How is Computational Science different/similar from what you learned in school?

I remember learning about science through a textbook and limited hands-on activities, probably due to resources and the prep it takes to put everything together for labs!

Considering computer science, a similarity is knowing the key terms and jargon related to the topic in order to communicate effectively. The whole concept of experimentation and collecting data isn't changed either. 

Something that is different is the possibilities afforded by the power of computers. By doing models, its a lot easier to watch simulations in real time and collect the corresponding data. Simulations are nice because you can also speed them up. Once you have observed the phenomena closely, you can just ramp up the speed to collect more data. 

Lastly, any simulation that is dangerous or materials-intensive would be impossible in class. I'm thinking about a water molecule simulation that shows how molecules move when heat is added to or removed from the system. This is also something that is just too small to see anyways. 

How is Computational Science different/similar from what you learned in school?

Computational Science is very different from what I learned in school (in science) because we did a lot of book work and watched videos from Kindergarten up to my 9th grade biology class. There were a few more labs in Chemistry. Computational science gives you the opportunity to explore things that we see in everyday life and allow us to possibly get answers to questions we may have from the observations we make. The fact that something may be to complex, expensive and/or dangerous doesn't matter because the simulation is ran on the computer. 

I loved the example given about how an emergency evacuation can be planned through the program because our first unit in 7th grade science is about natural disasters and evacuation planning goes hand in hand. 

In learning Math and Science when I was in school we rarely computational skills into the practice of creating a real world model.  When we did interact with real world problems it was so simplified that it little relationship to understanding the complexity of a real issue.

When I was writing educational software in the late 70's I had a simulation of solar energy and the path of the sun across the sky for different locations and seasons.  Using a computer model I was able to have the tilt angle of the earth as a variable.  In this way students could change the tilt angle of the earth to see the impact of this change on solar energy input and the path of the sun.  No such experiment could be done directly with our planet.  

Students could also add depth to their study of environmental topics by interacting with a more complex computer simulation.  Early programs using a simplified Limits to Growth model were a good start in this direction.  Interacting with the app  Earth Primer is a more recent example on how computational models can be used by students in the classroom to deepen their knowledge in a more complex manner and using systems thinking to understand the interaction of many different parts of the system.

In terms of formal education, I can really only contrast between my experience with K-12 science and CS as computational thinking was scarce and CS was definitely not something I was exposed to during primary and secondary education. I only recall rote memorization, with the addition of cookbook labs in HS. We did not use mathematical, computer, or molecular models, and we surely did not consider adaptive processes in between an immediate cause and effect. @jchandler51, I concur with respect to oversimplification. My fifth grade realization of the gross oversimplification of everything I was exposed to in school has been vividly seared into my memory. CAS and CS were the glorious spoils of university. Even then, that was a product of an amazing ecologist, and a stellar petroleum geologist/hydrogeologist. I feel like science (i.e. not memorizing a body of disconnected facts gained through someone else's scientific explorations) and CS have an intimate relationship as science employs CS as one of the many tools in it's belt. 

CS would allow for a realistic lab on groundwater remediation whereas a traditional science lab would be limited to more of a snapshot plume delineation as it would not be possible to model the effects of pumping, injection, or microbial activity. 

For the most part, science has been about learning facts and regurgitating those facts.  Our district has moved to inquiry model for the most part, but students and parents alike have not moved away from this idea.  We are also so content based that we lose site of what it takes to teach the whole child.  

As I learn about Computational Science, I see the pathway to creating an authentic problem based learning environment in my classroom.  Our school has also dabbled in Design Thinking and I can see how the two intertwine.  Creating a classroom where our students are trying to solve real world problems that are based on the needs of a user is exciting.  I believe using this model will give our students real purpose to learn and to be engaged in their community.

I see using Computational Science to help students understand their real human footprint in our world.  I also see using this model to help students question more and to develop areas in their life that they are interested in learning more about-giving the reins of learning over to the student rather than dictate everything they need to learn.  

I don't remember doing much in the way of experiments when is was in school. I do remember doing experiments with fruit flies involving eye color and heredity. On a small scale, we predicted the eye color of the fruit flies after several generations. A computational model could also predict heredity traits but would create many more generations of fruit flies than would be possible in the lab. In my own class, my students study the effects of problems such as overpopulation of a particular species or loss of habitat on an ecosystem . A computational model would allow them to see the the results of these types of problems over many years. This would be a much more realistic way to learn.

In terms of my own education, I don't remember doing any kind of computational science. The closest thing we did in biology was to look at population sizes and how they changed over time; I do not think this is considered computational science because we looked at data from the past, not predictive data.

Something we could use computational science for now might be individual people's actions and how they relate to global warming. This would take far too long to test in real life, but if we gave each human simple rules to follow, and we know current conditions, we might be able to see how our daily actions affect the planet.

Computational science is similar to the science I learned in school, both have you looking at and interpreting data.  Both have you coming up with a solution.  There is even a experiment with each.  However the complexity is where things differ.  I could only go so far with an experiment.  You can only focus on one variable at a time and figure out it's effect before moving on to another idea.  With computational science you can get more detailed at what you are looking at and observing.  You can look at a topic that might be to dangerous/expensive to otherwise study with.  I keep thinking about having the students look at how a virus spreads using the computational science.  They could have multiple variables from the random mutations of the virus to the health of the host and how that will impact the virus.  

Well, all of this is different because I never had computers in school and remember very little science. What I do remember had a hands on quality of drawing the different species with Kingdoms and that hands-on approach stayed with me over 40 years. I was so struck about all of the multiple applications that this has for all the different type learners in my classroom. As soon as he stuck his hands in the sand I was hooked. I understood as he put the different layers of the different agents within the system and saw how this could be used as a tool to help diverse groups of people with different jobs come together to see their own work as parts of the whole. I remember learning about the doctor in London who helped to cure a cholera epidemic by working on a map and interviewing people until he could see the pattern and determine that it was a contaminated well favored by the people. Having this ability would help in every branch of science that I teach as a middle school teacher and I can see that the level of engagement would increase exponentially. I believe I was taught science as separate eggs in the carton and this is a chance to see how the separate eggs make scramble eggs. 

When I was in school, we approached different concepts with empirical observations, reasonable questions about what we observed, testable hypotheses, experimental design and data collection, and data analyses and interpretation.  These steps (i.e. scientific method) is similar to how tasks are accomplished in computational science - identify a problem, simplify the problem so it can be translated into a computational model, etc. The major difference in my mind is how data is generated and analyzed. For example, in the science classroom of my youth,  one could only identify and understand the importance of a keystone species by actually removing the species from an ecosystem. Since ecosystem equilibrium is extremely delicate, and destabilization can have disastrous impacts, keystone species could only be studied "by accident" (e.g. wildfire destroys nesting sites, pollutants introduced, etc.).

However, with computational science, one could simulate the loss of a species to analyze the possible effects its removal would have on the ecosystem as a whole.  This would lead to more useful data, without having to have a real catastrophic event occur.

I remember a lot of book work and learning through reading and looking at pictures.  Once we moved to hs/college there were more labs and hands on. 

Computational experiments would come in handy when looking at infectious disease spreads.  Those types of experiments and modeling is hard to do in a classroom.  You can look at evidence or past epidemics and read but actually watching an infection move through a large population isn't possible, but with computational science we can simulation the spread and even have the students work their way back from the end to the beginning to trace the original source of the outbreak.

In my mind, my science journey is marked by many textbooks and multiple choice tests. I learned from hearing lectures, taking detailed notes, and studying mostly from the textbook and printed out PowerPoint notes. There was always a lab component, which was mostly very hands-on. The lab component of my science learning is the closest thing that can be compared to Computational Science.

Both of these sciences set out to produce evidence of relationships using an experimental design model such as the scientific method. By creating a lab experience and a Computational Science model, we are trying to imitate a phenomenon in the real world. The difference would be in that a lab experience, the connection is a little harder to make - whereas in Computational Science, the model is a direct reflection of the system that is being studied. In this system, it is easy and quick to change a variable, which is quite the opposite of lab experimentation. Human error takes a hand in this, and in order to repeat, the individual must be very careful and precise. 

One final, very important observation is that in a lab setting, the parameters are very limited. You may not be able to test your hypothesis due to the extent of the experiment - maybe it is too big and too expensive. With Computational Science, the possibilities are almost endless; time is not a factor and available/non-available resources are irrelevant. 

Honestly, I don't remember much of what I did in science when I was in school. I know it wasn't as technologically involved as what I've seen so far. The most technology we used was a calculator or a microscope. 

One example I could conduct with my class is create a model of the importance of a balanced ecosystem. I imagine you can program enough creatures to function in a certain manner to display the balance of a variety of organisms and the consequences of an imbalanced one. 

Computational science is very different what I learned in school.  My classes were text-booked based with lots of lecture notes that I doodled on to retain content.  Classes were centered around remembering information.  I didn't do experiments until I took AP classes in HS.  In contrast, computational science is creating models for solving real world problems.  I like how the focus is on how parts and systems are interconnected and related to one another.  

One example I could conduct in my class is to have students create a model of how people's actions affect recycling in NYC. Students could then create an artwork using recycled materials to illustrate their model or an artwork inspired by the process of creating their model or a peer's model.  

Milkweed Bug population - growth/death rate.

Reduced cost to perform this experiment.  Able to be done during distance learning.  Can be done numerous times in a shorter time period.