Game Development Reference
In-Depth Information
In addition to business complexity, a number
of behavioral factors come into play and make
the challenges, which organizations face, even
greater. First, the bounded rationality of the eco-
nomic actors (Simon, 1997) is a supplemental
element, which exacerbates the situation. Indeed,
decision makers generally opt for the first satis-
fying solution, and hence, they stop looking for
better alternatives. Second, decision makers, like
people in general, are prone to the misperception
of feedback. This means that their performance
in complex and dynamic systems is hindered by
non-linearities, time delays and feedback struc-
tures (Sterman, 1989). Therefore, decision makers
will tend to make poor decisions. Third, decision
making in dynamic systems is hard because it
calls for dynamic decision making, that is, where
a stream of decisions are interdependent on one
another. Last, decision makers are also limited
by the magical number seven, plus or minus two
(Miller, 1956). This number sets the maximum
number of cues, which can be simultaneously
considered by people while they evaluate a prob-
lem. Consequently, organizations are more and
more eager to collaborate around structured and
emergent manufacturing frameworks such as pro-
duction networks. These networks entail the joint-
manufacturing of products and are regarded as a
new form of co-operation between organizations
(Wiendhal & Lutz, 2002). Although, organizations
do take advantage of being a part of production
networks, since today competition takes place
between entire supply chains, or networks, instead
of single organizations, production networks are
nevertheless vulnerable and inflexible since many
disparate entities populate them, increasing the
risk of collapse due to external shocks of market
instability, or boom & bust cycles.
Dynamic systems such as production networks
confront their workforces with ever-changing
working environments (Baalsrud Hauge et al.,
2006). This stresses the need for continuous
learning, which constitutes the true competitive
advantage for organizations (Senge, 1990, p.
17). Moreover, the learning rate of the organiza-
tion must be higher than that of competition, so
that the former can survive (de Geus, 1988). An
effective tool for mediating learning is serious
computer games, also known as business games
(Warren & Langley, 1999). Computer games not
only convey hard skills such as the understanding
of how complex systems operate, such as produc-
tion networks, but also mediate soft skills, like
collaboration and communication (Scholz-Reiter
et al., 2002). Even though it is shown that the use
of games are useful for mediation of soft skills
(Windhoff, 2001), it is still difficult to find suit-
able methods for measuring the learning outcome
of serious games.
The objective of this chapter is to show differ-
ent approaches for the evaluation of the learning
outcomes of serious games and to discuss the
advantages and disadvantages of the methods.
The chapter is based on the authors' experience
of using games in lecturing activities in the field
of product development in production networks.
The target group for the games is engineering
students. The first section of the chapter deals
with the background of using serious games for
the mediation of skills, and gives an introduction
to the two games. These games have been used
in several courses over several years, so there are
a lot of evaluation results available. The second
section deals with the different evaluation meth-
odologies that were applied. It also explains the
different results. The final section is a comparison
on the used methods and also includes a discus-
sion of the advantages and disadvantages of the
different methods.
BACKGROUND
Serious games have a long tradition in the educa-
tion of military officers (Hays and Singer 1989).
In military education they are mainly used for
simulation and planning of war operations. In the
1950s the application area of simulation games
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