Serious Games Essay

Order Description

choose one game to analyse

do not copy and paste, last time I have a similarity of 21% when I turn it in.
It is my last assessment and worth 40%
the language can be simple and easy to understand because i am a english as a sencond laguage speaker,
I provided someinformation from the lecture you can have a look and can be used as resources, please read it through.
Grade Percentage: 40%
Choose one game between Unsavory and Parable of the Polygons, I prefer Unsavory, it is easier.
Games to Analyse:
Unsavory    2015    Ever wonder what it’s like to have a sickness you’re forced to work through? Unsavory let’s you experience the world of restaurant work without paid sick days.    http://playunsavory.com/
iphone/Android app
Parable of the Polygons    2014    A playable post on how harmless choices can make a harmful world. Half-videogame, half-blog-post, it’s formatted like an article, but contains dozens of games that let you learn by doing.    http://ncase.me/polygons/
web
Task Description:
Using the concepts from the course lectures analyse ONE of the serious games listed above in a 2000 word essay (+/- 10%).
In order to do this you will need to play the game yourself.  Play it first without accessing any additional tutorial or help features other than what occurs in normal gameplay. Then play it again after reading all that is on offer.
Think about:
•    How easy was it to learn to play the game?
•    How did the game “teach” you how to use it?
•    Did you draw on any of your existing knowledge of interactive media the first time you played?
•    What game mechanics and dynamics have the game designers designed into this game?
•    What kind of player experience does it evoke?
•    Identify the ‘serious’ purpose of the game and critically assess whether it has been successful at achieving this, particularly in light of the audience that it is aimed at.
•    Consider also how successful the game is as a game. Would you play it more than once? Does it matter if you wouldn’t? Was it engaging?
•    Does the game reveal any specific things to do (or not to do) in terms of designing a serious game? What works and what doesn’t in this game?
Now write an essay using your analysis of your chosen game to argue your position on the question:
How do you achieve the balance between ‘serious’ and ‘game’ in a serious game?
You may reference other games from the list as points of comparison. Due to the relatively short word length you can assume that the reader already understands LeBlanc’s model of Mechanics, Dynamics, Aesthetics. You can also use tables and/or bullet points to list game mechanics and use screen grabs to illustrate elements of the visual design.
Task Objectives:
This task will encourage you to think critically about the form and content of serious games. You will also develop a deeper and applied understanding of the theories covered in class.
Task Criteria:
•    Your ability to critically analyze and interpret the game.
•    Evidence of engagement with ideas and practices addressed in the course.
•    Evidence of critical thinking beyond mere description.
•    Evidence of personal engagement i.e. clarification of the aspects of the game you found useful, or intriguing, or frustrating, or engaging etc and why;
•    Generic aspects of scholarly writing i.e. writing style, citation practice, structure.

Resources

Learning and Games – Strange bedfellows?
“Anyone who thinks education and entertainment are different doesn’t know much about either”
(Marshall McLuhan quoted in Schell 2008:443).
Introduction
Those of us who approach serious game design from a game perspective rather than an educational perspective are often dismissive of games that can be described by the terms “chocolate coated broccoli” or “stealth learning”.  These titles, we would argue, see games as  “frothy entertainment that sugar coats the learning to make it invisible or at least more palatable” (Lieberman 2009: 121).  Such titles will often use games as rewards for watching/reading/or listening to educational content or will use game-like dynamics to test students and encourage them to memorise information. This perspective creates games that are often not games at all and that are certainly not as fun or motivating as games can be. Games that, you could argue, fail to see the connection between education and entertainment that McLuhan points out above.
There are a lot of educational titles out there that do take the stealth learning approach and sometimes they do result in good learning experiences. Those in the game design community, however, would argue that seeing games merely as sugar-coating for learning is a missed opportunity that promotes shallow or surface learning. They maintain that the stealth learning perspective both underestimates the power of games and fails to take advantage of the real connections between games and deep learning.
For Jenkins (2007), games are systems that allow you to:
“design activities that are social, authentic and meaningful,
connected to the real world,
open-ended and containing multiple pathways,
intrinsically motivating, and filled with feedback”
This characterization of games points to the connections that can be made between games and the types of deep learning advocated by those who believe that we learn best when
•    our learning environment involves us as active participants,
•    asks us to make our own connections between material,
•    is situated within an authentic context and culture
•    and allows us to participate in a community of passionate practitioners.
Adopting this approach to serious game design and learning will be required if you are to fulfill the brief for your project this semester. This design process will also ask you to change your own perspective from that of a student to teacher.
Deep vs Surface Learning
So what does the term deep learning mean and how can you promote it with your game? The opposite of deep learning is surface learning. Student approaches that result in surface learning are characterized as being driven by strategies that are focused on maximizing grades and by a corresponding “fear of failure” (Atherton 2011).  Deep learning, on the other hand, is described as being driven by an intrinsic interest and passion for the subject. Atherton outlines the key differences between the two in the table below (2011):

As this table shows, surface learning is associated with a taking ‘quick fix’ short-term view of learning.  It is seen as an approach that will progress a student through their courses but leave them with very little practical knowledge at the end of the process. Surface learning is associated with a view of learning as primarily about memorizing facts and acquiring knowledge and skills that may be used in practice at some future date. As Atherton describes, deep learning is associated rather with recognising patterns and connections and with reflecting, interpreting and transforming one’s view of the world. It is learning that is applied and reflected upon through practice and experimentation within real-world contexts. The argument is that the knowledge that a student acquires through these processes will be rich and complex and will prepare them well for the practice of their profession.
Although this description is focused on learning from a student perspective it does suggest ways that you now as teachers might approach your game design. It suggests that in order to design a game that will promote deep learning you will need first to focus on ways to motivate student interest in your topic, second to create a game that asks students torecognize and make sense of patterns and connections relevant to your topic, third to provide room for reflection and interpretation about your topic and fourth to ensure that the dynamic actions of your game reflect the situated practice of your topic.
As an example of this, think about the foraging for water section of Darfur is Dying (http://www.darfurisdying.com/). The act of choosing a character and the goal of not getting caught by the militia motivates you to engage both with the game goals and the game content.

The screen that is shown if you are caught provides a moment for reflection and encourages you to begin seeing patterns in the behavior of the characters. As you forage with more and more of the characters these patterns become clearer and clearer and you gain an affective and experiential knowledge of the plight of these refugees that you would not have gained by simply reading the text on one or even every capture screen.
Thinking about the Process of Learning
Watch the following Lynda.com video which explains two models of the learning process, Bloom’s hierarchy of learning and Kolb’s learning cycle (Note: Here are instructions for logging into Lynda.com). Both can help you think about the process of your game in terms of the stages of learning that it might include.
Watch this Lynda.com video: Bloom’s hierarchy of learning and Kolb’s learning cycle

One of the things that is so useful about thinking about learning as a process that occurs across time is that this process is something that you can try to map onto the way that your game unfolds across time and onto the types of activities that you include within it.
The Deep Learning Properties of Games
“..it’s easier to make a fun game educational than it is to inject fun into an educational game.”
(Peters 2007: online)
So where do you start when designing an educational game? Do you begin with your educational objectives or with your game?
James Gee argues that good games already exhibit all the properties required for deep learning. His argument suggests that good educational game design is not about shoehorning educational content into a game system. It is rather about finding a marriage between your content and the types of learning experiences that game systems can and do provide. His list of sixteen ways that games incorporate learning are a good starting point for thinking about what type of game might suit your client brief this year.
Read the article below on the Deep Learning Properties of Good Digital Games
Gee, J.P (2005) “Good Video Games and Good Learning”, Phi Kappa Phi Forum, 85(2), pp.33-37.
Available: http://jamespaulgee.com/pdfs/Good Games and Good Learning.pdf
Note: Gee’s full 36 learning principles are explained in his book What Video Games Have to Teach Us About Learning and Literacy which is available online in the UNSW library.
Transferring Skills from Games to Life
As we saw in the video on models of the learning process above reflection is an important part of deep learning. Our example game, Darfur is Dying does do an excellent job of providing moments of reflection within the game but many serious games choose to create space for reflection before and/or after game play – often through the use of teachers who guide and encourage reflection. This is because, as Ke points out, many studies have shown that the intense engagement often experienced during game play does not provide much room for reflection (2008:21). Whether your game will need any structured pre-briefing or de-briefing sessions and what those sessions might entail is something else your team will need to consider in your game design.
Now watch this video from the team at Extra Credits ( http://extra-credits.net/ ) where they discuss what can be learned through games and raise questions about how best to ensure that this knowledge is transferred to real world applications.

Are you starting to see some common themes emerging here in both the educational theory and the game design theory? There is a common focus onmotivating/engaging/intriguing the student/player and on paying attention to the development of this motivation throughout the whole learning experience. There is a common focus on allowing the student/player to take action within a structured authentic context and to learn through feedback. There is a common focus on the importance of providing moments of reflection or rest. And there is a suggestion that communities of learners are important for ensuring that deep learning takes place.

Resource 2
How then do we design such games? By no means can I offer a foolproof process, but here are some steps that I have found useful in my own process:
1. Identify the mechanics and dynamics of the real-world system.
2. Simplify the system to be representative rather than realistic.
3. Represent the system to facilitate the visualisation of patterns.
4. Provide the player room to play.
5. Add goals to stage the player’s exposure to the system.
6. Debrief to encourage transfer.

Step 1: Identify the mechanics and dynamics of the real-world system
The crucial first step is to approach the real-world with the mind of a game designer. Consider the topic to be taught as if it were a game already. What are the mechanics of that game? What are the resultant dynamics? Who are the agents in the game and what choices must they make? What resources do the agents use? How are they gained and lost? What are the sources of risk and randomness?
It is important for experiential learning that the mechanics we create are described at a level of abstraction lower than the patterns we want the students to learn. So, for example, if we are designing a game about economics and wish to teach the law of supply and demand, we should aim to create this law as an emergent property of our game, rather than as an extrinsically imposed idea. This could be done by allowing multiple players to produce different products with different levels of efficiency, and to consume them with different preferences (not matching their production). If players are allowed to trade in such a scenario, the law of supply and demand automatically emerges. What’s more, players gain an intuition for how it emerges that is more meaningful than just reading it in a textbook.
A major stumbling block at this stage can be the realisation that we don’t know what the low-level representation of a system might be, or our only description of it may be too vague to be operationalised. This is often the case with social dynamics. Wherever possible, social dynamics should be simulated by actual inter-player interaction, using role-play if necessary. Trying to simulate social dynamics mechanically is very difficult.
The games of Laver’s Playing Politics illustrate this well. They are a collection of games designed to teach the fundamentals of political science. As such, they are largely about social dynamics: trust, deal-making, betrayal. The concrete psychological mechanics that govern these dynamics are very hard to describe and would be impossible to model in a computer game. Laver avoids having to do so by using real opponents and real money. The rules of his games are quite simple; the complexity arises in the interpersonal dealings between the players, which can be quite sophisticated.
Step 2: Simplify the system to be representative rather than realistic
The realistic system derived in step 1 is often too complex to be implemented in all its detail, or to be understood if it were. The goal now is to aim for a representational game rather than a realistic one. “Make things as simple as possible, but not simpler,” as Einstein is supposed to have said. By simplifying irrelevant detail we eliminate distractions and make the system easier to understand, but we must not simplify beyond the point of removing the dynamics that we wish to represent.
One possibility is indexical simulation – reducing a large number of similar factors to a representative set (Dormans 2011). So, for instance, Sim City does not distinguish particular kinds of businesses in a society, it simply classifies land use as residential, commercial or industrial (later versions include high, medium and low densities of each of these plus agriculture).
Another alternative is symbolic simulation (ibid). In this case a different, non-representative mechanic stands in place for a realistic one, usually for the sake of simplicity. So for example battles in Risk are resolved by rolling dice. Randomness is understood to represent the vagaries of war.
Step 3: Represent the system to facilitate the visualisation of patterns
The design of the interface, the way in which the system is depicted, has significant effect on our ability to recognise patterns. Too much emphasis on detail can distract from the bigger picture. For example, consider two possible interfaces for a game based around landing space ships. In each case the mechanics are identical: a ship is descending under gravity towards a landing pad. The player has a limited supply of fuel they can burn to slow descent. The goal is to reach the landing pad (altitude 0) with velocity less than 1m/s.
In one representation the three relevant variables (altitude, velocity and fuel) are shown numerically and the player is asked to enter the quantity of fuel to burn at each second of the simulation. The other representation is graphical. The altitude is shown by the position of the ship, the velocity by its real-time movement and the fuel by a graphical meter. The player burns fuel at a constant rate by pressing a key.
It is clear that the latter interface is much more intuitive than the former, but it is worth pausing to consider why. We are much better at intuitively grasping physical quantities such as size and speed than we are at understanding numbers. This is especially true when it comes to recognising abstract patterns. Our senses are highly attuned to detecting patterns in images and also in sounds. A graphical or physical model of a system is often a good way to engage these intuitions when the real system is more conceptual, such as in the common hydraulic model of electricity which depicts electrical currents in circuits as water flowing through pipes (Esposito 1969). Water flow has also been used to depict the forces of macroeconomics in the Phillips Machine – a real physical hydraulic model of the British economy built in 1949 (Elliott 2008). These are, again, examples of symbolic simulation and carry the same dangers of lack of transfer, but offer a powerful way to aid visualisation of otherwise theoretical concepts.

Narrative is also a useful tool for depicting patterns. Our brains and naturally inclined to construct stories out of temporal sequences of events, and they form a significant part of how we understand the world (Schank 1990). Games can exploit this to make concepts more memorable. This is not to say that an arbitrary extrinsic narrative should be attached to a learning game, but rather that the intrinsic drama of a system should be brought out, if it exists. For example, ecological models of population growth and predatory-prey systems show patterns such as exponential growth followed by sudden collapse, and unstable equilibria. These are narrative patterns, each with its own dramatic arc that makes them memorable.
Step 4: Provide the player room to play.
One of the biggest mistakes of educational games is not allowing the player to play. As Salen and Zimmerman (2004) define it “Play is free movement within a more rigid structure.” In educational games, the learning topic provides the rigid structure and often we only allow the player to follow that structure along a single well defined path. Deviations from the path are considered failure, and there is no scope to play. As such, they are little more than exercises. Engaging play is creative and exploratory. It allows the learner to experiment and make discoveries in their own time without the constant fear of failure.
Step 5: Add goals to stage the player’s exposure to the system
While free play is valuable, it becomes directionless without goals. A large and complex system can present too much information at once. Without the basic ideas with which to break down this information into manageable patterns, the learner can be overwhelmed. Instead, the learner should be exposed to concepts incrementally, allowed to play within a constrained subset of the game in order to acquire and practice new ideas and skills in isolation before using them in a larger context.
The game Portal did a remarkable job of this. The player was lead through a series of “testing chambers” each of which presented a clear goal and a new concept or technique that would be used to achieve the goal. Subtle hints were given to guide the player towards certain solutions by attracting their attention to important objects or by demonstrating an effect before having the player imitate it, but never is the player told outright what they need to do. Each chamber provides multiple opportunities for the player to practice the new skill before proceeding, but practice is kept from being repetitive by creative use of variations. Each learnt skill is also tested as part of a larger complex problem where the application of the skill is not immediately obvious.

Step 6: Debrief to encourage transfer
A problem that is widely known to the simulations community but which seems to have been neglected by the serious games community is the transfer of learning. The experiential learning cycle promotes the construction of abstract concepts, but they are abstract concepts about the game. The psychology of the ?magiccircle’ means that players are not likely to see the applicability of their newfound knowledge outside the game unless prompted. As we said above, the realism of the representation will influence this. It is easy to make the leap from a flight simulator to a real plane; harder, possibly, to see how the simplified mechanics of Go apply to real warfare.
A learning game needs to be part of a greater learning context to relate the concepts learnt in the game to the corresponding concepts in the real world. This is especially true when dealing with emergence in games, as creating emergence is always an exercise in giving up authorial control. The player will do things that the designer did not intend, and creative ways to drive the system into pathological patterns that are artefacts of the limitations of the simulation, rather than useful patterns that can transfer to the real world. Many of these are obviously nonsensical but some can be genuinely misleading. We cannot rely on games to tell our players the whole story. Debriefing and discussion is a vital part of relating in-game concepts to reality.

Conclusion
Call it edutainment, games-based learning, serious games or gamification, a game-driven revolution of education has been “just around the corner” for decades. The reality is less exciting: designing a good game is hard, designing a good educational game is harder. Slapping an arbitrary game-like structure on top of traditional didactic teaching methods has proven unsuccessful. When the ?learning’ and the ?fun’ are in contention the educational parts of the game are often resented as obstacles in the way of the fun.
It does not have to be this way. Exploration, discovery and mastery are all fundamental aspects of what makes games fun. It should be possible to harness these same aspects to engage learners in more serious topics, but to do so we need to abandon didactic teaching and embrace an experiential model of education. Games should be meaningful models of real-world systems, which provide room for the learner to play and discover patterns that emerge intrinsically from the system rather that having them externally imposed. Designed well, such games promise to be more engaging and more effective learning tool.
However this design is not a simple matter. It involves detailed understand of the low-level mechanics of our learning domains and an understanding of how they might be represented with the right measure of simplicity, revealing patterns piece by piece without enforcing them. Even a well designed game is unlikely to stand alone – it should be part of a strategy that includes debriefing and discussion, to help students transfer concepts constructed in the game to the real problem, and to filter out any intended patterns in the game that do not apply.
Dr Malcolm Ryan Lectures in Game Design at Macquarie University.