that enhance it. As a result, this study aims to examine transfer opportunity as a pretraining factor and its influence on assimilated training content (in-training factor); the influence of assimilated training content on motivation to transfer (post-training factor) and training transfer; the influence of motivation to transfer on training transfer; and the mediating role of motivation to transfer in the relationship between assimilated training content and training transfer. Design/methodology/approach A structural equation model is developed to test the five hypotheses formulated in this study using survey data obtained from 195 respondents who attended various training programs across different organizations. Following the assessment of the measurement model, the determination of the significance of the hypothesized paths is assessed based on the bias-corrected and accelerated confidence intervals obtained from the bootstrapping of 10,000 subsamples. Findings The findings of this study are that: transfer opportunity positively influences assimilated training content; assimilated training content positively influences motivation to transfer and training transfer; motivation to transfer positively influences training transfer; and motivation to transfer plays a complementary mediation role between assimilated training content and training transfer. Practical implications The nature of the work environment regarding the opportunity to transfer training influences trainees’ assimilation of the training content when they undergo training. Hence, organizations need to ensure that employees are always afforded the opportunity to transfer training content assimilated from previously attended training programs to assimilate the content of subsequent training programs. Furthermore, for training to culminate in training transfer, organizations and, more specifically, learning and development practitioners ought to pay attention to trainees’ assimilation of the content of training programs. Originality/value To the best of the authors’ knowledge, this is the first study to empirically consider transfer opportunity as a direct antecedent of assimilated training content. More so, it is one of few studies to empirically examine the influence of assimilated training content on training transfer.
Given its acceptance and value as an important facet of workplace behavior, research has primarily attempted to understand adaptive performance by way of examining its antecedents. Although useful, these findings provide little insight into the in-situ, intra-individual processes that occur during adaptive performance (i.e., How do people adapt to change? What determines the speed at which people adapt? How do failures to adapt occur?). The current paper develops and presents a process model of adaptation in order to provide a framework for organizing, understanding, and investigating the in-situ process involved when individuals adapt to changes in job demands. In particular, we suggest that in order to successfully adapt to a changing task environment, individuals must go through a series of processes in order to detect the nature of a change, diagnose its cause, develop or refine strategies, learn additional knowledge or skills, and enact appropriate performance behaviors. At the same time, dynamic emotional, cognitive, motivational, and situational factors serve as proximal inputs and outputs of these processes. In doing so, they shape the success and speed with which people adapt and suggest a broadened set of outcomes of adaptive performance. We describe how this model can be leveraged to stimulate dynamic adaptive performance research and to promote adaptive performance in applied settings.
Zusammenfassung Digitale Assistenzsysteme stellen Beschäftigten aufgabenbezogene Informationen in ihren Arbeitsprozessen über tragbare Endgeräte wie beispielsweise Datenbrillen bereit. Durch eine Personalisierung können diese Systeme dazu befähigt werden, sich an den individuellen Arbeitsfortschritt und Wissensstand der Beschäftigten anzupassen. Die technischen Komponenten eines Assistenzsystems stellen hierzu dann nicht nur die Assistenz bereit, sondern sammeln auch personenbezogene Daten, um die Personalisierung zu ermöglichen. Die Personalisierung erfordert jedoch auch einen adäquaten Umgang mit diesen sensiblen Daten, um die Akzeptanz der Assistenzsysteme durch die Beschäftigten sowie einen rechtskonformen Einsatz der Assistenzsysteme überhaupt zu gewährleisten. Um die Akzeptanz und Rechtskonformität sicherzustellen, wurden im PersonA-Projekt insgesamt 19 Design-Prinzipien entwickelt und evaluiert, die bei der Entwicklung von personalisierten Assistenzsystemen in Produktion und Service berücksichtigt werden sollten. Diese gliedern sich in Design-Prinzipien zu generellen Funktionen von Assistenzsystemen, zur Personalisierung von Assistenzsystemen und zur Datenerhebung und -verwendung von Assistenzsystemen. Ausgehend von diesen Design-Prinzipien wurden Funktionen für ein Privatsphäre-Management für das bestehende Lösungsportfolio von TeamViewer Germany GmbH entwickelt und in Pilotprojekten bei den Anwendungspartnern Kemper GmbH und WS System GmbH zum Einsatz gebracht. Vorher-Nachher-Betrachtungen der Prozesse und Kennzahlen bei den Anwendungspartnern zeigen bemerkenswerte Verbesserungen. Darüber hinaus wurden organisationskulturelle Aspekte bei der Einführung und Nutzung der technischen Lösungen bei den Anwendungspartnern durch die nextpractice GmbH untersucht. Aus den Pilotprojekten ergaben sich praktische Handlungsempfehlungen, die auch für weitere Unternehmen eine Orientierung für ihre eigenen Projekte zur Einführung von personalisierten Assistenzsystemen bieten können.
In diesem Beitrag werden die Ergebnisse des DigiKomp-Ambulant Verbundprojektes (BMBF und ESF-Förderung, 01.07.2019 – 30.06.2022, FKZ: 02L 17C580 – 585) im Zusammenhang dargestellt – aber auch die Schwerpunkte der einzelnen Teilprojekte von den entsprechenden Partnern erläutert. In diesem Verbundprojekt konnte gezeigt werden, dass und wie eine partizipative Technikentwicklung zum Vorteil der späteren Nutzer/innen wie auch der Entwickler/innen realisiert werden kann. So wurde im DigiKomp-Ambulant Projekt nicht nur ein marktnaher Technikdemonstrator in Form einer Sensormatte zum Einsatz im ambulanten Pflegedienst realisiert, sondern es wurde auch ein Vorgehensmodell für eine intensive Nutzerbeteiligung im Technikentwicklungsprozess entwickelt, erprobt und evaluiert. Es konnte gezeigt werden, dass eine ergänzende Beteiligungsqualifizierung die Partizipationsfähigkeit und -motivation der ambulanten Pflegekräfte verbessert. Die frühzeitige Erarbeitung marktfähiger Geschäftsmodelle verbessert Motivation und Perspektiven auf der Entwickler- und Produzentenseite der neuen Technologie. Ein pflegespezifisches Technologieakzeptanzmodell wurde entwickelt und validiert.
While most participants benefit from action-oriented entrepreneurship training, such programs can paradoxically also have negative effects. Training programs in which participants actively engage in entrepreneurship involve facing problems that might be too difficult to overcome, potentially decreasing trainees' entrepreneurial self-efficacy. Based on theories of self-regulation, we argue that error mastery orientation is a factor that explains under which condition problems do or do not lead to decreases in entrepreneurial self-efficacy during training. To test our model, we conducted a 12-week action-oriented training program and applied a longitudinal design with one baseline measurement, seven measurements during training, and one measurement after training. Analyses based on 415 lagged observations from 109 training participants indicated that participants with low error mastery orientation experienced decreases in entrepreneurial self-efficacy during training when facing problems. In contrast, participants high in error mastery orientation could buffer the negative effects of problems on entrepreneurial self-efficacy. Our results suggest that error mastery orientation is a critical factor to understand why participants' episodic experiences of problems during training negatively influence their entrepreneurial self-efficacy. Shedding light on these self-regulatory factors advances the understanding of the potential dark side of action-oriented entrepreneurship training.
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Objective The objective of this meta-analysis is to explore the presently available, empirical findings on transfer of training from virtual (VR), augmented (AR), and mixed reality (MR) and determine whether such extended reality (XR)-based training is as effective as traditional training methods. Background MR, VR, and AR have already been used as training tools in a variety of domains. However, the question of whether or not these manipulations are effective for training has not been quantitatively and conclusively answered. Evidence shows that, while extended realities can often be time-saving and cost-saving training mechanisms, their efficacy as training tools has been debated. Method The current body of literature was examined and all qualifying articles pertaining to transfer of training from MR, VR, and AR were included in the meta-analysis. Effect sizes were calculated to determine the effects that XR-based factors, trainee-based factors, and task-based factors had on performance measures after XR-based training. Results Results showed that training in XR does not express a different outcome than training in a nonsimulated, control environment. It is equally effective at enhancing performance. Conclusion Across numerous studies in multiple fields, extended realities are as effective of a training mechanism as the commonly accepted methods. The value of XR then lies in providing training in circumstances, which exclude traditional methods, such as situations when danger or cost may make traditional training impossible.
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An interleaved presentation of items (as opposed to a blocked presentation) has been proposed to foster inductive learning (interleaving effect). A meta-analysis of the interleaving effect (based on 59 studies with 238 effect sizes nested in 158 samples) was conducted to quantify the magnitude of the interleaving effect, to test its generalizability across different settings and learning materials, and to examine moderators that could augment the theoretical models of interleaved learning. A multilevel meta-analysis revealed a moderate overall interleaving effect (Hedges’ g = 0.42). Interleaved practice was best for studies using paintings (g = 0.67) and other visual materials. Results for studies using mathematical tasks revealed a small interleaving effect (g = 0.34), whereas results for expository texts and tastes were ambiguous with nonsignificant overall effects. An advantage of blocking compared to interleaving was found for studies based on words (g = -0.39). A multiple meta-regression analysis revealed stronger interleaving effects for learning material more similar between categories, for learning material less similar within categories, and for more complex learning material. These results are consistent with the theoretical account of interleaved learning, most notably with the sequential theory of attention (attentional bias framework). We conclude that interleaving can effectively foster inductive learning but that the setting and the type of learning material must be considered. The interleaved learning, however, should be used with caution in certain conditions, especially for expository texts and words.
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Gamification is now commonly used in adult learning contexts, but its effects remain unclear. This has happened in part because of the initially trendy and faddish nature of gamification leading to high rates of adoption without significant critical evaluation. This was most problematic in the years leading up to peak hype in 2013, at which point “gamification” was used as a catchall faddish buzzword that did not refer to any particular construct or approach, instead being used primarily as a marketing strategy. Since then, as gamification has been explored in the academic research literature, these broader problems have been largely addressed via carefully considered theoretical and empirical studies. Nevertheless, empirical work in particular is still relatively sparse; the last major published summary of such work only identified nine empirical gamification studies in the learning context as of 2014. Since then, the literature has grown, but there are still many unanswered questions among both gamification practitioners and academics. Among non-specialists, there is still substantial construct confusion stemming directly from gamification’s initially faddish definition. Given this, the purpose of this chapter is threefold. First, we define gamification and provide a comprehensive introduction to it, contrasting it with existing approaches. Second, we explore which theories describe its known and potential effects. Third, we provide extensive practical literature-driven recommendations for those seeking to gamify training themselves.
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The impact on learning outcome of tailoring instruction and teaching toward modality-specific learning style preferences has been researched and debated for decades. Several topical reviews have concluded that there is no evidence to support the meshing hypothesis and that it represents a persistent neuromyth in education. The concept, however, is still utilized in educational practice and favored by many academics. This mini-review presents literature, which has applied explicit and rigorous methodological criteria, in relation to the meshing hypothesis. In order to demonstrate evidence for the meshing hypothesis, studies had to screen participants for their preferred learning style, assign participants to matched or non-matched conditions, and then provide the same test to assess learning for all participants, as well as presenting statistical crossover-interaction effects. Across studies that have applied these methodological criteria, the overall effect sizes were very low and non-significant, indicating that there is still no replicable statistical evidence for enhanced learning outcome by aligning instruction to modality-specific learning styles.
The purposes of the present investigation were to attempt to replicate the negative effects for learning prompts in older adults reported by Cavanagh, Kraiger and Peters (2016), determine if the impact of learning prompts depends on type of prompt, and investigate the two possible explanations of the negative impact of prompts – increased cognitive load and higher negative affect. Learning prompts refer to short text inserted into training content to encourage trainees to rehearse new content or engage in meta‐cognitive activity. Although learning prompts generally lead to greater learning in training, Cavanagh et al. reported a negative impact for prompts on measures of recall and transfer. Using similar training materials and learning outcome measures, we conducted two studies using both elaboration and meta‐cognitive prompts in online training. In the first, older adults (N = 194) between 55 and 70 years and younger adults (N = 218) were randomly assigned to either a meta‐cognitive, elaboration or no prompts (control) condition. Older adults learned less and reported greater mental effort than younger learners, but these effects were not moderated by prompt condition. In the second, N = 57 older adults were randomly assigned to the same three prompts conditions listed above. Older adults learned less with prompts than without, but there were no differences between conditions in mental effort or negative affect. In sum, we found negative effects for learning prompts in older adults in two studies, but found no evidence to suggest that these effects were due to either increased cognitive load or greater negative affect.
The Cambridge Handbook of Technology and Employee Behavior - edited by Richard N. Landers February 2019
A common theme uniting articles in this special issue is a focus on aptitude-by-treatment interactions (ATIs). This timely and welcome focus allows the field to synthesize current substantive findings on ATIs in educational intervention research in both reading and math domains. In this methodological commentary, we begin by reviewing traditional approaches for detecting and reporting interactions in single-level and multilevel models. Next, we discuss some limitations of traditional approaches for theorizing about and modeling ATIs, and we suggest some solutions. These solutions include interpreting level-specific (unconflated) ATIs, understanding and ameliorating threats to adequate power for detecting ATIs, expanding focus beyond linear ATIs, and increasing the number of measurement occasions beyond two to allow use of a growth modeling framework for investigating ATIs. Incorporating some of these advances into future research can motivate new research questions about educational interventions and lead to new discoveries in the search for ATIs.
This paper presents a personal account of developments in research on online learning over the past 30 years. Research on how to design online instruction represents an example of applying the science of learning to education. It contributes to the science of learning (as exemplified by developments in cognitive load theory, the cognitive theory of multimedia learning, and incorporating metacognitive, motivational, and affective aspects of learning), the science of instruction (as exemplified by the continuing development of research‐based principles of instructional design), and the science of assessment (as exemplified by supplementing self‐report surveys and retention tests with multi‐level transfer tests, log file data during learning, and cognitive neuroscience measures of cognitive processing during learning). Some recurring themes are that learning is caused by instructional methods rather than instructional media, so research on should focus on features that are uniquely afforded by digital learning environments; practice should be grounded in rigorous and systematic research, including value‐added experiments aimed at pinpointing the active ingredients in online instruction; research in online learning should identify boundary conditions under which instructional techniques are most effective; and research in online learning should test and contribute to learning theory.