Cool Teacher Podcast

I slipped today’s image into the banner of my final assignment for 502; it’s my homage to my EdTech 501 instructor’s Cool Teacher Podcast.  If you are an educator and you have the time, you should consider giving it a listen.  They are the Kim Komandos of educators.

I finally collapsed with exhaustion as I turned in my final assignments.  It’s not that they were particularly hard; it’s just that I think too hard into them.  I always feel like there’s more to be done, right up to the deadline.

I’ve decided to attach a copy of my EdTech 501 final paper here.  The purpose of the assignment was to research and write a short paper on what I learned about the importance of technology in education.  It was pretty open ended, so I decided to write about the importance of games in education.  Enjoy!

Why Games Matter: The Value of Games in Education


Educational games and simulations have been described in science fiction for decades. From the Kobayashi Maru’s “no-win scenario” for the purpose of testing character (Ecklar, 1989), to a child prodigy named Andrew Wiggins guiding actual troops in what he believed to be a mere simulation (Scott, 1994), games have proven to be plot devices in many science fiction scenarios.

Like many technologies that found their roots in science fiction (Stewart, 2010), games are beginning to find their place in modern society, both as an art form and as an educational tool. The tabletop and video gaming industries have boomed in the last twenty years, and the current proliferation of games could lead to an increased use of games and simulations in education

In Salen & Zimmerman’s Rules of Play: Game Design Fundamentals, a game is defined as “a system in which players engage in artificial conflict, defined by rules, that results in quantifiable outcome (Salen & Zimmerman, 2003).” If games can be designed to provide a desirable quantifiable outcome, and knowledge can be established as a necessary resource to achieve that outcome, then games have the potential to be very useful teaching tools.

The purpose of this paper is three-fold: To describe the biological effects of games, and how educators can employ those effects to create an addiction to learning; to present statistical evidence of the benefits of games and simulations in the classroom; and to describe a test that used quality assurance testing techniques common to the game industry to guide traditional course development.

The Biology of Gaming

One of the goals of games in education is to get learners to learn without realizing it. While little research has been done on how games get players to learn without realizing it, the danger of drug addiction has lead to extensive research on the reward circuitry of the brain (Johnson, 2006).

In his 2006 book “Everything Bad is Good for You,” Steven Johnson suggests dopamine release could be the reason why people seek out the rewards of games. In a later article, He describes several studies that demonstrate dopamine release during game play (Johnson, 2007). One such study of dopamine during game play showed that dopamine release positively corresponded with performance during the game (Koepp, et al, 1998).

This explains why games are often described as “addictive.” Given that knowledge is often the resource required to participate in the game, an addiction to games could potentially translate to an addiction for new knowledge. With that in mind, video games addiction could be a useful mechanism for delivering new information to learners in a meaningful way, provided the information is embedded into an enjoyable and well-designed game. In other words, a properly nurtured gaming experience can create an addiction to learning.

Addiction to Learning

Goal-oriented behaviors are usually learned (Arias-Carrión & Pöppel, 2007) over a period of time, and the reward prediction error — that is, the difference between our expected rewards and the actual rewards we receive — triggers changes in dopamine levels in the brain. Research has shown that dopamine, commonly associated with the reward mechanism in the brain, conditions us to seek out the conditions that have rewarded us in the past.

In a sense, games allow the player to “have the cake and eat it, too.” Because the consequences of failure are generally low in games, players are encouraged to explore to the edge of their imagination to come up with a solution to a given problem. Failure simply tells the player that yet another solution does not work, and that the player is free to explore other possibilities. Success is rewarded when the task is completed.

Johnson suggests that, given most games are filled with clearly defined rewards, it makes sense that games would trigger the same biological motivations as addicting drugs. In his estimation, the keys to good design are (1) informing the players of potential rewards to build anticipation, and (2) quantifying how much those rewards are “needed.” As long as the rewards are clearly defined, players are willing to spend 90% of their time frustrated to get to the 10% of time where they are rewarded (Johnson, 2006).

Surfing the Learning Curve

In a piece entitled Changing Minds: Computers, Learning, and Literacy, Andrea DiSessa coins the term regime of competence to describe the domain of knowledge that a person possesses (DiSessa, 2000). According to James Gee, good games remain at the frontier of the player’s regime of competence (Gee, 2005). By staying at the edge of this frontier, games are able to stimulate dopamine production by providing players a path to seek what they want, even if they don’t immediately provide exactly what they want.

DiSessa’s idea that maximized learning takes place at the edge of the regime of competence seems to be reinforced by an experiment conducted by Richard Haier in the early 1990s (Johnson, 2007). Haier conducted PET scans to assess brain activity on test subjects as they played the puzzle-game Tetris. This established a baseline. After a month of play, the subjects returned. Haier’s experiment showed that even though the test subjects’ performance improved dramatically, their brain activity had actually decreased. By this point they understood the game well enough to play on a more passive basis. This seemed to show that the regime of competence is an ever-expanding domain; in order to increase brain activity further, the learners need an ever-increasing struggle.

Success embeds the new knowledge into the brain, and the search for new knowledge is no longer required to achieve the same success. In order to further satisfy the game addiction, the learner must seek out a more difficult or different type of challenge. I call this progressive incremental challenge. Single player games are typically designed to provide this by steadily increasing the difficulty and complexity; multi-player games typically provide this through ever-changing opponents.

Games matter to educators because they can motivate and challenge learners on a fundamental, biological level. By stimulating and satisfying the player’s addiction, monitoring performance, and keeping players as close to the edge of their abilities as possible, games can provide individualized experiences that maximize the learner’s potential.

By the Numbers – the Statistics of Educational Gaming

The studies described in the last section have shown the effects that could happen as a result of games in education, but does that possibility represent reality? Based two meta-analyses of games in the classroom, it appears so.

Games in the Classroom

In the early 1990s, a meta-analysis was performed on 68 studies related to the effectiveness of games in education (Randel, Morris, Wetzel, & Whitehill, 1992). Of the cases studied, 32% favored the effects of simulations/games in education, while only 5% favored conventional instruction. The remainder either found no differences or had questionable controls.

The meta-analysis also revealed over 85% of the studies involving math favored games over traditional instruction. Further, in most cases, the simulations and games showed both a greater interest and greater retention came from simulation and games than through traditional instruction. This tends to back up the biological analysis provided by Steven Johnson.

Another meta-analysis was conducted in 2005, this time examining 42 papers related to the use of games in educational contexts (Tobias & Fletcher, 2006). The studies showed very positive results in the areas of transferability to real-life tasks, introduction of new variables, team characteristics, and motivation. The studies also suggested a general improvement of cognitive ability as a result of games and simulations.

Games are being used in higher education and in vocational training every day. Business schools regularly use the MarkStrat simulator to teach projections or the Littlefield Simulator to teach bottleneck calculations. Civilian power plant operators regularly spend time in a simulator receiving refresher training and to explore unusual plant transients. The US Navy uses a control room simulator, called an IDE, to train reactor operators on casualty procedures (Daniels, 2008). In fact, the military is using a broad spectrum of games and simulations to train personnel for the realities of their jobs (Macedonia, 2002).

Metrics Driven Course Design – the Quality of Gaming

In addition to the educational value of the games themselves, the quality assurance and continuous improvement practices of the gaming industry can be applied to lesson development. The increase in game quantity over the years has been accompanied by a general increase in quality. This is due, in large part, to the rigorous vetting that most games endure before they are released.

In this section, I wish to show how educators can polish their courses using techniques commonly applied to polish games.

A Flash Love Letter

In his 2009 article advocating Flash game development, Daniel Cook describes a process for gathering feedback from his players during their game session (Cook, 2009). Through timed questions and trending of feedback, he was able to identify exact moments when players were received increased or decreased levels of enjoyment, and modify his games accordingly.

Here are some examples of possible trends, and the actions that can be taken to improve on them. In each of these examples, players would be sampled at random 2 minute intervals to determine their level of enjoyment.

If the feedback trends formed the first curve, then it would be clear that players’ enjoyment was dropping off after about 10 minutes. Based on this data, the designer would know that it is time to introduce a change to keep the player interested. This is the ideal time to introduce a progressive incremental challenge.

On the other hand, if the feedback shows this second curve, then it is clear that players initially didn’t care for the game, but those that kept playing grew to like it over time. This might be an indicator that the introduction is too long, or the control scheme may be difficult to learn in the first 8 minutes.

The third curve presents a more interesting situation. In this instance, something annoyed the players at around the 8-10 minute point. This could be an unskippable cut scene, or a “cheap” enemy. In any case, this allows the designer to identify precisely when the problem occurs. This is the first step in finding a solution.

Metrics Driven Course Development

Using Cook’s method, I conducted an experiment in one of my own courses.

At the time of the 2009 article, I was in the process of developing a Fundamentals of Electricity course. The course was taught in 40 hours over of 5 days. Like most learning institutions, we used a standardized end-of-course evaluation form for feedback.

Instead of simply relying on the end-of-course form, I developed a daily evaluation sheet that allowed me to receive feedback at more regular intervals. A copy of this sheet was submitted with this report. The survey asked students to score a variety of statements about the course. By taking the average of their scores, I was able to trend their perception of the class over the course of the five days and pinpoint where changes could be made.

Workflow for Metrics Driven Course Development

Survey Data Analysis Trending

Here is an example. Based on the survey of the first test class, I plotted a curve for the statement, “Today’s lesson did not feel rushed.”

The feedback average was 3.0 or above on Monday, Tuesday, and Thursday, but dipped to 2.5 on Wednesday. Further feedback showed that this was based on the start contrast in course content on that day, coupled with the pacing of the course and the reduction in time to practice new problems. While the end-of-course feedback form provided useful general information, it would not have told me about this slump.
Based on this data, I adjusted the transition into the Day-3 material, changed the order in which the course material was being delivered, and spread out some of the more difficult concepts. As a result of these changes, I saw the following feedback trend for the next class. To show the comparison, I have overlaid the new trend on the original.

Several things improved here. The overall scoring was higher, the magnitude of the slump was lower, and the slump that did occur came earlier in the week, which allowed more time for review.

Note that, while I did care about the absolute score to some degree, I placed more weight on the trends. One student’s 2 might be another student’s 3, but most students keep the same scoring standard throughout the week.

I’ve used this same feedback form for several classes since then, and have noticed general improvement and a flattening of most of the curves. This method, which was transplanted from a game designer’s QA technique, has proven to be a valuable resource to my own educational development process.


Recall Salen & Zimmerman’s definition of games:

A system in which players engage in artificial conflict, defined by rules, that results in quantifiable outcome.

In a sense, every traditional test that a student has ever taken in the classroom has met this definition. The test is a conflict with rules and constraints, and the outcome is quantifiable. The student’s resource is the knowledge. Students that assign value to the outcome will be motivated to attain the resources (learn the material) necessary to win the conflict (pass the test). Those who don’t will often perform poorly.

Tabletop and video games introduce affective and psychomotor elements to a student’s learning process by creating an emotional connection to the learning, encouraging the learners to assign value to the steps that lead to reward, and requiring the learner to perform hands-on tasks during the learning process.

Games can be a vehicle for cultivating an addiction to learning, and they have demonstrated their ability to improve the effectiveness of the learning environment. In addition to the games themselves, my own experiment has demonstrated that the QA testing methods commonly used in game development can be useful in course design.

And that is why games matter.


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Ecklar, J. (1989). The kobayashi maru. New York, NY: Simon and Schuster.

Scott, O. (1994). Ender’s game. New York, NY: Tor Science Fiction.

Salen, K, & Zimmerman, E. (2004). Rules of play: game design fundamentals. Massachusetts: The MIT Press.

Arias-Carrión, Ó., & Pöppel, E. (2007). Dopamine, learning, and reward seeking behavior. FESTO-Program for Applied Knowing. Ludwig Maximilian University: Munich, Germany.

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Aldrich, C. (2005). Learning by doing: A comprehensive guide to simulations, computer games, and pedagogy in e-learning and other educational experiences. San Francisco, CA: John Wiley and Sons.