Learning Through Game-like Simulations

Maria Fasli and Michael Michalakopoulos 

University of Essex, Department of Computer Science, Wivenhoe Park, Colchester CO4 3SQ, UK

{mfasli;mmichag}@essex.ac.uk

Abstract

1. Introduction

During the last few decades the very nature of teaching in modern universities has changed. Whereas higher education was once thought of as primarily the process of transmitting knowledge through formal presentations, a growing body of research has made it clear that student motivation and engagement play fundamental roles in learning. Setting challenges, goals and problems which are engaging is a key factor in the learning process. As a result, the idea of incorporating gaming elements into learning has received increasing attention recently, as games are known to engage the user through providing a challenge and immersing them into an environment.

This paper discusses an approach to active learning that uses gaming elements. The structure of the paper is as follows. First game-based learning is discussed. The following section describes the background and motivation for this work. The presentation of a simulation game that has been developed and used in teaching follows. Next the experiences from using this exercise as well as the observed benefits of such an approach are presented. The subsequent section discusses another game which has been developed for artificial intelligence courses and allows students to develop and experiment with search techniques. The paper ends with the conclusions.

2.     Game-based learning

Research has shown that students learn better and retain more when they actively engage in the learning process. Active learning strategies involve engaging the students in higher-order thinking tasks such as analysis, synthesis and evaluation. A prerequisite for engagement is motivation; motivated students learn better and hence progress faster.

Pedagogists and researchers have recently shown an increased interest in incorporating gaming principles into teaching and learning (Kirriemir and McFarlane, 2004; Prensky, 2001). Games have been the subject of research in a number of disciplines, including education, computer science, media and cultural studies and psychology. 

Although there is no standard classification of games, Herz (1997) presents the following major categories:

• action games

• adventure games

•  fighting games

•  puzzle games

•  role-playing games

• simulations

• sports games

• strategy games

Some games may fall into more than one categories and this taxonomy does not take into account the number of players that are involved in a game. 

Prensky (2001) identifies the following characteristic elements as being fundamental in games:

• Goals and objectives. Almost every game has a goal and it gets to an end.

• Competition. Games provide challenges to the user; she encounters difficulties and has to overcome obstacles, solve riddles or even face adversaries either operated by the game engine itself or perhaps other players.

• Outcomes and feedback. To keep the user's attention throughout a game she is rewarded or punished for her actions using most of the times some sort of points system. The utility or score achieved at a certain point in time in the game reflects the user's progress and her/his skills.

• Interaction. Games are highly interactive and in some games the content may even be generated dynamically (Fairclough and Cunningham, 2003).

• Story. Games provide a concrete story line, an engaging scenario, and complex games usually involve multiple levels all of which need to be passed in order to win the game.

• Rules. Games have specific rules which are known to the players in advance.

As motivation and engagement in the learning process are key factors, there is a huge potential in integrating gaming elements into an educational context. Games have a number of characteristics that make them attractive from a pedagogic and instructional point of view. First of all, they immerse users into a world, they are interactive, engaging and fun. They encourage active learning and motivate participation and even persistence. Additionally, they provide instant feedback on the user's actions. In some knowledge domains, games may be the only possible means of simulating and practicing real world problems. A successful integration of gaming principles and techniques in a learning environment would make learning itself more fun providing a challenge for the students and motivating them to learn, help them learn more effectively and improve their overall experience.

Learning by doing is a powerful learning tool. Even tedious and difficult tasks can be engaging and fun when they are part of a good story (Klaila, 2001). Thus, borrowing concepts and principles from games, students can be presented with highly interactive and engaging game-like exercises that provide challenges and help them apply knowledge learnt. Providing immediate and relevant feedback while playing the game and positive reinforcement are additional elements to effective learning that can be borrowed from gaming.

What can be taught through games? Facts, principles, cause and effect, and complex problem solving among other things.

There have already been attempts to incorporate gaming elements in learning environments. For instance there have been a number of works in using games to teach specific concepts to children. Some of these commercial systems have been recognized to further skills and abilities such as deductive reasoning, memorisation or cooperation. The commercial game Where is Carmen Sandiego? (McFarlane et.al., 2002) is for children  nine years old and over and helps them develop their knowledge of U.S. geography, their vocabulary and deductive-reasoning skills, as well as practice map reading. Monkey Wrench Conspiracy (Prensky, 2001) is a videogame tutorial, which teaches how to use a complex CAD program. Each level corresponds to an exercise in that program, while the exercises are enveloped in a script. ToonTalk (Kahn, 1999) is a system developed with children in mind in order to teach programming concepts, though not how to write source code. Spion (Molla et.al, 1988), which uses Artificial Intelligence techniques, has been used for years to teach the German language using a textual adventure game about spies in the years prior to Germany’s unification. More recently, Gomez-Martin et.al (2004) have developed a system called JV2M borrowing ideas from games to teach programmers with Java knowledge the internal workings of the Java Virtual Machine. Their approach uses Case Based Teaching (CBT) and pedagogical agents. JV2M provides a background story and a virtual environment in which the student can manipulate objects and interact with agents and other characters and has to solve exercises which incorporate concepts at different levels.

3. Motivation and Background

In Computer Science teaching intelligent agents and multi-agent systems involves covering a wide range of topics including architectures, coordination and cooperation as well as negotiation among agents. Using practical examples and providing realistic scenarios where this technology could be applied is highly desirable. For instance, teaching how intelligent software agents can be used for negotiations in electronic commerce involves drawing from computer science, artificial intelligence, economics and game theory, negotiation protocols, market mechanisms and strategies. In this context, semi-autonomous software agents are continuously running entities that negotiate for goods and services on behalf of their users, reflecting their preferences and negotiation strategies. Ideally, we would like students to have the chance to have a hands-on experience in an electronic marketplace. However, experimenting in real markets is not possible. The use of market simulations offers the only realistic alternative if one wants to provide the students with the opportunity to practice.

The intrinsic value of simulations in education has long been recognised as they offer a safe, inexpensive and effective way of training. In certain sectors such as medicine and business, simulations have been extensively used for training purposes. In the military for instance, simulations have been used to teach pilots to fly as well as for combat scenario training (Stottler, 2000). The pedagogic benefits of using simulation are highlighted by Laurel (1991):

“Educational simulations (as opposed to tutorials and drill-and-practice forms) excel in that they represent experience as opposed to information. Learning through direct experience has in many contexts, been demonstrated to be more effective and enjoyable than learning through ‘information communicated facts’. Direct, multi-sensory representations have the capacity to engage people intellectually as well as emotionally, to enhance the contextual aspects of information, and to encourage integrated, holistic responses.”

But as Prensky (2001) points out a simulation is not necessarily a game. In order for a simulation to be characterised as a game, it needs to include additional elements such as rules, goals, competition, interaction and feedback, and fun. It is these characteristics that differentiate simple simulations from simulation games.

Although simulation games are quite common, multiple player simulation games are scarce. Playing against a software system or a character controlled by the game is challenging, but playing against other users offers additional benefits. Students can learn from each other without the direct intervention or input from the instructor and in addition develop their communication skills.

Our aim was to provide an environment where students could practice principles taught, experiment, but also learn from each other by participating in realistic multiple-player simulation games. The underlying idea was to integrate concepts and techniques from gaming in a framework that would also support active learning, with the aim being to harness the motivational power of games and the pedagogic benefits of learning through doing.

4. The Long Play CMG game

The Computer Market Game (CMG) as described in (Fasli and Michalakopoulos, 2004) has been used for assessment purposes in a graduate course on agent technology for e-commerce since 2004. The game simulates a small marketplace in which agents need to satisfy clients by assembling personal computers (PCs) according to their preferences. The goods are traded in electronic auctions. The students need to endow their software agents with a strategy in order to compete successfully in the marketplace. The game is highly interactive and there is a clear goal that needs to be accomplished. The users obtain constant feedback on how their agents are performing in the marketplace, and there are rules that need to be observed in order to participate. The element of competition is inherent in this game, as there are other players that attempt to achieve similar objectives at the same time in a constrained environment where the availability of goods is limited. How well an agent performs does not only depend on its own strategy and actions, but also on the actions of the other agents which inadvertently affect it.

In the Spring term of 2005, apart from using the original game with some modifications as part of coursework, we decided to implement a version of the game suitable for human agents and ask the students to experiment with it. The aim of this exercise was to enable the students to experience first hand the sort of problems that human traders face in complex marketplaces and in particular the problems that their software agents face when they compete.

The Long Play CMG game lasts 22 minutes and requires six human players to participate. Each player is a supplier agent whose task is to assemble PCs for its five clients. There are three types of parts that make up a properly working PC: a motherboard, a case and a monitor. There are three types of motherboards MB1, MB2 and MB3 with different CPUs. Cases come in two different types, one with a DVD player (C1) and the second with a DVD/RW (C2) combo drive. There is only one type of monitor.

Figure 1. Example of client preferences for a player.

The components are available in limited quantities and are traded in different auctions. Figure 2 summarizes the quantities and respective auctions.

Figure 2. Component availability and auction types.

To give the players some time to study the client bonuses, the first auction starts 3 minutes into the game. Figure 3 illustrates how the auctions for the various items are scheduled during the game. The thick part at the end of some of the lines, indicates that the auction closes sometime within that minute, but the exact time is not revealed beforehand.

Figure 3. Auction schedule in the Long Play CMG.

A player's success depends on the satisfaction of her clients. For each client that has been allocated a completed PC the agent gets 1000 monetary units plus any bonuses for upgrading to better components. If no completed PC is allocated to a client, its utility is 0. A client's i utility CU_i is:

CU_i= 1000 + Motherboard Bonus + Case Bonus

For every extra item that the player buys which exceeds the quantity needed to satisfy her clients she has to pay a penalty which is perceived as a storage cost and is determined at the beginning of the game as a random amount between 150 and 300 units. This has been added as a disincentive to buy additional items surplus to requirements. An agent's utility function (AU) is the sum of all the client utilities minus the expenses and any penalties:

AU=sum(CU_i) - Expenses - Penalties

The students can compete in this game by submitting bids for the different goods through a web page, Figure 4.

Figure 4. Participation web page for the Long Play CMG.

They can observe what happens in the game and what the other participants do through an applet, Figure 5, although they cannot know the others' strategies.

Figure 5. The applet for the Long Play CMG.

5. Experiences

We used the game described above to run a series of experiments during a two-hour laboratory session. The group included 16 students who were divided into six groups of 2-3 persons and were assigned team names in order to control the conduct of the game. Prior to running this exercise, the students had had experience in using the e-Game platform to schedule and participate in different types of simple auctions. They were also familiar with the general setting as the original CMG is used in coursework for which the students had started working on at the time that we ran the exercise. Our aim in running this exercise was to introduce the students to the uncertainties and problems in complex markets and give them an insight into what the software agents that they build have to face. Also we wanted them to get a taste of the problems of competing in auctions with multiple, substitutable and interrelated goods.  

Each game was followed by a debriefing session led by ourselves during which the students had to discuss the game and what had happened and their observations. They had to comment on their own approach, how this had affected the marketplace and the other participants, and in turn how they had been affected by the other participants' actions and strategies.

The results have shown that this form of interactive exercise has significant pedagogic benefits for the students. The learning process is interactive. As such it provides the students with a deeper experience, motivates them and helps them retain knowledge. As this is a simulation game with a realistic scenario they can put into practice principles taught. Since the outcome of a game does not only depend on one's (or a team's) approach, but also on the other participants', this makes the game even more challenging. The extended length of the game and the three minutes leeway allowed for considering the clients' preferences in the beginning provide an opportunity to lay down a basic strategy.

Most importantly, students have the opportunity to interact with other students through this game. Unlike traditional simulations, the Long Play CMG game allows the participation of six players while viewing is unrestricted. When one plays against other real players with similar objectives, the competitive element is more powerful which in turn makes the game more engaging, interesting and fun. Not only students can observe their own results, but also those of other students (although they cannot know their exact strategy). After each game they discussed different approaches, what worked, what didn’t while trying to understand why. This helped them understand relations such as cause and effect by analysing the consequences of different strategies. They seemed to enjoy the process and did not regard this as an "exercise" in a traditional lab setting, but as a game that they had to play to the best of their abilities. 

At the end of the exercise students were asked to provide feedback on their experience. Among the comments received were:

- I must say the game we played in the lab was very interesting and I really enjoyed it and in the process was also able to understand the concept of virtual market place.

- It was a good experience. I think with regard to the assignment, it was useful in the sense that it opened my mind to the problems that could occur when a preferred item's price goes through the roof in the last stages and the less preferred item is already sold out so that you can't even switch. As far as auctions are concerned I realized how tormenting it must be not being able to remove bids and ending up winning something that you would have been better off losing!

- I liked the exercise in the lab today, but, to be honest, I prefer to see my agent doing this job!  

- Now I understand why bidding high in the Mth price auction right from the start is not a good strategy, this makes other people submit even higher bids and the prices go up very fast.

6. The Server Farm Game

Using the facilities of e-Game one can design and develop exercises that do not concentrate on negotiation protocols themselves, but these are used as the means to provide a competitive environment in which another aspect of artificial intelligence is the focus. One of the central aspects of a course in artificial intelligence is search. To extend e-Game’s applicability and use to other courses, we decided to develop an exercise that would focus on search rather than auctions.

The idea of having computer or server “farms”, that is, a large number of powerful computers or clusters of servers capable of executing computationally demanding jobs, is not new. Grids are clusters of interconnected computers that can collectively tackle large computational problems or provide quicker access to very large bodies of data. It is highly likely that services based on computer farms will be provided in the near future by big companies like IBM and Hewlett-Packard. Individuals and organizations would then be able to get slots of computation time to execute their jobs.

This is the scenario explored in the Server Farm Game (SFG). Each of the six participants in the game is a middle agent acting on behalf of 5 clients and each of these clients has a job or task that requires intensive computation. A job has a duration expressed as whole hours, and the client may like to have the results back by a certain time in the day.

The service provider owns a server farm and offers computation slots, where slots are essentially hours of computation for a particular day. For simplicity, we assume that the jobs can be scheduled from 8:00 until 16:00. Each hourly slot can accommodate a number of jobs in parallel as there are a large number of machines. The number of jobs that can run in parallel during a slot is the same for all slots and is set to 20.

Figure 6. Client preferences in the SFG game.

The service provider acts as an auctioneer running 8 auctions in parallel one for each slot and offers hours in each slot at a price which is determined by a random walk in the interval [40-180]. Price quotes are issued every 30 seconds. The price can go up as well as down. An auction closes when there are no more hours available for a particular slot.

The agent’s objective is to construct packages of hours so that the client requests can be satisfied. In the beginning of the game the agent needs to decide the hours that it needs to buy that will allow it to schedule all its clients’ jobs with the minimum penalty. To this end, it has to take into account the deadline by which the client has requested the results. Although this is not a hard deadline, nevertheless the agent is better off by adhering to it, as it will obtain the full utility. As conditions in the game change, i.e. slots are sold out, the agent has to consider alternative packages for its clients. The utility for a client i whose job can be satisfied is:

CU_i=1000 – (100*(ActualEndTime – Deadline))

For every hour that the computation results are delayed the agent looses 100 monetary units, while if the client cannot be satisfied its utility is 0. The agent’s utility is the sum of all the client utilities:

AU=sum (CU_i) – Expenses – Penalties

For every extra slot purchased surplus to requirements the agent has to pay a cost which is considered to be a negative interest rate. At the end of the game an agent needs to decide which clients can be satisfied and report back the results of the allocation to the service provider (e-Game) which calculates the scores. This can be seen as sending the schedule of the jobs to be performed.

7. Conclusions

Games have a number of characteristics that makes them particularly attractive from an instructional and pedagogic point of view. They are engaging, challenging and fun and seem to provide a key factor of the learning process: motivation. They also provide instant feedback, which makes them appealing to students as they can observe the consequences of their actions immediately. The idea behind the approach presented here was to introduce principles and techniques from games and in particular simulation games in a learning environment in order to motivate students, enhance their experience and allow them to put into practice principles taught. The feedback from running the exercise with real players has shown that this is indeed a beneficial experience for the students.

Acknowledgements

The authors would like to thank the HEA-ICS for their support through a Development Fund for the design and implementation of the Server Farm Game.

Notes

¹e-Game and the games described here as well as others are accessible at http://csres43:8080/egame/index.jsp. Colleagues that are interested in using e-Game as part of their courses can contact Maria Fasli at mfasli@essex.ac.uk.  

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Author details

Michael Michalakopoulos (mmichag@essex.ac.uk) is a Ph.D. student at the Department of Computer Science of the University of Essex. His research interests include trust issues in electronic markets and software agents. He is currently a Research Assistant at the University of Essex working on an e-learning project with Maria Fasli.