Tag Archives: games

So close . . . and yet so far.

One of my science students, PK, recommended a game to me called Doodle God, from JoyBits Ltd.  My student said, “since we are studying chemistry, which is about combining things, check out this game, where you try to combine things.”

I was excited to give it a try and have now been playing it off-and-on for about a month.

Here’s what I like

  • Easy to learn to play.
  • Very rewarding to see two things swirl and create something new.
  • Some combinations are very creative, not immediately obvious, but logical.

Here’s what I don’t like

  • From the start, the portrayal of a monotheistic, creative deity as a grandfatherly, tinkering, bumbling, bug-eyed, wild-haired, white-bearded, tongue-protruding simpleton, seems borderline blasphemous for at least three major religious traditions of the world.  But, hey, whatever sells…
  • When I let my 8-year-old play—ignoring the 13+ rating of the game—I should not have been too surprised when certain risqué themes emerged.  Alcohol, drugs, sex, [censored], rock-n-roll are all there, and were they all necessary?  But, hey, whatever sells…
  • From the beginning I was a little peeved when I tried to combine certain things—that made logical sense to me—but they didn’t combine.  Similarly, when I saw hints which led to things that did wondrously(?) and improbably combine, I almost put the game down.  (And how was I supposed to guess that?)

Seeing that the dislikes for me seem to outweigh the likes, why am I writing this blog post?  I feel this game is *so close* to being something of real and amazing educational value.  Imagine something like “Chemistry Zeus” [any similarities between deities living or dead is purely coincidental], where students start with a few elements and either bombard them to make new elements (nuclear physics) or combine them with other elements or molecules to make compounds, or whole families of substances.  I think I would play that game, and if it taught a little science or history of science along the way, cool!

JoyBits, if you need a scientific consultant, you can contact me.  Smile

Some math fooling around

You start with 4 elements and are asked to deduce pairings that create successively more complex elements.  Since the deduction part is flawed in my opinion, I believe most successful game-players resort to trial and error.  Let me explain.

If I give you N items and tell you some of them might pair, by trial and error you would take the 1st item and try to pair it with the 2nd, 3rd, 4th, etc. up to N.  When you are done with the 1st item you then take up the 2nd item, and try some pairings, but you don’t have to test it with the 1st item, since you already did that, so you test 2nd+3rd, and 2nd+4th, and 2nd+5th, etc.  one way to visualize this is with a grid.

  1 2 3
1 1+1 1+2 1+3
2 X 2+2 2+3
3 X X 3+3
Doodle God game with 3 elements (1-3)
Table shows all the combinations you need to check.
You do not need to check combinations marked “X”

The square with 1+1 means you are taking the 1st element in the game and trying to pair it with itself.  The 1+2 means that you are trying to pair the 1st element with the 2nd element.  Notice that the square that would be 2+1 in this example is marked with “X”.  That means you don’t need to test that combination because in the game 1+2 is the same as 2+1, the order you click on elements to pair them in the game doesn’t matter.  (If someday it did, the following analysis would be invalid.)

The formula for how many pairings you have to check for N total elements is

Total Pairs You Need to Check = N2-(1/2)(N-1)(N)
(thanks to Wolfram Alpha for helping me evaluate a sum)

We can verify that this formula is correct, by checking for N=3, plugging that into the formula and then counting in the table above to see if the results agree.  For N=3, from the table I would need to check 6 pairings to exhaust all possible combinations in the game.  The formula predicts

Total Pairs You Need to Check = N2-(1/2)(N-1)(N)

Total Pairs You Need to Check = 3*3-(1/2)(3-1)(3)=9-(1/2)(2)(3)=9-(1/2)(6)=9-3=6.

Now, the object of the game is to find successful pairings so let’s say 1+1 is successful.  But that would produce a 4th element.  That means we have to check more potential pairs.  (Note that some pairings produce two elements, that happens pretty rarely so the analysis so far and following is not invalidated.)

If successful, then you create a 4th element, and the table would now look like this:

  1 2 3 4
1 1+1      
2 N      
3 N N    
4 N N N  
Doodle God game with 3 elements
But the 1st element paired with 1st element produced a new 4th element.
The table of combinations thus adds a row and a column
 
Notice that although the 1st element paired with the 1st element made a 4th element, you haven’t tested any combinations of that 4th element yet.  You will have to test those, so we add a column to the table.
 
The number of total combinations we needed to check when we only had 3 elements was 6.  We tried one pairing of elements and we were successful so now we only need to check 5 plus the new pairings we potentially created.  It turns out that when you add 1 new element to an N-element game, you add N+1 more pairs to check.  In this case, i.e. N=3, we get 6-1+4=9.
 
Can we write a formula for how many pairs we still need to check on the 12th turn of the game?  Sure!  First let’s define a few things.
 
N = total number of elements in the game that you start with.
t = the turn of the game that you are on, in other words how many pairs you have tried already
s = successful matches already
u = unsuccessful matches already
Note that one relationship we can spot right away is
t = s + u
Which just says that the number of unsuccessful + successful matches you have made is equal to the number of turns you have been playing.  But the relationship we are after is “How many more matches do I need to test after t turns in the game?”  I believe this works, let’s try it out.
Potential Matches Left = (N+s)2-(1/2)(N+s-1)(N+s)-t
In the example above, N=3, t=1, s=1, u=0, the number of matches left to test, i.e. the number of blank squares in that grid is:
(3+1)2-(1/2)(3+1-1)(3+1)-1=16-(1/2)(3)(4)-1=16-(1/2)(12)-1=16-6-1=9.
Notice that the function goes like a quadratic in the total number of elements, which means the game gets progressively harder as it goes along.  Even if you don’t blindly try all combinations, you still have to remember which combinations you have made and the combinations you haven’t or review all those elements you have not yet combined for “reasonable suspicion” of being able to combine to form new element.  We say the order of that comparison is O(N2), O() means “order of”.
 
The tradeoff that becomes important in the game is that if every turn in the game produces a new element (s=t), then the number of new combinations increases quadratically.  But that is the reward of the game, producing a new element.  The frequency of reward needs to be traded-off with the rate of increase in complexity of the game.  You can make the game less complex (s << t) by not letting any elements combine, but then who would play it?
 

Back to the game Doodle Farm (Free)

 
Meanwhile…a game that would allow players to combine things in ways that are accurate given physical laws, e.g. chemistry, would be an amazing pedagogical tool.  None of the flavors of Doodle God to date seem to represent any even remotely accurate view of the physical world.
 
I played Doodle Farm (Free) and used a Google Sheet to keep track of my pairings, much like the table above.  I was able to solve the game fairly systematically that way.  But what was annoying (and a deal-breaker for me, sadly) is that two of the first 4 pairings were completely illogical.  Not that the game makes any pretense of teaching accurate animal husbandry, but the whole point of this post is that the game would be used by Teachers if it were more accurate.
 
image
Doodle Farm Free initial elements and successful pairings.
How does Mouse+Mouse=Rat+Cat?
How does Worm+Mouse=Ant?
 

National Research Council. (2011). Learning Science Through Computer Games and Simulations.

I paste here verbatim some conclusions from chapter 2 of the above report.  No silver bullet for STEM teachers here.  I assume that the video games or simulations studied here were not of the highly interactive (physically) type since Kinect® / Wii® technology is relatively recent.  Either way a conclusion of “we need more data” seems universally safe.  Also see the executive summary (NRC, 2011). 

CONCLUSIONS


Science learning is a complex process involving multiple learning goals. A simulation or game can be designed to advance one or more science learning goals.

Conclusion: Simulations and games have potential to advance multiple science learning goals, including motivation to learn science, conceptual understanding, science process skills, understanding of the nature of science, scientific discourse and argumentation, and identification with science and science learning.

There is promising evidence that simulations enhance conceptual understanding, but effectiveness in conveying science concepts requires good design, testing, and proper scaffolding of the learning experience itself.

Conclusion: Most studies of simulations have focused on conceptual under-­standing, providing promising evidence that simulations can advance this science learning goal. There is moderate evidence that simulations motivate students’ interest in science and science learning.

Less evidence is available about whether simulations support development of science process skills and other science learning goals. The emerging body of evidence about the effectiveness of games in supporting science learning is much smaller and weaker than the body of
evidence about the effectiveness of simulations. Research on a few examples suggests that games can motivate interest in science and enhance conceptual understanding, but overall it is inconclusive.

Conclusion: Evidence for the effectiveness of games for supporting science learning is emerging, but is currently inconclusive. To date, the research base is very limited.

The available research suggests that differences among individual learners influence how they respond to, and learn from, simulations and games. Some studies of simulations have found that students with lower prior knowledge experienced greater gains in targeted learning goals than students with more prior knowledge related to these goals. Differences across gender and race in young people’s use of commercial games could potentially influence their motivation to use games for science learning;  however, a few studies of games have demonstrated gains in science learning across students of different genders, races, English language ability, and socioeconomic status.

Conclusion: Emerging evidence indicates that different individuals and
groups of learners respond differently to features of games and simulations.

Although the research evidence related to science learning through interaction with simulations is stronger and deeper than that related to games, the overall research base is thin. Development of simulations and games has outpaced research and development of assessment of their learning outcomes, limiting the amount of evidence related to other learning goals beyond conceptual understanding.

Conclusion: The many gaps and weaknesses in the body of research on the use of simulations and games for science learning make it difficult to build a coherent base of evidence that could demonstrate their effectiveness and inform future improvements. The field needs a process that will allow research evidence to accumulate across the variety of simulations and games and in the face of the constant innovation that characterizes them. (pp. 54-55)

 

So I guess more work needs to be done, according to these folks.

I noted that a strong proponent of appropriate games, James Paul Gee once of the University of Wisconsin-Madison, now at Arizona State University, was not on the panel.

References

National Research Council. [NRC] (2011). Learning Science Through Computer Games and Simulations. Committee on Science Learning: Computer Games, Simulations, and Education, Margaret A. Honey and Margaret L. Hilton, Eds. Board on Science Education, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.  Retrieved August 25, 2011 from http://www.nap.edu/catalog.php?record_id=13078

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