In water pollution studies students tend to think pollution is something seen. This model would allow students to experiment with harmful chemicals that cannot be seen but end up in the water system.
This would allow students to develop a hypothesis based on observations of their own (vs. already knowing the outcome or just making a wild prediction with no prior knowledge.
I try to do as many experiments in my class as I can in order to give the students hands on activities. However, we only have time to do the experiment one time and have to use that as our basis to analyze the data. In some experiements, they do run multiple trials, but not many due to time constraints and they do not have time to manipulate a variable and explore the outcome of it. Computational models will allow them to run scenarios in a faster time and collect more data. I can easily see having a group of students each running a trial and collecting their data on a shared document. This would allow them to share their data and then discuss their findings which I believe would allow them to go deeper into the cause and effect of the variable manipulated. Being able to do computational models would also allow for students to do work at home or in another setting instead of just in the science classroom. This will open up many possibilities for student learning.
No argument that simulations have some dramatic advantages over an actual lab experiment, but I feel that there is value to the actual âhands onâ approach and manipulation of materials. Kids who understand science skills and processes and concepts should be able to use and do both finding value - albeit different value - in both âmediums.â
Both would be following the scientific method. The computational models could allow for many more trials and would result in more accurate data. It would also probably minimize experimental error.
With labs my students try to investigate a problem with controlled experiments just like using an experiment with a computer model. The problems relate to real life; however, it is often difficult to replicate a real life scenario. Experiments offering computational models can eradicate this problem while allowing students to repeat experiments or to change a different variable to analyze the outcome.
I agree. Some of the materials needed for a âhands-onâ experience are costly and it becomes difficult for a science department to purchase something if it is only going to be used by 1 grade level for 1 specific class.
Computational experiments allow for repetitive experiments with small âtweaksâ in the factors.
It is similar because students generate an idea using the scientific method and test that idea using controlled variables. Computational modeling will open the door for students in my class to test more complex ideas and generate evidence based on their tested variables.
The computational models allow for repetition. In the classroom time is very precious and so repetition is difficult. Allowing students the ability to repeat the experiments with a stochastic model demonstrates how an experiment with all the same variables can have different results. They can use their results to answer new questions with the same computational model.
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As I see it, the greatest advantage to computational modeling is the ability to change e variables as students run and rerun their investigations. Students can easily change their models to investigate new and different questions.
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Time constraints is probably the most obvious differences between running an experiment and running it with computational models. Even when the result(s) can be singular there are still so many variables that only a computational model can accomplish. Additionally that same models can be done online live with other offsite students and teachers!
Typically students design an experiment, make predictions, run the experiment and make conclusions. These computational models allow for continued testing so that students can rerun experiments to ensure that they get the same or similar results. This idea of running an experiment more than once is crucial in sciences and emphasized in NGSS, so it will be a great addition to my class.
It doesnât appear to be so different but it allows for more repetitions, time for changing and testing many hypotheses and manipulating variables .
One experiment that I do in class is making models of the different types of alternative energy. If we used computational models, the students would be able to change some of the variables. For example, the difference in the amount of energy produced from a spinning turbine would be easy to see if you could change the rate at which the wind was blowing, or the amount of water flowing. This would be another great tool to have in the classroom.
I do like that you can use the computer model over and over. There are many times where if I could go back to the lab that we did, it would help build background and show how the lab relates to the current thing that I am teaching. Using computer models would give teachers a great tool for connecting concepts by being able to go back and look at it later.
Equipment/material availability and time constraints have a major effect on the types of labs that can be conducted in the science lab. The use of computer models would be an asset to our science program.
Computational Science is a science uses advanced computing and data analysis to understand and solve real-world complex problems. It allows you to experiment with the complete system experiment of the species, environment, and other influencing factors interacting as you conduct the experiment. A possible experiment would be the effects of climate change on the environment. This will be incredibly powerful for the classroom.
Thank you for sharing this!
I think computational models can help close the digital divide that classrooms suffer from with the lack of funding for supplies and equipment. Students should be exposed to both types of experiments as they move through their science curriculum.