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Ten Steps to Complex Learning A New Approach to Instruction and Instructional Design


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Kirschner, P. A., & Van Merriënboer, J. J. G. (2008). Ten steps to complex learning: A new approach to instruction and instructional design. In T. L. Good (Ed.), 21st century education: A reference handbook (pp. 244-253). Thousand Oaks, CA: Sage.
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ten stePs to ComPlex learning
A New Approach to Instruction and Instructional Design
Paul kirsChner
Utrecht University
Jeroen J. g. Van merriënboer
Open University of the Netherlands
he subject of this chapter, ten steps to complex
learning (van Merriënboer & Kirschner, 2007),
was recently published as a practical and modified
version of the four-component instructional design (4C-ID)
model originally posited by van Merriënboer in 1997.
These ten steps are mainly prescriptive and aim to provide
a practicable version of the 4C-ID model for teachers,
domain experts involved in educational or training design,
and less experienced instructional designers. The model
described here will typically be used to develop educa-
tional or training programs, which can have a duration
ranging from several weeks to several years, aimed at the
acquisition of complex cognitive skills (in this chapter
referred to as complex learning).
Complex Learning
Complex learning is the integration of knowledge, skills
and attitudes; coordinating qualitatively different constitu-
ent skills; and often transferring what was learned in school
or training to daily life and work. There are many examples
of theoretical design models that have been developed to
promote complex learning: cognitive apprenticeship (Col-
lins, Brown, & Newman, 1989), 4-Mat (McCarthy, 1996),
instructional episodes (Andre, 1997), collaborative problem
solving (Nelson, 1999), constructivism and constructivist
learning environments (Jonassen, 1999), learning by doing
(Schank, Berman, & MacPerson, 1999), multiple
approaches to understanding (Gardner, 1999), star legacy
Schwartz, Lin, Brophy, & Bransford, 1999), as well as the
subject of this contribution, the Four-Component Instruc-
tional Design model (van Merriënboer, 1997; van
Merriënboer, Clark, & de Croock, 2002). These approaches
all focus on authentic learning tasks as the driving force for
teaching and learning because such tasks are instrumental
in helping learners to integrate knowledge, skills, and atti-
tudes (often referred to as competences), stimulate the
coordination of skills constituent to solving problems or
carrying out tasks, and facilitate the transfer of what has
been learned to new and often unique tasks and problem
situations (Merrill, 2002b; van Merriënboer, 2007; van
Merriënboer & Kirschner, 2001).
Though the first two goals are essential for education
and training and should not be underestimated, the funda-
mental problem facing instructional designers is education
and training’s apparent inability to achieve the third goal,
the transfer of learning. Instructional design (ID) theory
needs to support the design and development of programs
that will help students acquire and transfer professional
competencies or complex cognitive skills to an increas-
ingly varied set of real-world contexts and settings. The
Ten Steps to Complex Learning approach to ID (van Mer-
riënboer & Kirschner, 2007) claims that a new ID approach
is needed to reach this goal. In the next section, this holis-
tic design approach is presented.
Ten Steps to Complex Learning: A New Approach to Instruction and Instructional Design • 245
Holistic Design
Holistic design is the opposite of atomistic design where
complex contents and tasks are usually reduced to their
simplest or smallest elements. This reduction is such that
contents and tasks are continually reduced to a level
where they can easily be transferred to learners through a
combination of presentation (i.e., expository teaching)
and practice. This approach works very well if there are
few interactions between those elements, but often fails
when the elements are closely interrelated because here
the whole is much more than the sum of its separate parts.
Holistic design approaches to learning deal with com-
plexity without losing sight of the separate elements and
the interconnections between them. Using such an
approach solves three common problems in education,
namely, compartmentalization, fragmentation, and the
transfer paradox.
ID models usually focus on one particular domain of
learning (i.e., cognitive, affective, psychomotor) and within
that domain between models for declarative learning that
emphasize instructional methods for constructing concep-
tual knowledge and models for procedural learning that
emphasize methods for acquiring procedural skills. This
compartmentalization—the separation of a whole into dis-
tinct parts or categories—has had negative effects in
vocational and professional education.
Any good practitioner has highly developed cognitive
and technical skills, a deep knowledge of the work domain,
a good attitude toward that work, and keeps all of this
up-to-date. In other words, these different aspects of pro-
fessional competencies cannot be compartmentalized into
atomistic domains of learning. To counter this compart-
mentalization, holistic design integrates declarative,
procedural, and affective learning to facilitate the develop-
ment of an integrated knowledge base that increases the
chance of transfer.
Most, if not all, ID models are guilty of fragmenta-
tion—the act or process of breaking something down into
small, incomplete, or isolated parts—as their basis (see
Ragan & Smith, 1996; van Merriënboer & van Dijk, 1998).
Typically they begin by analyzing a chosen learning
domain. They then divide it into distinct learning or perfor-
mance objectives (e.g., recalling a fact, applying a
procedure, understanding a concept), and then they select
different instructional methods for reaching each of the
separate objectives (e.g., rote learning, skills labs, problem
solving). For complex skills, each objective corresponds
with one subskill or constituent skill, and their sequencing
results in part-task sequences. The learner is taught only
one or a very limited number of constituent skills at the
same time, and new constituent skills are gradually added
until—at the end of the instruction—the learner practices
the whole complex skill.
The problem here is that most complex skills are char-
acterized by numerous interactions between the different
aspects of task performance with very high demands on
their coordination. Learning and instruction that is based
upon such fragmentation of complex tasks into sets of dis-
tinct elements without taking their interactions and required
coordination into account fails because learners ultimately
cannot integrate and coordinate the separate elements in
transfer situations (Clark & Estes, 1999; Perkins &
Grotzer, 1997; Spector & Anderson, 2000; Wightman &
Lintern, 1985). To remedy this, holistic design focuses on
highly integrated sets of objectives and their coordinated
attainment in real-life performance.
The Transfer Paradox
Instructional designers often either strive for or are
required to achieve efficiency. To this end they usually
select methods that will minimize the (1) number of prac-
tice items required, (2) time spent on task, and (3) learners’
investment of effort to achieve the learning objectives.
Typical here is the situation in which students must learn
to diagnose different types of technical errors (e.g., e1, e2,
e3). If a minimum of three practice items is needed to learn
to diagnose each error, the designer will often choose to
first train students to diagnose e1, then e2, and finally e3,
leading to the following learning sequence: e1, e1, e1, e2,
e2, e2, e3, e3, e3.
Although this sequencing will probably be very effi-
cient, it yields low transfer of learning because it encourages
learners to construct highly specific knowledge for diag-
nosing each distinct error, only allowing them to perform
in the way specified in the objectives. If a designer aims at
transfer, and with the objective to train students to diag-
nose as many errors as possible, then it would be better to
train students to diagnose the three errors in a random
order leading, for example, to a different sequence such as
e3, e2, e2, e1, e3, e3, e1, e2, e1.
This sequence will probably be less efficient for reach-
ing the isolated objectives, because it will probably
increase the needed time-on-task or investment of learner
effort and might even require more than three practice
items to reach the same level of performance for each
separate objective as the first sequence. In the long run,
however, it will help learners achieve a higher transfer of
learning because it encourages them to construct general
and abstract knowledge rather than knowledge only
related to each concrete, specific error and will thus allow
learners to better diagnose new, not yet encountered,
errors. This is the transfer paradox (van Merriënboer &
de Croock, 1997), where methods that work best for
reaching isolated, specific objectives are not best for
reaching integrated objectives and transfer of learning.
Holistic design takes this into account, ensuring that
students confronted with new problems not only have
acquired specific knowledge to perform the familiar
aspects of a task, but also have acquired the necessary
general or abstract knowledge to deal with the unfamiliar
aspects of those tasks.
Four Components and Ten Steps
The Ten Steps (van Merriënboer & Kirschner, 2007) is a
prescriptive approach to the Four-Component Instructional
Design model (4C-ID; van Merriënboer, 1997) that is prac-
ticable for teachers, domain experts involved in ID, and
instructional designers. It will typically be used for devel-
oping substantial learning or training programs ranging in
length from several weeks to several years or that entail a
substantial part of a curriculum for the development of
competencies or complex skills. Its basic assumption is
that blueprints for complex learning can always be
described by four basic components: learning tasks, sup-
portive information, procedural information, and part-task
practice (see Table 26.1).
The term learning task is used here generically to
include case studies, projects, problems, and so forth.
They are authentic whole-task experiences based on real-
life tasks that aim at the integration of skills, knowledge,
and attitudes. The whole set of learning tasks exhibits a
high variability, is organized in easy-to-difficult task
classes, and has diminishing learner support throughout
each task class.
Supportive information helps students learn to perform
nonroutine aspects of learning tasks, which often involve
problem solving and reasoning. It explains how a domain
is organized and how problems in that domain are (or
should be) approached. It is specified per task class and is
always available to learners. It provides a bridge between
what learners already know and what they need to know to
work on the learning tasks.
Procedural information allows students to learn to per-
form routine aspects of learning tasks that are always
performed in the same way. It specifies exactly how to
perform the routine aspects of the task and is best pre-
sented just in time—precisely when learners need it. It
quickly fades as learners gain more expertise.
Finally, part-task practice pertains to additional prac-
tice of routine aspects so that learners can develop a very
high level of automaticity. Part-task practice typically pro-
vides huge amounts of repetition and only starts after the
routine aspect has been introduced in the context of a
whole, meaningful learning task.
Each of the four components corresponds with a spe-
cific design step (see Table 26.1). In this way, the design of
learning tasks corresponds with step 1, the design of sup-
portive information with step 4, the design of procedural
information with step 7, and the design of part-task prac-
tice with step 10. The other six steps are supplementary
and are performed when necessary. Step 2, for example,
organizes the learning tasks in easy-to-difficult categories
to ensure that students work on tasks that begin simple and
smoothly increase in difficulty, and step 3 specifies the
standards for acceptable performance of the task which is
necessary to assess performance and provide feedback.
Steps 5 and 6 may be necessary for in-depth analysis of the
supportive information needed for learning to carry out
nonroutine aspects of learning tasks. Finally, steps 8 and 9
may be necessary for in-depth analysis of the procedural
information needed for performing routine aspects
of learning tasks.
Designing With the
Four Blueprint Components
Figure 26.1 shows how the four blueprint compo-
nents (also see the left hand column of Table 26.1)
are interrelated to each other.
Learning Tasks
Learners work on tasks that help them develop
an integrated knowledge base through a process of
inductive learning, inducing knowledge from con-
crete experiences. As a result, each learning task
should offer whole-task practice, confronting the
learner with all or almost all of the constituent skills
important for performing the task, including their
associated knowledge and attitudes. In this whole-
task approach, learners develop a holistic vision of
the task that is gradually embellished during train-
ing. A sequence of learning tasks provides the
Table 26.1 The Four Blueprint Components of 4C-ID
and the Ten Steps to Complex Learning
Blueprint Components of 4C-ID Ten Steps to Complex Learning
Learning Tasks 1. Design Learning Tasks
2. Sequence Task Classes
3. Set Performance Objectives
Supportive Information 4. Design Supportive Information
5. Analyze Cognitive Strategies
6. Analyze Mental Models
Procedural Information 7. Design Procedural Information
8. Analyze Cognitive Rules
9. Analyze Prerequisite Knowledge
Part-Task Practice 10. Design Part-Task Practice
SOURCE: Van Merrienboer, J. J. G., & Kirschner, P. A. (2007). Ten steps to
complex learning. Mahwah, NJ: Lawrence Erlbaum Associates.
Ten Steps to Complex Learning: A New Approach to Instruction and Instructional Design • 247
backbone of a training program for complex learning.
In line with the earlier discussed transfer paradox, it is
important that the chosen learning tasks differ from each
other on all dimensions that also differ in the real world, so
that learners can abstract more general information from
the details of each single task. There is strong evidence
that such variability of practice is important for achieving
transfer of learning—both for relatively simple tasks (e.g.,
Paas & van Merriënboer, 1994; Quilici & Mayer, 1996)
and highly complex real-life tasks (e.g., Schilling, Vidal,
Ployhart, & Marangoni, 2003; van Merriënboer, Kester, &
Paas, 2006). A sequence of different learning tasks thus
always provides the backbone of a training program for
complex learning. Schematically, it looks like this:
Task Classes
It is not possible to use very difficult learning tasks with
high demands on coordination right from the start of a
training program, so learners start work on relatively easy
whole-learning tasks and progress toward more difficult
ones (van Merriënboer, Kirschner, & Kester, 2003). Cate-
gories of learning tasks, each representing a version of the
task with the same particular difficulty, are called task
classes. All tasks within a particular task class are equiva-
lent in that the tasks can be performed based on the same
body of general knowledge. A more difficult task class
requires more knowledge or more embellished knowledge
Part-task practice
provides additional practice for selected recurrent
aspects in order to reach a very high level of
provides a huge amount of repetition
only starts after the recurrent aspect has been
introduced in the context of the whole task (i.e., in
a fruitful cognitive context)
Supportive information
supports the learning and performance of
nonrecurrent aspects of learning tasks
explains how to approach problems in a domain
(cognitive strategies) and how this domain is
organized (mental models)
is specified per task class and always available
to the learners
Procedural information
is prerequisite to the learning and performance of
recurrent aspects of learning tasks (or, practice items)
precisely specifies how to perform routine aspects of
the task, e.g., through step-by-step instruction
is presented just in time during the work on the
learning tasks and quickly fades away as learners
acquire more expertise
aim at integration of (nonrecurrent and recurrent)
skills, knowledge, and attitudes
provide authentic, whole-task experiences based
on real-life tasks
are organized in easy-to-difficult task classes
have diminishing support in each task class
show high variability of practice
Learning tasks
Figure 26.1 A Schematic Training Blueprint for Complex Learning
for effective performance than the preceding, easier task
classes. In the training blueprint, the tasks are organized in
an ordered sequence of task classes (i.e., the dotted boxes)
representing easy-to-difficult versions of the whole task:
Support and Guidance
When learners start work on a new, more difficult task
class, it is essential that they receive support and guidance
for coordinating the different aspects of their performance.
Support—actually task support—focuses on providing
learners with assistance with the products involved in the
training, namely the givens, the goals, and the solutions
that get them from the givens to the goals (i.e., it is product
oriented). Guidance—actually solution-process guid-
ance—focuses on providing learners with assistance with
the processes inherent to successfully solving the learning
tasks (i.e., it is process oriented).
This support and guidance diminishes in a process of
scaffolding as learners acquire more expertise. The contin-
uum of learning tasks with high support to learning tasks
without support is exemplified by the continuum of sup-
port techniques ranging from fully-reasoned case studies
through partially worked out examples using the comple-
tion strategy (van Merriënboer, 1990; van Merriënboer &
de Croock, 2002) to conventional tasks (for a complete
description see van Merriënboer & Kirschner, 2007). In a
training blueprint, each task class starts with one or more
learning tasks with a high level of support and guidance
(indicated by the grey in the circles), continues with learn-
ing tasks with a lower level of support and guidance, and
ends with conventional tasks without any support and
guidance as indicated by the filling of the circles:
Recurrent and Nonrecurrent Constituent Skills
Not all constituent skills are the same. Some are con-
trolled, schema-based processes performed in a variable
way from problem situation to problem situation. Others,
lower in the skill hierarchy, may be rule-based processes
performed in a highly consistent way from problem situ-
ation to problem situation. These constituent skills
involve the same use of the same knowledge in a new
problem situation. It might even be argued that these
skills do not rely on knowledge at all, because this
knowledge is fully embedded in the rules and conscious
control is not required because the rules have become
fully automated.
Constituent skills are classified as nonrecurrent if they
are performed as schema-based processes after the train-
ing; nonrecurrent skills apply to the problem solving and
reasoning aspects of behavior. Constituent skills are clas-
sified as recurrent if they are performed as rule-based
processes after the training; recurrent skills apply to the
routine aspects of behavior. The classification of skills as
nonrecurrent or recurrent is important in the Ten Steps
(van Merriënboer & Kirschner, 2007) because instruc-
tional methods for the effective and efficient acquisition of
them are very different.
Supportive Versus Procedural Information
Supportive information is important for nonrecurrent
constituent skills and explains to the learners how a learn-
ing domain is organized and how to approach problems in
that domain. Its function is to facilitate schema con-
struction such that learners can deeply process the new
information, in particular by connecting it to already
existing schemas in memory via elaboration. Because
supportive information is relevant to all learning tasks
within the same task class, it is typically presented before
learners start to work on a new task class and kept avail-
able for them during their work on this task class. This is
indicated in the L-shaped shaded areas in the schematic
training blueprint:
Procedural information is important for constituent
skills that are recurrent; procedural information specifies
for learners how to perform the routine aspects of learn-
ing tasks, preferably in the form of direct, step-by-step
instruction. This facilitates rule automation, making the
information available during task performance so that it
can be easily embedded in cognitive rules via knowledge
compilation. Because procedural information is relevant to
the routine aspects of learning tasks, it is best presented to
learners exactly when they first need it to perform a task
(i.e., just in time), after which it quickly fades for subse-
quent learning tasks. In the schematic training blueprint,
the procedural information (black beam) is linked to the
separate learning tasks:
Part-Task Practice
Learning tasks provide whole-task practice to prevent
compartmentalization and fragmentation. There are, how-
ever, situations where it may be necessary to include
part-task practice in the training, usually when a very
high level of automaticity is required for particular
Ten Steps to Complex Learning: A New Approach to Instruction and Instructional Design • 249
recurrent aspects of a task. In this case, the series of
learning tasks may not provide enough repetition to reach
that level. For those aspects classified as to-be-automated
recurrent constituent skills, additional part-task practice
may be provided—such as when children drill the multi-
plication tables or when musicians practice specific
musical scales.
This part-task practice facilitates rule automation via a
process called strengthening, in which cognitive rules
accumulate strength each time they are successfully
applied. Part-task practice for a particular recurrent aspect
of a task can begin only after it has been introduced in a
meaningful whole-learning task. In this way, learners start
their practice in a fruitful cognitive context. In the sche-
matic training blueprint, part-task practice is indicated by
series of small circles (i.e., practice items):
Ten Steps
Figure 26.2 presents the whole design process for com-
plex learning. The grey boxes show the ten activities that
are carried out when properly designing training blue-
prints for complex learning. These activities are typically
employed by a designer to produce effective, efficient,
and appealing educational programs. This section
explains the different elements in the figure from the
bottom up.
The lower part of the figure is identical to what was
just discussed. For each task class, learning tasks are
designed to provide learners with variable whole-task
practice at a particular difficulty level until they reach the
prespecified standards for this level, whereupon they
continue to the next, more complex or difficult task class.
The design of supportive information pertains to all
information that may help learners carry out the nonre-
current problem solving and reasoning aspects of the
learning tasks within a particular task class. The design
of procedural information pertains to all information that
exactly specifies how to carry out the recurrent, routine
aspects of the learning tasks. And finally, the design of
Design learning tasks
Design procedural
Design supportive
nonrecurrent aspects
Set performance
Sequence task classes
Design part-task
Analyze recurrent
Analyze cognitive rules
Analyze prerequisite
Figure 26.2 The Ten Activities (grey boxes) in Designing for Complex Learning
part-task practice may be necessary for selected recurrent
aspects that need to be developed to a very high level
of automaticity.
The middle part of the figure contains five activities.
The central activity—sequence task classesdescribes
an easy-to-difficult progression of categories of tasks that
learners may work on. It organizes the tasks in such a
way that learning is optimized. The least difficult task
class is at the entry level of the learners and the final,
most complex or difficult task class is at the final attain-
ment level defined by the performance objectives for the
whole training program.
The analyses of cognitive strategies and mental models
are necessary for learners to achieve the nonrecurrent
aspects of carrying out the task. The analysis of cognitive
strategies answers the question, How do proficient task
performers systematically approach problems in the task
domain? The analysis of mental models answers the
question, How is the domain organized? The resulting sys-
tematic approaches to problem solving and domain models
are used as a basis for the design of supportive information
for a particular task class.
The analyses of cognitive rules and prerequisite
knowledge are necessary for learners to achieve the recur-
rent aspects of carrying out the task. The analysis of
cognitive rules identifies the condition-action pairs that
enable experts to perform routine aspects of tasks without
effort (IF condition, THEN action). The analysis of pre-
requisite knowledge identifies what learners need to
know to correctly apply those condition-action pairs.
Together, the results of these analyses provide the basis
for the design of procedural information. In addition,
identified condition-action pairs help to specify practice
items for part-task practice.
The upper part of the figure contains only one activity,
setting performance objectives. Because complex learning
deals with highly integrated sets of learning objectives, the
focus is on the decomposition of a complex skill into a
hierarchy describing all aspects or constituent skills rele-
vant to performing real-life tasks. In other words, the
specification of performance objectives and standards for
acceptable performance for each of the constituent skills,
and a classification of the skills within these objectives is
either nonrecurrent or recurrent.
As indicated by the arrows, some activities provide
preliminary input for other activities. This suggests that
the best order for performing the activities would be to
start with setting performance objectives, then to con-
tinue with sequencing task classes and analyzing
nonrecurrent and recurrent aspects, and to end with
designing the four blueprint components. Indeed, the ten
activities have previously been described in this analyti-
cal order (e.g., van Merriënboer & de Croock, 2002). But
in real-life design projects, each activity affects and is
affected by all other activities. This leaves it an open
question as to which order for using the ten activities is
most fruitful.
A Dynamic Model
The model presented takes a system dynamics view of
instruction, emphasizing the interdependence of the ele-
ments constituting an instructional system and recognizing
the dynamic nature of this interdependence, which makes
the system an irreducible whole. Such a systems approach
is both systematic and systemic. It is systematic because the
input-process-output paradigm where the outputs of partic-
ular elements of the system serve as inputs to other elements,
and the outputs of particular design activities serve as inputs
for other activities is inherent to it. For example, the output
of an analysis is the input for the design of supportive infor-
mation in the blueprint. At the same time, it is actually also
systemic because the performance or function of each ele-
ment directly or indirectly affects or is affected by one or
more of the other elements—thereby making the design
process highly dynamic and nonlinear. For example, this
same analysis of nonrecurrent aspects of a skill can also
affect the choice and sequencing of task classes.
The Pebble-in-the-Pond:
From Activities to Steps
M. David Merrill (2002a) proposed a pebble-in-the-pond
approach for instructional design that is fully consistent
with the Ten Steps. It is a content-centered modification of
traditional instructional design in which the contents-to-
be-learned, and not the abstract learning objectives, are
specified first. The approach consists of a series of expand-
ing activities initiated by first casting a pebble in the pond;
that is, designing one or more learning tasks of the type
that learners will be taught to accomplish by the instruc-
tion. This simple little pebble initiates further ripples in the
design pond. This prescriptive model is workable and use-
ful for teachers and other practitioners in the field of
instructional design.
A Backbone of Learning Tasks: Steps 1, 2, and 3
The first three steps aim at the development of a series
of learning tasks that serve as the backbone for the educa-
tional blueprint:
Step 1: Design Learning Tasks
Step 2: Sequence Task Classes
Step 3: Set Performance Objectives
The first step, the pebble so to speak, is to specify one
or more typical learning tasks that represent the whole
complex skill that the learner will be able to perform fol-
lowing the instruction. Such a task has in the past been
referred to as an epitome, the most overarching, fundamen-
tal task that represents the skill (Reigeluth, 1987; Reigeluth
& Rodgers, 1980; Reigeluth & Stein, 1983). In this way, it
becomes clear from the beginning, and at a very concrete
Ten Steps to Complex Learning: A New Approach to Instruction and Instructional Design • 251
level, what the training program aims to achieve. Nor-
mally, providing only a few learning tasks to learners will
not be enough to help them develop the complex skills
necessary to perform the whole task. Therefore, another
unique characteristic of the pebble-in-the-pond approach
is—after casting the first whole learning task pebble into
the pond—to specify a progression of such tasks of
increasing difficulty such that if learners were able to do
all of the tasks identified, they would have mastered the
knowledge, skills, and attitudes that are to be taught. This
ripple in the design pond, or step 2, involves the assign-
ment and sequencing of learning tasks to task classes with
different levels of difficulty. Tasks in the easiest class are
at the learners’ entry level, whereas tasks in the most diffi-
cult task class are at the training program’s exit level. To
give learners the necessary feedback on the quality of their
performance and to decide when learners may proceed
from one task class to the next, it is necessary to state the
standards that need to be achieved for acceptable perfor-
mance. This next ripple in the design pond, or step 3,
consists of the specification of performance objectives
that, among other things, articulate the standards that
learners must reach to carry out the tasks in an acceptable
fashion. In this way, the pebble-in-the-pond approach
avoids the common design problem that the objectives that
are determined early in the process are abandoned or
revised later in the process to correspond more closely to
the content that has finally been developed.
Component Knowledge, Skills,
and Attitudes: Steps 4 to 10
Further ripples identify the knowledge, skills, and atti-
tudes necessary to perform each learning task in the
progression of tasks. This results in the remaining blue-
print components, which are subsequently connected to
the backbone of learning tasks. A distinction is made here
between supportive information, procedural information,
and part-task practice. The steps followed for designing
and developing supportive information are as follows:
Step 4: Design Supportive Information
Step 5: Analyze Cognitive Strategies
Step 6: Analyze Mental Models
Units of supportive information that help learners per-
form the nonrecurrent aspects of the learning tasks related
to problem solving and reasoning are connected to task
classes, and more complex task classes typically require
more detailed or more embellished supportive information
than easier task classes. If useful instructional materials are
already available, step 4 may be limited to reorganizing
existing instructional materials and assigning them to task
classes. Steps 5 and 6 may then be neglected. But if
instructional materials need to be designed and developed
from scratch, it may be helpful to perform step 5, where
the cognitive strategies that proficient task-performers use
to solve problems in the domain are analyzed, or step 6,
where the mental models that describe how the domain is
organized are analyzed. The results of the analyses in steps
5 and 6 provide the basis for designing supportive infor-
mation. Analogous to the design and development of
supportive information, steps 7, 8, and 9 are for designing
and developing procedural information:
Step 7: Design Procedural Information
Step 8: Analyze Cognitive Rules
Step 9: Analyze Prerequisite Knowledge
Procedural information for performing recurrent aspects
of learning tasks specifies exactly how to perform these
aspects (and is thus procedural) and is preferably presented
precisely when learners need it during their work on the
learning tasks (i.e., just in time). For subsequent learning
tasks, this procedural information quickly fades, often
replaced by new specific information for carrying out new
procedures. If useful instructional materials such as job
aids, quick reference guides, or even Electronic Perfor-
mance Support Systems (EPSSs; van Merriënboer &
Kester, 2005) are available, step 7 may be limited to updat-
ing those materials and linking them to the appropriate
learning tasks. Steps 8 and 9 may then be neglected. But if
the procedural information needs to be designed from
scratch, it may be helpful to perform step 8, where the
cognitive rules specifying the condition-action pairs that
drive routine behaviors are analyzed, and step 9, where the
knowledge that is prerequisite to a correct use of cognitive
rules is analyzed. The results of the analyses in steps 8 and
9 then provide the basis for the design of procedural infor-
mation. Finally, depending on the nature of the task and the
knowledge and skills needed to carry it out, it may be nec-
essary to perform the tenth and final step:
Step 10: Design Part-Task Practice
Under particular circumstances, additional practice is
necessary for selected recurrent aspects of a complex skill
in order to develop a very high level of automaticity. This,
for example, may be the case for recurrent constituent
skills that cause danger to life and limb, loss of expensive
or hard to replace materials, or damage to equipment if not
carried out properly and quickly. If part-task practice needs
to be designed, the analysis results of step 8 (i.e., the con-
dition-action pairs) provide useful input. For a detailed
description of the Ten Steps see van Merriënboer and
Kirschner (2007).
Ten Steps Within an Instructional
Systems Design Context
The Ten Steps will often be applied in the context of
Instructional Systems Design (ISD). ISD models have a
broad scope and typically divide the instructional design
process into five phases: (a) analysis, (b) design, (c) devel-
opment, (d) implementation, and (e) summative evaluation.
In this so-called ADDIE model, formative evaluation is
conducted during all of the phases. The Ten Steps is nar-
rower in scope and focus on the first two phases of the
instructional design process, namely, task and content anal-
ysis and design. In particular, the Ten Steps concentrates on
the analysis of a to-be-trained complex skill or professional
competency in an integrated process of task and content
analysis and the conversion of the results of this analysis
into a training blueprint that is ready for development and
implementation. The Ten Steps is best applied in combina-
tion with an ISD model to support activities not treated in
the Ten Steps, such as needs assessment and needs analysis,
development of instructional materials, implementation and
delivery of materials, and summative evaluation of the
implemented training program.
References and Further Readings
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... Instruktionsdesign beschreibt die Entwicklung von Unterstützungsprogrammen zum Erwerb komplexer kognitiver Fertigkeiten in realen Kontexten (Kirschner & van Merriënboer, 2008) und somit ein systematisches Vorgehen zur Gestaltung von Lernumgebungen (Vogel et al., 2017;Ifenthaler, 2017). Sowohl direkte Förderungsmöglichkeiten, die auf das Vermitteln von Lernstrategien abzielen als auch eine indirekte Förderung in Form der Gestaltung einer Lernumgebung, verdeutlichen die Funktion eines Instruktionsdesigns als Unterstützung regulierter Lernprozesse (Bannert, 2007;Renkl, 2015). ...
... Routinen und geben genau dann eine regulative Anleitung, wenn diese im Rahmen individueller Lernaufgaben benötigt wird. Mit der Integration repetierender Übungen von wiederkehrenden Aufgaben stellen Teilaufgaben die letzte der vier Entwurfskomponenten dar Vogel et al., 2017;Kirschner & van Merriënboer, 2008). Da sowohl das regulierte Lernen als auch das Instruktionsdesign auf eine gemeinsame Problemlösung abzielen, sind der Problemlösungs-und der Teamfindungsprozess bei der Gestaltung von OLC zu berücksichtigen. ...
... 5.3), das sich an den in Abschn. 5.2 beschriebenen Regulationsformen (Hadwin et al., 2018) und an den Entwurfskomponenten von 4C/ID Kirschner & van Merriënboer, 2008) sowie an den zuvor skizzierten Handlungen der Interaktionsprozessanalyse (Kreutz, 2002) orientierte, wurden alle Nutzeraktivitäten und die stattgefundenen Regulationsaktivitäten bestimmt. ...
Lernen mit digitalen Medien ist ein zwar junges aber weit erforschtes Feld der psychologischen Forschung. Ein Großteil der Forschung widmete sich dabei der Erforschung kognitiver Prozesse bei der Selektion und Verarbeitung sowie der Speicherung und dem Abruf von Informationen. Erst in den letzten 20 Jahren wurden verstärkt begleitende psychische Prozesse wie der Motivation, der Emotion, sozialer Prozesse sowie der Metakognition untersucht. Dieser Beitrag gibt einen Überblick über grundlegende und um zusätzliche Prozesse erweiterte Theorien zum Lernen mit digital präsentierten Lernmaterialien. Darüber hinaus werden alle Prozessarten, die am Lernvorgang beteiligt sein können, näher beleuchtet um ein ganzheitliches Bild des Lernens mit digitalen Medien zu zeichnen. Gleichzeitig wird anhand aktueller Forschung aufgezeigt, in welchen Bereichen noch bestehende Forschungslücken herrschen.
... To master complex skills, it is necessary to practice and apply them actively, regularly and, if possible, frequently. Complex skills consist of constituent subskills which require high cognitive effort and concentration to integrate (Kirschner & Van Merriënboer, 2008;Van Merriënboer & Kirschner, 2017;Voogt & Pareja-Roblin, 2012). Complex generic skills are not specific for a domain, occupation, or type of task, but important for all kinds of work, education, and life in general. ...
... However, teacher time is scarce, as they have large student groups to support; therefore, frequent and personalized feedback can often not be realized in practice. Students also need repetition and meaningful, different whole task contexts to apply their skills, also called variability of practice (Kirschner & Van Merriënboer, 2008;Van Merriënboer & Kirschner, 2017) in order to enhance transfer of the application of skills to new settings. Furthermore, they need good and bad examples of skills execution and associated visible behavior (Van Gog et al, 2014;Van Gog & Rummel, 2010) in order to form a mental model of what task performance level is expected from them. ...
Full-text available
Higher education is faced with the question of how large numbers of students can be supported to learn complex skills without increasing teachers' supervision time proportionally and while preserving, or preferably improving, quality. Just practicing skills once does not work. Students need repetitive practice, feedback, and structured support to master a skill. They need to gain insight into what went well and what could be improved so that they can further direct their attention while practicing. However, teachers cannot provide feedback on every practice session of students, as their time is scarce. To solve this problem, an online formative assessment method for interactive and practice-oriented skills' training, Pe(e)rfectly Skilled, was developed that provides structured support for self-regulation, goal setting, feedback, and reflection. This method affords practicing skills repetitively, both individually and collaboratively, at students' own time, pace, and place. In this article, theoretical and practical underpinnings underlying the Pe(e)rfectly Skilled method are described.
... Un modelo que está ganando espacio en la comunidad académica para guiar el diseño curricular y el aprendizaje para la resolución de problemas, pero la parece aún no es tomado en cuenta en Ecuador, es el modelo de los cuatro componentes para el diseño instruccional (4C/ID) de Van Merriënboer y Kirschner (2013). Este modelo sitúa en el mismo nivel al aprendizaje complejo, el desarrollo de competencias para el mundo real y la solución de problemas (Kirschner & Van Merriënboer, 2008). Los autores han definido al aprendizaje complejo como la adquisición de "conocimientos, habilidades y actitudes integrados, coordinando cualitativamente diferentes 'habilidades constituyentes', que a menudo son transferidas desde las situaciones escolares y de entrenamiento a las realidades de la vida diaria y la profesión" (Van Merriënboer & Kirschner, 2013, p. 2). ...
... Componentes del bosquejo esquemático de entrenamiento para el aprendizaje complejo. Fuente: Van Merriënboer yKirschner (2008). ...
Las carreras de educación superior de Ecuador deben contribuir con la construcción de la sociedad del Buen Vivir. Para esto se sugiere rediseñar el currículum y la enseñanza a partir de modelos que desarrollen el aprendizaje y la experticia de los estudiantes, a fin que resuelvan los problemas cruciales de la sociedad ecuatoriana. En este trabajo primero se plantea la necesidad de articular la metodología de diseño curricular con la evidencia empírica, sobre las mejores condiciones para aprender y desarrollar el talento humano. Segundo, se argumenta que el enfoque basado en la solución de problemas contribuiría significativamente a esta articulación. Y por último, se presenta el diseño curricular e instruccional de los cuatro componentes del diseño instruccional para el aprendizaje complejo, como un modelo alternativo que guíe la implementación adecuada de carreras de educación superior. Se concluye que la articulación entre las teorías del aprendizaje, el diseño curricular y la solución de problemas, es una vía promisoria para la construcción de la sociedad del Buen Vivir. ABSTRACTEcuadorian higher education careers must contribute building a Good Living society. This suggests redesigning the curriculum and instruction from models that develop learning and student expertise in order to solve the crucial problems of the Ecuadorian society. Due to, this paper firstly proposes the need to articulate the curriculum design methodology with the empirical evidence about the best conditions to learn and develop human talent. Secondly, it suggests that the problem-solving approach can contribute significantly to this articulation. And finally, it presents the instructional design model of four components to complex learning to design curriculum instructional programs as an alternative model to guide the effective implementation of programs in higher education. It concludes proposing that the integration between learning theories, curriculum design, and problem-solving approach is a promising way to build the Good Living society.
... It is because it does not adhere to the real-work tasks: when a learner knows how to deal a specific activity, from its acquired knowledge, but does not know the interactions with the other parts, could have significative problems in applying it in the real field. This ambivalence represents the so called "Transfer paradox" [13]. ...
... The Instructional design (ID) theory supports the course designers in this process and a specific approach is called four-component Instructional Design (4C-ID) [13]. ...
Full-text available
The paper regards an experimental study of an instructional design (ID) model applied in the routines of a digital learning company for content production and course design. Given the complexity of adult learning, the study tried to identify an operative model that could support production process and at the same time ensure rich and engaging learning experiences. The model guarantees the possibility to evaluate learning objectives achievement of through the expression of the target knowledge/skills. Therefore, the ID model can be applied to both mandatory, self-enrolled and professional paths (in up-skilling and re-skilling courses). The development of the model is based on the four-component instructional design (4C-ID) that represents the main structure for the design. The paper presents a practical application of this model in a digital Italian company (Piazza Copernico srl) and explains the preliminary version of the model for future exploitations. Keywords 1 Instructional design, four-component instructional design, 4C-ID, complex learning
... Este conjunto de contrastes solo muestra lo complejo que es la red de variables que se entrecruzan en la práctica educativa; una práctica que es mejor descrita como una actividad cognitiva constructiva autorregulada que implica un aprendizaje complejo (Castañeda, 2004;Kirschner & Van Merriënboer, 2008). De acuerdo con Castañeda y Ortiz (2017), el aprendizaje complejo está influido por poderosos componentes de agencia académica, entre ellos la epistemología personal, un componente que parece influir en todo el proceso. ...
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Con el objetivo de examinar los estilos y enfoques de aprendizaje, se aplicaron dos cuestionarios (CHAEA y R-SPQ-2F) a estudiantes de psicología (N = 221) de tres semestres diferentes. Los resultados obtenidos mostraron que el estilo Reflexivo fue elegido en primer lugar, mientras que el estilo Activo lo fue en último (F(2.071, 455.620) = 45.836, p < .001). No se encontraron diferencias entre los tres semestres examinados. Por otro lado, los participantes se decantaron por el enfoque Profundo de aprendizaje y no por el Superficial (t(220) = 24.603, p < .001, d de Cohen = 2.55). Una prueba de Pearson mostró relaciones fuertes y positivas entre el enfoque Profundo y los estilos de aprendizaje Reflexivo, Teórico y Pragmático. Tal y como se ha encontrado en otros estudios, los participantes se inclinaron por el estilo Reflexivo y eligieron el enfoque Profundo, lo cual parece reflejar la forma en cómo se desarrolla el trabajo académico realizado en el nivel universitario. Es importante mencionar que las características psicométricas de los instrumentos utilizados poseen una consistencia adecuada, lo cual sustenta su empleo para el análisis de los estilos y enfoques de los estudiantes como un paso previo antes de su inserción en el proceso educativo.
... Gemäß dem didaktischen Prinzip des kognitiv aktivierenden und situierten Lernens im Ansatz des 4CID (Kirschner & Van Merrienboer, 2008) In der Plenardiskussion wird herausgearbeitet, dass der mathematische Gehalt der Texte mit den Oberflächenmerkmalen und der Wortebene allein kaum verbunden ist (Ali schreibt den bzgl. Oberflächenmerkmalen besten Text, doch Suleika den aus fachlicher Sicht besten). ...
Zusammenfassung. Präsentiert werden das Konzept, die Umsetzung und die Wirksamkeits-evaluation einer Master-Veranstaltung, in der Studierende lernen, sprachlich inklusiven Fa-chunterricht zu erteilen. Dazu werden im Seminar sprach-und fachdidaktische Hintergründe in situierten, reichhaltigen Lernsituationen erarbeitet und durch zunehmende Integration ver-knüpft. Die Wirksamkeit des Veranstaltungskonzepts wurde im Prä-Post-Design mit Kon-trollgruppe evaluiert, und zwar im Hinblick auf Diagnosekategorien und Orientierungen der Studierenden zu sprachlich inklusivem Fachunterricht. Orientierungen der n = 52 Lehramts-studierenden wurden durch standardisierte Skalen erhoben und die individuellen Kategorien in veranstaltungsintegrierten diagnostischen Aktivitäten. Der Gruppenvergleich der jeweili-gen Veränderung von Vor-zu Nacherhebung zeigt in der Interventionsgruppe eine deutliche Verschiebung hin zu lernförderlicheren Orientierungen und zu einer breiteren und treffsiche-reren Aktivierung wichtiger Kategorien für die Diagnose.
... Indeed, it is often easier for professionals to recognise mistakes made by someone attempting to learn an epistemic game associated with a profession than it is to explicitly list what people should be doing ahead of time (Markauskaite and Goodyear, 2017). Within this context it becomes vital that we develop methods to help students find appropriate pathways towards career goals that they identify, and to understand the complex skills (Kirschner and Van Merriënboer, 2008) that they need to master in order to achieve those goals. Furthermore, universities need to do more than support our students in developing a rich portfolio of attributes and skills; we also need to help them to demonstrate those attributes, in a manner that employers can understand and interpret. ...
Full-text available
Pressure is mounting upon universities to ensure that our graduates are employable. Business and governments increasingly demand that graduates are equipped with skills and competencies that map into labour market needs. But students often struggle to choose courses, subjects and activities that will support their career goals and aspirations. This paper introduces an approach designed at UTS which aims to embed a skills analytics tool at key transition points for our students. The need to support such tools will a well-grounded learning design is discussed, along with the need to move beyond a “one size fits all” model for supporting EdTech tools. A solution that utilises a series of modules in the LMS is introduced.
... Second-order scaffolding intends to support the learning of domaingeneral skills, especially SRL (Kirschner & van Merriënboer, 2008). SRL refers to the modulation of affective, cognitive, and behavioural processes throughout a learning experience to reach the desired level of achievement (Karoly, 1993). ...
Full-text available
In game-based learning, adaptive scaffolding can enhance the learning of domain-specific skills, known as first-order scaffolding, and self-regulatory skills, known as second-order scaffolding. To design adaptive scaffolding, we need indicators that identify learning opportunities. Therefore we investigated how indicators of performance and self-regulation relate to overall game performance in a medical emergency simulation game. These indicators have the potential to guide the design of adaptive first-order and second-order scaffolding, respectively. Twenty-six fourth-year medical students played 116 game sessions. Using a multilevel model, we investigated the relationship between overall game performance and a range of online and offline measures. For first-order scaffolding, accuracy, systematicity and thoroughness were found to be valid indicators; for second-order scaffolding, high global self-regulatory scores and frequent monitoring were found to be valid indicators. These indicators can be included in future algorithms for adaptive scaffolding in game-based learning.
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This randomized controlled trial with first- and second-grade students is the first experimental study addressing long-running disagreements about whether primary grade students should develop transcription and oral language abilities before learning to compose. It is also the first study at these grade levels to teach close reading (using science text aligned to the Next Generation Science Standards) to plan and write a timed informative essay. Theoretically and evidence-based multi-component writing instruction was developed, termed “Self-Regulated Strategy Development (SRSD) Plus.” SRSD Plus integrates evidence-based practices for transcription (handwriting and spelling) and oral language skills (vocabulary and sentence structure) with SRSD instruction for close reading to learn and then write informative essays. A total of 93 children in Grade 1 (n = 46, 50% female) and Grade 2 (n = 47, 51% female) in a high poverty school participated in the study (50% boys; mean age = 6.68; SD = .48). Students were randomly assigned to either teacher-led SRSD Plus or business-as-usual (writers workshop) condition within class in each grade. SRSD Plus was implemented with small groups for 45 minutes, three times a week, for 10 weeks. Outcomes examined included: instructional fidelity, spelling, handwriting fluency, vocabulary, sentence proficiency, discourse knowledge, planning, writing quality, structural elements in informative essays, number of words written, use of transition words, text comprehension and use of source text. Results showed moderate to large effect sizes in writing outcomes, oral language skills (vocabulary and sentence proficiency), spelling, and discourse knowledge. Differential effects due to grade, gender, and race are examined, and directions for future research are discussed.
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Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert–novice differences, and cognitive load. Although unguided or minimally guided instructional approaches are very popular and intuitively appealing, the point is made that these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half-century that consistently indicate that minimally guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide “internal” guidance. Recent developments in instructional research and instructional design models that support guidance during instruction are briefly described.
One outcome of recent progress in educational technology is strong interest in providing effective support for learning in complex and ill-structured domains. We know how to use technology to promote understanding in simpler domains (e.g., orientation information, procedures with minimal-branching, etc.), but we are less sure how to use technology to support understanding in more complex domains (e.g., managing limited resources, understanding environmental impacts, etc.). Such domains are increasingly significant for society. Technology (e.g., collaborative tele-learning, digital repositories, interactive simulations, etc.) can provide conceptually and functionally rich domains for learning. However, this introduces the problem of determining what works in which circumstances and why. Research and development on these matters is reflected in this collection of papers. This research suggests a need to rethink foundational issues in educational philosophy and learning technology. One major theme connecting these papers is the need to address learning in the large - from a more holistic perspective. A second theme concerns the need to take learners where and as they are, integrating technology into effective learning places. Significant and systematic progress in learning support for complex domains demands further attention to these important issues.
The four-component instructional design (4C/ID) model claims that four components are necessary to realize complex learning: (1) learning tasks, (2) supportive information, (3) procedural information, and (4) parttask practice. This chapter discusses the use of the model to design multimedia learning environments in which instruction is controlled by the system, the learner, or both; 22 multimedia principles are related to each of the four components and instructional control. Students may work on learning tasks in computer-simulated task environments such as virtual reality environments, serious games, and high-fidelity simulators, where relevant multimedia principles primarily facilitate a process of inductive learning; they may study, share, and discuss supportive information in hypermedia, microworlds, and social media, where principles facilitate a process of elaboration and mindful abstraction; they may consult procedural information using mobile apps, augmented reality environments, and online help systems, where principles facilitate a process of knowledge compilation; and, finally, they may be involved in part-task practice with drill-and-practice computer-based/app-based training programs and part-task trainers, where principles facilitate a process of psychological strengthening. Instructional control can be realized by adaptive multimedia systems, but electronic development portfolios can be helpful when learners are given partial or full control. Research implications and limitations of the presented framework are discussed.
Efforts to improve human intelligence and thinking have a long history and a lively presence in a number of programs and approaches. Many studies have demonstrated that targeted interventions can teach people to think better within particular subject matters and in some general ways as well, with transfer beyond the kinds of tasks used in instruction and moderate persistence. Effective interventions reorganize learners' thinking with strategies, metacognition, and other means, not just practice-up skills. However, do such improvements genuinely constitute gains in intelligence? They only sometimes and modestly advance intelligence in the sense of IQ, but the authors argue that this essentialist sense of intelligence is flawed. Taking a more eclectic view of intelligence, the interventions reviewed here and others like them teach intelligence because they provide people with the psychological resources to think better across a range of contexts.
Collins, A., Brown, J.S., & Newman, S.E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.) Knowing, learning, and instruction: E...