Computer-simulated experiments
and computer games: a method of design analysis

Jerome J. Leary

Department of Mathematical Sciences, University of Brighton

This paper describes a new research programme to design computer-simulated experiments in the
field of fuels and combustion, and describes a method of categorization based on a taxonomy
proposed by Gredler. The key features which enhance science content and process skills are
identified The simulations are designed to be as realistic as possible, and are built using three-
dimensional computer-aided design, rendering and animation tools, with the intention of creating
an interactive virtual laboratory on the computer screen. A number of computer games are also
categorized against the computer simulations and the same taxonomy for comparison. The paper
then describes how designers of computer simulations can add to their own learning by
retrospectively analysing their own simulations.

Background
Through the new modularization of the undergraduate science degree at the University of
Brighton, larger numbers of students are choosing to take some science modules which
include an amount of laboratory practical work. Indeed, within energy studies, the fuels
and combustion module, for which the computer simulations were written, has seen a
fourfold increase in student numbers from twelve to around fifty. Fitting out additional
laboratories with new equipment to accommodate this increase presented problems: the
laboratory space did not exist; fitting out the laboratories with new equipment would
involve a relatively large capital spend per student for equipment that would be used
infrequently; and, because some of the experiments use inflammable liquids and gases,
additional staff would be needed for laboratory supervision.

However, the option of running a science-based module such as fuels and combustion
with students doing fewer practicáis was not desirable from an educational view, since
experimentation plays a key role in enabling students to develop science process skills
(Gibbs & Jaques, 1990), and a reduction in the amount of experimentation would have a
consequential effect on students developing those process skills. The necessity of teaching

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ALT-] Volume 3 Number I

the maximum amount of science content and science process in a cost-effective manner
leads to consideration of computer simulations as a supplemental means of instruction.
Indeed, the opportunity was seen that the total number of experiments that an individual
student performed could be increased with the use of computer simulations.

Much research has already confirmed that simulations are no less a viable means of
instruction than any other form of instructional approach (for example, Hedlund et al,
1986; Sherwood et al, 1984), and in fact studies have shown that students achieved as well,
if not better, on the post tests through computer simulations. Magín and Reizes (1990)
suggest there is considerable unexploited potential for computer simulations in education
in the field of laboratory experiments, but in reality there is little computer simulation
software available for undergraduate teaching in the field of science. In 1992, this dearth
lead to the instigation of a research programme by the author of this paper in computer
simulations within the University (Leary, 1994). The latest three-dimensional computer-
aided design, rendering and animation tools would give the experiments a sense of realism.

The experiments

It was initially planned to develop eight computer-simulated experiments in the field of
fuels and combustion. This phase is now complete and the simulations are currently being
tested on second-year energy studies students. The students use the simulations to:

• determine the gross and net calorific values of natural gas using a Boy's Calorimeter;

• determine the upper and lower flash points of liquid hydrocarbon fuels using the
Pensky Martens apparatus;

• measure the relative density of liquid hydrocarbon fuels by the balance method and
with hydrometers;

• determine the viscosity of liquid hydrocarbon fuels by using a Redwood Viscometer;

• determine the fractions of light hydrocarbons in gasoline using a distillation column;

• measure the pollutant emissions from a Wankel engine;

• determine the constituents of coal and efficiency of combustion by measuring the
products of combustion in a fluidized bed boiler;

• investigate diesel-engine efficiency and diesel knock by varying fuel injection profiles.

Classification of simulations
Before undertaking any redesign or refining of the initial eight computer-simulated
experiments, or indeed developing any further simulations, it was felt necessary to
perform an analysis to identify the factors involved in optimizing the success of the
learning outcome and, thereby, add to the designer's own learning. As a comparison,
some popular computer games would also be analysed on the same basis. The model
chosen for the analysis was Gredler's taxonomy (Gredler, 1986) of computer simulations
which identified four categories of simulation:

41



¡eróme J, Leary Computer simulated experiments and computer games

1. Structured questions with associated graphics. The learner views the simulation, then
answers specific questions. These can be either drill-and-practice or tutorial type exercises.

2. Assignment of variables with results exercises. One or more independent variables are
set by the learner and the results on the dependent variable or variables are observed
through several trials. The nature of this simulation is cyclic, feedback being related to
variables affecting other variables.

3. Diagnostic simulations. This type of simulation usually involves applied decision-
making in a professional content area leading to the development of 'expert knowledge'.
A decision variable is selected by the learner after several episodes, and there is usually
one large scenario.

4. Group-interactive. This is applied decision-making in a real social context.
Distinguishing the correct approach in context is the principal learning outcome. There is
no right or wrong answer in this case, and the results of decisions are difficult to
determine. None of the laboratory experiments falls into this category, so the category is
not considered further.

Computer games
The following computer games were chosen arbitrarily on the basis of being commercially
successful and representing a cross-section:

• Microsoft Flight Simulator

• Pong

• SimCity

• Doom

Anal/sis of the taxonomy
Table 1 identifies the fit between the eight computer-simulated experiments described
above, the four computer games, and the taxonomy proposed by Gredler (1986). The
descriptions of type, or categories, are listed on the left, and the shaded boxes identify
those items which match each of the four classifications. If there is a match between a
computer simulation or a computer game and a category, a star is used to indicate this.
Then, in looking at the data in each of the columns, it is a matter of identifying a match
between each column and each of the columns with shaded boxes. If the'pattern is the
same, the category of simulation is clearly identified as being either of the four types. The
taxonomy splits the category-structured questions with associated graphics into two other
categories: drill and practice, and tutorial.

The conclusions that can be drawn from the analysis of the simulations in the table are
that:

• there is a weak but marked correlation between both the tutorial and variable
assignment categories and the majority of the simulations;

42



Nature of situation presented
I.I
1 1

1.3

1.4

Specific problems each requiring a particular answer •
A continuing situation that includes two or more variables [

Complex real-world problem that takes new directions in response to in

|

prt"

Use of visuals and data | |

Less«

II A1

n.A2
H.A3

>n preparation

Visual presentation of a real-world problem •
Graphic presentation of a set of data I -'V

Feedback to students

II.B1

II.B2

II.B3

II.B4

Identifies right and wrong answers

Provides clue's to errors in student's selected strategy

Identifies outcomes of student's assignment of values to variables 1

Describes new direction in a complex problem that follows student's inp

Nature of instructional process

HI.1

m.2
in.3
H1.4

IU.5

Solve a set of discrete, specific problems

Identify new examples of concepts or rules

Disco ver the optimum strategy to solve a problem 1

Discover the set of values identified as optimum for several variables I

Implement the knowledge acquired in a particular subject area

V
ar

. A
ss

ig
n

.

I

u t |

•

Experiment Number

1

*

r
4-
Ü

*

**

2

*

3

*

*

4

*

*

*

5

*

*

*

6

*

7

*

*

8

*

•—•

*

*

*

*

*

*

*

•

H H Condition
i l l Optional

Table I: Classification of computer simulations and computer games



Jerome J, Leary Computer simulated experiments and computer games

• there is strong use of visuals in both demonstration of items and presentation of the
running experiments;

• the simulations provide only weak feedback and do not identify errors in the student's
selections;

• simulation number 3 shows marked characteristics of the drill-and-practice class;

• the nature of the instructional process of simulations 7 and 8 is akin to the diagnostic
class.

This analysis provides an extremely useful agenda for further design work on the
simulations with the issue of weak feedback heading the list. It also provides an insight
into the timing of the use of the simulations within the fuels and combustion module as a
whole, as it is apparent from the taxonomy that simulations 1, 7 and 8 are partly
diagnostic in nature and require some prior knowledge, that simulations 2, 4, 5, and 6 are
mainly variable assignment and require minimal prior knowledge, while simulation 3, as a
drill-and-practice type, can be used without any prior knowledge of measuring relative
density. Lastly, the analysis also provides a level of comfort as it confirms that about half
of the simulations fall into the assignment of variables class, which Clariana (1989)
indicates do result in improved learning of specific content and also of more generalized
cognitive processes.

The games were also analysed using Gredler's taxonomy and the results are also shown in
Table 1. It is quite interesting that there is a strong correlation in all cases with variable
assignment and diagnostic categories. The emphasis in these games is providing an
environment in which players can test their skill, be it landing an aircraft, playing ping
pong, managing a city or doing battle, and although the teaching of these skills is not a
primary aim of the software - commercial success is - the development of the skills does
occur, and the players in all cases do improve their performance and increase their
knowledge.

However, although the analysis using the taxonomy provides much useful information, it
leaves unanswered some questions concerning the effectiveness of the simulations. This is
because these questions primarily concern learner understanding rather than typological
issues of context and feedback, and are, therefore, outside the ambit of Gredler's
taxonomy. When addressing issues of learner understanding there is, unfortunately, no
single reference point like Gredler's taxonomy, and a comprehensive view of the issues is
gained only by studying relevant literature, some of which is discussed below. They
include areas such as enhanced reality, learner guidance or learner control, level of
questioning, degree of fascination, and transfer.

Further issues
Enhanced reality. The eight simulations developed for the fuels and combustion module
are, thanks to the latest 3D graphics tools, quite realistic, and this is borne out by student
feedback. Realism is considered important as many of the undergraduates on the module
have no prior experience of what some fuel-testing apparatus looks like. Thus, important
features have been enhanced using animations and cut-away views. For example,

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ALT-J Volume 3 Number I

Figure I: Boy's Calorimeter
simulation

simulation number 1, which uses a Boy's Calorimeter (Figure 1) to determine the gross
and net calorific values of gaseous fuels, shows a cut-away view of the inner workings of
the calorimeter, allowing the student to view the direction and magnitude of water flows,
gas flows and the combustion flame. This view conveys additional information which
would not be available in a conventional laboratpry. Furthermore, where possible, task-
irrelevant cues have been eliminated or reduced. Again using simulation number 1 as an
example, the experiment would, in reality, involve a mass of wiring, connecting
thermocouples and other components to temperature meters, whereas the simulation
shows only simple digital thermometers. The simulations also highlight features that may
not be visible in reality, such as heat flows, and show magnified views, such as a single oil
droplet burning, and slow down time so that fast processes, such as combustion occurring
in a diesel engine, can be studied in detail in slow motion (Figure 2). Successful
implementation of these features was a key design consideration, and the ability of the
eight simulations to aid the students' understanding more effectively than traditional
laboratory-based experiments is therefore deemed to be high.

Learner guidance or learner control. It has been shown, for example by Vockell & Rivers
(1984), that guided discovery computer simulations are generally better than the
exploratory computer simulations, particularly in the development of general problem-
solving skills. This is contrary to the commonly held view that free exploratory discovery
is best for learning science process, and that guided discovery is best for learning science
content. In any case, simulations of a diagnostic nature require prerequisite knowledge
and provide a test environment to try out that acquired know-how. In other types, it is
more difficult to determine in advance exactly how much learner-guidance to include in
each of the simulations, so the method for deciding this is an interactive one based on
student feedback. The amount of student questioning is monitored and used to include or
remove additional information.

45



Jerome J, Leaiy Computer simulated experiments and computer games

Figure 2: Diesel engine
simulation

Level of questioning. This is aimed at stimulating reflection on what has occurred during
each experiment. A correct level of questioning is important because reflection is needed
to create a deeper understanding, and in order to develop cognitive processes in the
learner. In a study by Merrill (1985), the use of higher-level questions in computer
simulations was supported, but not lower-level questions where individual attributes of a
concept are treated in isolation. In the simulations described in this paper, students are
asked a set of higher-level questions at the end of the experimentation phase to encourage
reflection. In diagnostic simulations and computer games, students are already working at
a deeper level, testing and refining their mental model continuously, and so additional
questioning is less appropriate.

Fascination. The level of difficulty of a simulation is important. Ideally, there should be
an exact match between the learner's ability and the difficulty of the simulation to
stimulate interest. If a simulation is too easy it will result in boredom and poor skill
development, whereas if a simulation is too difficult it will cause anxiety in the learner
and a failure to understand or complete the task. However, if there is an exact match, the
learner is more likely to proceed with maximum concentration, or even become fascinated
with the task, at which point learning occurs with consummate ease (Mayhew 1994) as the
student becomes addicted. With computer simulations, it is much easier to match the
difficulty of the task with the level of competence of the learner. Some computer games
do this particularly well with different skill levels. With real experiments, this process is
limited by physical constramts. Student feedback suggests that the level of difficulty for
one or two of the experiments is too low, in particular simulation number 3. Table 1
confirms that this simulation belongs to the drill-and-practice class, which are routine in
nature, and therefore we can conclude that simulation number 3 needs to be redesigned to
make it more challenging.

Validation. One desirable learning outcome from any experimentation is development of
skill in validation of results. Rather than trusting implicitly the output of the experiment, as
students are more likely to do with computer generated results, in these experiments

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ALT-} Volume 3 Number I

students are made aware that they should treat the measurements from the computer
simulations with the same degree of caution as they would measurements from equivalent
real experiments. Several processes are incorporated into the simulation to ensure that good
results can be obtained only with careful procedure. For example, a mathematical model
which is dynamic, with built-in time delays, thermal inertias and so on; a measurement
variation or 'noise' added to measurements; and, thirdly, variations introduced into the
parameters of the model so that it changes gradually over time in a random manner. The
level of these contingencies actually built in determine the development of experimentation
skills and lead to a fuller appreciation of the nature of empirical investigation.

Transfer. The above issues directly affect transfer, that is, the ability of a student to use
knowledge in either a similar context (near transfer) or a different context (far transfer).
This is tied in with whether the student is gaining a surface knowledge or a deeper
understanding (Entwistle, 1993). To perform far transfer, the student must acquire a
deeper understanding, and in general simulations of a diagnostic class are better at this
than drill and practice. However, other issues are also important, for example enhancing
reality, providing an optimum level of learner guidance, generating higher level questions,
and making the simulations fascinating all help the student achieve a deeper
understanding and obtain skills in both near and far transfer.

Conclusions

It is natural for designers of simulations to want to undertake some form of analysis of
their work to identify how their own learning can be improved. More effective designers
lead to more effective simulations. Educational software also has a lot to learn from
computer games in their potential to support that old adage that learning can be fun.
Gredler's taxonomy is a useful tool for assessing the context and feedback characteristics
of simulations and comparing them to other types of software, such as computer games,
and it can provide useful pointers for retrospective design work. An assessment of the
effectiveness of characteristics beyond typological considerations is, however, much more
difficult as there is no single reference point as yet that provides a model for comparison.
The analysis of issues such as enhanced reality, learner control or program control, level
of questioning, fascination, and transfer, is, therefore, judgmental and often subjective.
As designers' judgement about the effectiveness of their own simulations may be clouded
by their close proximity to the development work, other judgements such as the students'
feedback should be taken into account. Despite the problems associated with the
assessment of learner understanding, the exercise outlined in this paper encourages the
designer to take a holistic view, and provides criteria which will tend to make his or her
work more effective.

References

Clariana, R.B. (1989), 'Computer simulations of laboratory experiences', Journal of
Computers in Mathematics and Science Teaching, 8, 2, 14-19.

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Jerome J, Leary Computer simulated experiments and computer games

Entwistle, N. (1993), Recent Research on Student Learning and the Learning Environment,
Centre for Research on Learning and Instruction, University of Edinburgh.

Gibbs, G. & Jaques, D. (1990), Labs and Practicals, Oxford, Educational Methods Unit,
Oxford Brookes University.

Gredler, M.B. (1986), 'A taxonomy of computer simulations', Educational Technology,
26, 4, 7-12.

Hedlund, D.E. & Casolara, W.S. (1986), 'Student reactions to the use of computerised
experiments in introductory psychology', Educational Technology, 26, 3, 42-5.

Leary, J.J. (1994), 'Computer simulated practicals', paper presented at the 29th
International Conference of the Association for Educational and Training Technology
(Napier University, Edinburgh, 11-13 April).

Magin, D.J. & Reizes, J.A. (1990), 'Computer simulation of laboratory experiments: an
unrealised potential', Computers and Education, 14, 3, 263-70.

Mayhew, J. (1994), Motivation, Interest and Learning, School of Law, Social Work and
Social Policy, Liverpool John Moores University (forthcoming).

Merrill, J. (1985), 'Levels of questioning and forms of feedback: instructional factors in
courseware design', paper presented at the Annual Meeting of the American Educational
Research Association (Chicago IL, March 4).

Sherwood, R.D. & Hasselbring, T. (1984), A Comparison of Student Achievement Across
Three Methods of Presentation of a Computer Based Science Simulation, Technical Report
Series, Report No. 84.1.5 (ERIC Document Reproduction Services No. ED 262 750).

Vockell, E.L. & Rivers, R.H. (1984), 'Computerised science simulations stimulus to
generalised problem solving capabilities', paper presented at the Annual Convention of
the American Education Research Association (68th, New Orleans LA, April 24).

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