Games and Simulations and Their Relationship to Learning
Margaret E. Gredler, University of South Carolina

Presented by: Judith Au, Ruth Larsen, and Massiel Pérez Maciel


Games and simulations used for educational purposes can be traced back to the 1600’s with war games to improve the strategic planning of armies and navies. In the 1950’s, the use of simulations became an important tool for the military in planning of major world powers. It is during this time that simulations became a staple in business and medical education.

Games in which the objective is to win offer competitive exercises where players apply subject matter and knowledge in an effort to advance or essentially, win. In contrast, simulations are open-ended evolving situations with many interacting variables. Here the goal is for each participant to assume their role in addressing issues, threats, problem solving etc. and most importantly, to experience the effects of their decisions as if in a real life situation.

With the technology boom in the 1980’s and advances in the use and implementation of computers in education, the development of a variety of problem-based exercises for students to solve surfaced. Further discussions are conceptual frameworks for games, simulations, current examples and unresolved issues in design and research (Gredler, 2004).

Conceptual Framework

While games and simulations both elicit a high level of active involvement in learning by giving students control of the action and outcomes, they differ greatly in terms of their purposes, feedback and design structures, and advantages and disadvantages in the classroom.


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Purposes and Design Criteria

The purpose of academic games is either to practice and review knowledge, to identify weaknesses, or to form new relationships among ideas. Games are designed to be competitive exercises among players in which the objective is to win. Five criteria are important in the design of effective academic games. Winning must be based solely on demonstration of knowledge; the game must address key content only; the game dynamics should be developmentally appropriate, accessible, and appealing to its audience without sound and graphics distracting from the learning; students should not be penalized for wrong answers; and finally, educational games should not result in one winner only. Rather, academic games should define success in terms of reaching multiple criterion, such as gaining a certain number of points, having the fewest errors, or having the best strategy. Additionally, it is essential that students receive clear feedback for correct and incorrect answers, such as points and loss of points.

Advantages and Disadvantages
Games used in the classroom can be very beneficial in increasing students' interest and participation levels. They also provide students with opportunities to apply learning in a new context. Some problems with the design of current educational games is their tendency to become distraction from the target learning, to lead to frustration when students are penalized for their efforts, and to allow success for a minority, or most successful students only.


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Structure and Design
Unlike games, the goal of simulations is for students to take on a realistic role in an evolving, real world scenario in which the student's actions have direct, authentic consequences in terms of the outcomes of problems that arise in the simulation. Also, simulations require participants to apply metacognition to solve ill-defined problems where feedback occurs in the form of changes in the status of the problem and reactions of other participants.

There are two primary types of simulations: Experiential and symbolic. In experiential simulations, learners become immersed in a complex situation in which they are one of the dynamic components. Validity and fidelity are crucial to the effectiveness of this type of simulation. Examples of experiential simulations include pilot and astronaut training programs.

Symbolic simulations differ from experiential in that the learner takes on an external rather than internal role where they act as a researcher or troubleshooter in the context of a simulated computer program. Two major types of symbolic simulations are laboratory research simulations and system simulations.

Virtual environments are a variation of simulations which involve computer-generated three dimensional environments that respond in real time to the actions of the users and are designed to draw the participant into a particular setting. Examples include a 360 degree visual panorama of a particular setting, and a virtual desktop environment using a mouse, touch screen, or keyboard. Whether or not a virtual environment can be considered a learning simulation depends on the nature of the problem the learner is faced with and the complexity of the abilities required of the learner.

Unlike games, which involve discrete, static problems, simulations bridge the gap between the classroom and the real world by presenting ill-defined, authentic opportunities for problem solving. Also, they can reveal student misconceptions about the content. Finally, they can provide information about students' problem solving strategies.

Research in Games and Simulations

Educational Games

A key feature of educational games is the opportunity to apply subject matter knowledge in a new context (Gredler, 2004). Games like "Underwater Sea Quest" permit students to design their own computer game using particular content such as the laws of motion. Educational games are more widely accepted by parents and teachers of elementary students, but interest for educational games starts to decline by middle school.

Experiential Simulations

Social-process simulations

Social-process simulations are one of the three categories of experiential simulations. They are often developed to provide experiences in using language to communicate for various purposes. A suggested application is that of providing environments for learning-disabled students to develop independent living and survival skills. A specific example was shopping in a supermarket. Participants used a joystick to navigate the aisles and selected items on their list with a mouse. When students were taken to a real store, severely disabled students who had participated in the simulation were more successful at navigating the store than their counterparts in the control group.

Diagnostic simulations

In diagnostic simulations, participants take processional roles that involve problem solving. These types of simulations can be created for any age group, although the majority can be found in higher education. An example is with students in a second grade class were given the opportunity to become paleontologists. Through the simulation they excavated fossils in virtual gridded dig sites using the appropriate tools. Students were asked to predict what type of fossil they had excavated and verify using pictures found on a database. The post data from this simulation showed positive learning outcomes, internalization of scientific terminology and personal investment in the exercise.

In higher education these types of simulations were developed for medical education, but they have now expanded into relating fields such as counseling. During the 1980's simulations were introduced into rehabilitation counseling to enhance students' clinical problem-solving skills. Students with less than optimal approaches work with a mentor to develop compensatory strategies.

Data management simulations

The largest group of experiential simulations is data-management simulations. Data-management simulations include competition among management teams as a major variable. Some instructors, for example, allocate as much as 25% of the course grade to students' performance. One problem associated with an emphasis on winning is that, in the real world, major quarterly decisions are not collapsed into a brief 45-min, time period. Also, a focus on winning can detract from meaningful strategic planning. Other deficiencies include not forcing participants to deal with the conflicting demands of multiple parties and not allowing for the range of strategies currently taught in management courses. Students who did not perform well in simulations were strong in basic knowledge but lacked metaknowledge. Metaknowledge (knowing what we know and what we do not know) is essential to the identification of key issues in data collection to solve problems.

Symbolic Simulations

Symbolic simulations are referred by some as microworlds. According to Saucer a microworld is "a computer-based simulation of a work or decissionmaking environment" (as cited in Gredler, 2008, p. 575). Simulations in Science education showed that student who used simulations for discovery learning did not outperform their counterparts. Both groups still held key misconceptions. When asked to build a real circuit the experimental classes were able to outperform the control groups. Experimental were able to outperform the control groups because they were able to use the simulation as a feedback device, the control group received feedback through their physical trials. However, neither of the two groups were able to design a circuit using the theoretical model.

Visual field trips are the combination of hypermedia with video images that can create a virtual experience for students who are fulfilling roles as researchers. Programs such as A Virtual Field Trip-Plant Collecting in Western New South Wales and Blue Ice: Focus on Antarctica provide students the opportunity to collect and analyze data, students research wild life and weather topics. Based on students responses on surveys the virtual field trips were helpful in that they helped students use their time wisely and prepare for actual field trips.

Simulations have also been incorporated into introductory psychology courses, and business courses to help students learn how to troubleshoot in real world-situations. Data showed that students who used the simulation for psychology did not receive information on their shaping. Lower performing students who completed the troubleshooting simulation were more likely to check the gauges and components of the simulation than the efficient problem solvers. According to Recker, "Lower-performing students also implemented a strategy of confirming leading hypothesis instead of choosing tests that served to disconfirm a maximum number of possible hypothesis.


Areas such as deal with clients and their services most often implement simulations where students diagnose and manage individuals' problems. Business education relies on team exercises that help students manage finances of a company or institution. There is no clear outcome in favor of simulations. When students are expected to learn how to use the program and learn the material all at once it can lead to cognitive overload.

Design and Research Issues

The impetus behind, “Bridging the gap” between conference rooms to the real world and applicability of business and medical education textbooks to the real world was the expansion of simulations. However, one must understand that games and simulations are not to replace teaching and learning rather, games and simulations are activities one may use to build upon students knowledge and what was previously taught through traditional instruction. These activities and simulations must be looked at as “culminating experiences; they are not devices to teach basic information”(Gredler, 2004).

If prior instruction is missing, potential failure is high in students who use games and simulations to learn content. In this way, students acquire skills prior to the students engagement in a simulation. Instruction should be modeled and taught with the expected research skills meaning, planning, executing the experiment, and collecting data to evaluate. Succeeding the instruction of research skills, acquisition of capabilities to develop the students’ conceptual models follows and students additionally, acquire the skills needed to test in systematic ways.

Due to what Sweeler, van Merrienbaer (1998) refer to as extraneous cognitive load, students working memory may be limited. Students ultimate goal is to become and meta-cognitive expert as a planner, monitor, and evaluator. Albeit, designing courseware or simulation at a basal level may lead to student boredom and lack of engagement. Conversely, designing a workload too difficult, assumes that the learners know more then what they really know. One must keep in mind that it is not only important to know what material you will be teaching, but also what your learners need to be taught.
Final note - as Horowitz (1999) suggests, the associated context must be developed carefully to accomplish that purpose. That is, addressing the prior questions and prior knowledge is detrimental to explore the potential of simulations for both cognitive and meta-cognitive learning.