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Demonstrating the Learning Effectiveness of Simulations: Where We are and Where We Need to Go

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This paper focuses on the learning effectiveness of simulation exercises. It identifies a myriad of possible Learning objectives for simulations. Most of these objectives have been studied using perceptions as the dependent variable; few have been investigated using a more objective dependent variable. It is clear that objective measures have been used for a very limited range of learning objectives. Much of the reason for the limitations of our present state of knowledge can be attributed to our lack of attention or inability to develop appropriate, objective dependent measures of learning. Until appropriate objective variables are identified for a broader range of learning objectives, solid support for the learning effectiveness of simulations will be lacking.
Developments In Business Simulation & Experiential Learning, Volume 24, 1997
Demonstrating the Learning Effectiveness of Simulations:
Where We are and Where We Need to Go
Philip H. Anderson, University of St Thomas
Leigh Lawton, University of St Thomas
ABSTRACT
This paper focuses on the learning effectiveness of
simulation exercises. It identifies a myriad of possible
Learning objectives for simulations. Most of these
objectives have been studied using perceptions as the
dependent variable; few have been investigated using a
more objective dependent variable. It is clear that
objective measures have been used for a very limited
range of learning objectives. Much of the reason for
the limitations of our present state of knowledge can be
attributed to our lack of attention or inability to
develop appropriate, objective dependent measures of
learning. Until appropriate objective variables are
identified for a broader range of learning objectives,
solid support for the learning effectiveness of
simulations will be lacking.
INTRODUCTION
Since the early days of gaming, there has been a call
for hard evidence to support the teaching effectiveness
of simulations (see, for example, Neuhauser, 1976;
Snow, 1976). The purpose of this paper is to identify
where the deficiencies in our knowledge exist and to
suggest directions for future research.
An abundance of research has been conducted
analyzing various dimensions of the simulation
experience. The length of the bibliography in Keys’
and Wolfe’s 1990 review of the state of simulation is
awe-inspiring. Despite the extensive literature, it
remains difficult, if not impossible, to support
objectively even the most fundamental claims for the
efficacy of games as a teaching pedagogy. There is
relatively little hard evidence that simulations produce
learning or that they are superior to other
methodologies. Much of the reason for the inability to
make supportable claims about the efficacy of
simulations can be traced to the selection of dependent
variables and to the lack of rigor with which
investigations have been conducted. If we are to
increase our knowledge of what student’s gain from
participating in a simulation, we must be more
systematic in our research efforts.
Can We Generalize the Learning Outcomes of
Simulations?
Much of the research on simulations deals with “the
generic” simulation. While acknowledging that there
are functional and TE simulations, conclusions are
drawn as if all simulation experiences are alike. Is it
justifiable to discuss the “simulation experience,” or
must we treat each simulation experience as a unique
case?
The outcomes to be expected from a simulation depend
upon several factors. Different games are designed to
model different disciplines and to emphasize different
learning outcomes. In addition, a simulation experience
rarely consists exclusively of a game. Most game
administrators use the game as the hub for a broader
set of activities. For example, simulations may be run
as team competitions; game participants may write
plans and strategies; and the participants may deliver
oral presentations and/or submit written reports on the
performance of their team. The set of activities selected
by the game administrator will almost certainly
influence the learning that occurs over the course of the
simulation.
Given the diversity of simulation experiences that
students may encounter, it may seem fruitless to
attempt to generalize about the learning outcomes of a
business simulation. However, the situation may not be
as hopeless as it first appears. By the nature of a
simulation, virtually all games, regardless of the
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Developments In Business Simulation & Experiential Learning, Volume 24, 1997
discipline for which they are developed, require the
participants to make decisions under conditions of
uncertainty. Almost all (over 90%) simulation users
run their games as team competitions, and over 75%
require the teams to prepare written plans (Anderson &
Lawton, 1990). As a result, we should be able to draw
some general conclusions about the learning
effectiveness of simulations in their most commonly
used form.
What is needed to demonstrate the teaching
effectiveness of simulations?
Clearly specify learning outcomes
Virtually all research designed to measure the
outcomes produced by engaging in an activity requires,
by necessity, assumptions concerning the expected
outcomes of performing that activity. We cannot
construct an assessment instrument without knowing
what it is we wish to measure. For example, most
simulations attempt to put the student in the role of a
manager. Managing requires a broad range of
knowledge and skills - such things as knowledge of the
business discipline, interpersonal skills, problem-
solving and decision-making skills, etc. Consequently,
participating in a game may produce a wide range of
outcomes. To assess whether a simulation is successful
at teaching managerial skills, we must first identify
which specific skills we wish to assess and then
accurately measure them.
The list presented in Table 1, identifies learning
outcomes instructors may adopt as they strive to
educate business students. These learning outcomes
have been advanced in the simulation literature, as
targeting the skills and knowledge needed by
practicing managers. The items on the list were
compiled from a review of simulation literature. The
sources were: Miles, et al. (1986); Teach and Govahi
(1988); Teach (1990); Hemmasi and Graf (1992); and
Klabbers (1996). Miles, et al. drew items from two
1979 studies. Teach and Govahi state, “The literature
was searched to define the skills and attributes that
‘managers’ need and the tasks they employ in plying
their trade. A set of 41 tasks, skills and/or attributes
was developed (Waters, 1980) (Livingstone, 1971)
(Mintzberg, 1973).” Hemmasi and Graf drew several
items from the Miles, et al. study and added a number
of their own.
Simulation users have speculated, and in many cases
claimed, that game playing is an effective pedagogy for
achieving many of these outcomes. However, there is
evidence that some of these Learning objectives can be
developed more effectively by pedagogies other than
games. For example, Teach and Govahi (1988)
conclude that, based on student perceptions, lectures
are superior for learning to listen reflectively; cases are
best for learning a set of nine skills including analyzing
problems, conceptualizing, and writing effectively; and
experiential exercises excel for a set of 17 skills. But it
is not the purpose of this paper to review the literature
and draw conclusions about the effectiveness of the
various pedagogies. Several such reviews already exist
[for example, Wolfe (1985); Keys and Wolfe (1990)].
Rather, it is the purpose of this paper to question what
claims attributed to simulations can be supported by
the objective evidence presented in the literature.
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Developments In Business Simulation & Experiential Learning, Volume 24, 1997
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Developments In Business Simulation & Experiential Learning, Volume 24, 1997
Note: There are some additional ‘non-learning’
objectives having to do with improved attitudes toward
the course or toward business which may influence the
choice of a pedagogy. These objectives include:
Adding realism to the course 1
adding enjoyment to
class 1 adding entertainment value to course1; and
gaining a positive mental attitude -essential for a
happy, successful career in life3. 1 Miles et al. (1986);
2Teach and Govahi (1988);
3Teach (1990); Hemmasi and Graf (1992); 5 Klabbers
(1996)
Note: A “p” in the first column indicates that at least
one study exists with participant perceptions used as
the dependent variable. An “o” in the second column
indicates that at least one study exists with some
objective measure used as the dependent variable.
The Choice of Dependent Variables
Many are Based on Perceptions There is a reasonably
extensive literature concerned with the perceived effect
of participating in a simulation. [See, for example,
Hemmasi and Graf (1992); Anderson and Lawton
(1989); Teach, Richard and G. Govahi, (1988); Miles,
et al. (1986)] Many of the studies compared the
perceived learning for simulations versus that for other
pedagogies -particularly cases. The findings of these
studies have not been consistent, particularly for the
perceived learning from simulations versus cases. Even
a replication by Anderson and Lawton (1989) found
contradictory findings from the original Miles, et al.
study (1986). In general, however, simulations have
fared quite well, especially when compared to lectures.
But, of course, these results are based on perceptions
rather than on objective measures of learning.
Table 1 is presented in an attempt to identify the gaps
the current state of literature. The “p” in Column 1 of
Table 1 denotes that at least one study exists where
participant perceptions were used as the dependent
variable for a learning outcome. The “o” in Column 2
of Table 1 indicates that at least one study exists where
some form of objective measure was used as the
dependent variable for a learning outcome. (So, for
example, there have been studies measuring student
perceptions of the extent to which simulations help
them learn to resolve conflicts (item B-4 in Table 1
“p”), but there have been no such studies using some
objective measure (no “o”). For the outcome,
Increase the student’s knowledge of basic
principles and concepts of the discipline,” (item A in
Table 1) there have been studies using student
perception as the dependent variable and there have
been studies using objective tests - thus, both “p” and
“o”.) Even a glance at the table reveals that our review
of the literature found studies dealing with perceptions
for many of the learning outcomes, but objective
studies have attempted to examine far fewer of these
learning outcomes.
While objectives involving improved attitudes toward
a course or toward business (e.g., adding realism or
enjoyment to a course) necessitate using attitudes and
perceptions as the dependent measure, perceptions
alone should not be taken as a sufficient indicator of
merit for most of the other learning objectives. As far
back as 1981, Parasuraman called
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Developments In Business Simulation & Experiential Learning, Volume 24, 1997
for a movement away from perceptions to more
rigorous measures of learning. Yet, a decade and a half
later, we still speak anecdotally of the effectiveness of
simulations with few studies using rigorous, objective
dependent variables to support our claims.
A relatively recent departure from straight self-
evaluations has been the move to solicit the opinions of
team members. Instruments like the modified Stumpf’s
Strategic Management Skills Questionnaire (see
Wolfe, 19xx) and the Human Synergistics’ modified
Managerial Effectiveness Profile System (see
Anderson and Lawton, 1990) have been used in an
effort to gather more objective information than a
simple self-report. While the aim of this approach is
laudable, these evaluations are still based on subjective
perceptions. Further, the validity and reliability of
these instruments have not been established for a
classroom setting. Until such instruments have been
validated, they must be viewed with the same suspicion
as self-evaluations.
Few Studies are Rigorous
A second factor interfering with our ability to make
definitive statements about the efficacy of simulations
is that many of the studies purporting to demonstrate
the prowess of simulations have employed weak
designs. As Keys and Wolfe state, “...Many of the
claims and counterclaims for the teaching power of
business games rest on anecdotal material or
inadequate or poorly implemented research designs.
These research defects have clouded the business
gaming literature and have hampered the creation of a
cumulative stream of research.” (Keys and Wolfe,
1990).
Many studies lack control measures to ascertain the
influence of moderating variables. In addition, there
has been a lack of replication to determine whether
results are generalizable, or merely artifacts of a
particular simulation. Yet this hasn’t prevented us from
making claims as to the power of simulations.
External Validity Studies are Inadequate to Assess
Learning
A handful of studies have been conducted to in an
effort to establish the external validity of simulations.
(See, Norris and Snyder, 1982; Wolfe and Roberts,
1986; and Wolfe and Roberts, 1993).
While these studies make a useful contribution to our
knowledge of the association between successful
performance in a simulation and successful
performance on-the-job, they are not substitutes for
measures of what is, or is not, learned in simulations.
Although Wolfe and Roberts’ (1993, p 22) contend that
“external validity studies must be conducted because it
is the ultimate test of this learning technique’s value,”
these studies can not establish the learning
effectiveness of simulations. Performance in a
simulation exercise may, as Wolfe and Roberts state
(1993, p. 25), serve “as a device for assessing potential
managerial talent.” However, if we see a strong
relationship between successful game performance and
on-the-job performance, have we demonstrated that
learning from the simulation caused better on-the-job
performance or that performance in both arenas is
dependent upon similar skills and knowledge? External
validity studies cannot determine whether simulation
performance is a cause or merely reflects some other,
unmeasured variable or variables. Until we design
experiments to control (and test) for these effects, we
will not know if there is a causal relationship between
simulation performance and later career success.
DISCUSSION
The Guide to Business Gaming and Experiential
Learning (1990) has two excellent articles that address
problems in evaluating the educational value of
simulations - Burns, et al. and Wolfe. Neither of the
articles emphasizes what is perhaps the single most
difficult task in conducting research to establish the
value of simulations - the development of appropriate
dependent measures. Even if researchers are
assiduously attentive to good experimental design,
useful research results will not be achieved if the
measure of learning is invalid. Blooms Taxonomy
provides a useful guide to selecting and assessing
learning outcomes, but as Bums et al. (1990, p. 262)
point out, “...the Bloom et al. classification scheme
does not solve all the various problems in the
assessment of learning .. [even though] it does provide
a framework within which to begin systematically
working on these difficulties.”
Looking at Table 1 it is clear that many of the skills are
aimed at high levels of Bloom’s Taxonomy. Few
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Developments In Business Simulation & Experiential Learning, Volume 24, 1997
of the objectives are cognitive or affective. It will
require an enormous amount of ingenuity to develop
appropriate objective measures for most of these
learning objectives; and for most of these objectives,
we haven’t even begun to develop objective
instruments. Only after we have valid instruments can
be begin to investigate meaningfully the relative merits
of different pedagogies.
Even if valid instruments are developed, there are at
least two other factors, which further complicate our
ability to measure: (1) Relatively few instructors base
their entire course on a simulation. Most use the game
in combination with lectures, cases, etc. So, if learning
is observed, how can we attribute the learning to the
simulation rather than some other part of the course?
(2) Many, if not most, of the learning objectives listed
in Table 1 involve very broad skills. Even if a
pedagogy is an extremely powerful learning technique,
how much change can we expect to observe in such a
deeply ingrained skill as, say, decision-making just as a
result of a single course? These two factors suggest
that we will not only need valid measures, but will we
need extremely sensitive measures.
The upshot of paper is that the reason for the lack of
objective support for the learning effectiveness of
simulation games cannot simply be attributed to sloth
or ignorance. Perceptions and attitudes have been over-
used because we know how to measure them.
However, even with the best of intentions and the
greatest of rigor, we will not succeed in assessing the
educational merits of simulations until we develop
better dependent measures; measures which are
objective. Table 1 highlights the many areas where
dependent measures must be identified or developed if
we are to examine the broader effects of using a
simulation.
How Should we Proceed?
An issue that has arisen in past ABSEL meetings when
discussing the assessment of learning, concerns the
disagreement between those who emphasize the need
to measure learning versus those who advocate the
need to measure mastery. Perhaps the difference
between these two camps is more a matter of semantics
than a real difference in opinion. Nevertheless, in an
effort to clarify the situation, we offer the following:
One model for establishing learning involves
using a pre-, post-design. The difference between the
score on the first assessment and the second
assessment is taken to be the amount of learning that
has taken place. This approach yields the most direct
measure of learning since it yields a measure of change
in cognition or behavior for each individual participant.
A second model for establishing learning is an
after-only, with control group design. If we take two
randomly selected groups and expose them to different
pedagogies (e.g., one group is taught using cases while
a second group uses a game, we take the difference in
scores between the two groups as our measure of the
relative effectiveness of the two pedagogies. This
approach to establishing learning is less direct than the
first model, since we get no measure of learning at the
individual level.
We contend that the after-only, with control group
provides a more useful measure. As Burns et al. (1990,
p. 253) note, “implicitly we should be seeking to
discover and adopt the ‘best’ pedagogical climate for
our students, for, in the absence of this concern, the
educator is continually plagued with opportunity cost
and efficient time use questions.” Using this second
model does not require that we measure learning at the
individual participant level. Rather we can measure the
mastery of one group against the mastery of a second
group and draw conclusions about the relative
effectiveness of the two pedagogies. This is not to say
that only one of these models is correct. But we need to
be clear about what we intend to measure and then use
a model that is appropriate for our purpose.
Regardless of which of these two models a researcher
employs, It is imperative that the validity and
reliability of the measurement instrument is
established. We can continue to regale ourselves with
anecdotal evidence about the ‘wonders of simulation,’
but we are preaching to the converted. If we truly want
others to respect and adopt simulations, we need to
provide objective evidence as to its efficacy.
Reference available upon request.
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