Using a computational model cuts down on human error and it allows the students the opportunity to return to an experiment without having to restart from scratch when our hour class time is over.
I second that! Time is so valuable and in such a short supply.
As I reflect on some similarities of âReal-Worldâ experiments and Computational models, the scientific method is present in both cases. As differences goes, I am drawn to human error, time constraints, and resultant expectations. Working with students human error is always reflected on and is excepted, with practice human error is minimized, but still present. With computational human error can be pinpointed to the processes of the simulation and can be tweaked to then have continuous repetition of the model without the human error. I big difference I must also note is the lack of clean up that ends with every âReal-Worldâ experiments.
Previously students were given a problem and asked to offer a hypothesis, with NGSS and inquiry students are offered a phenomena and asked to offer a claim with evidence to corroborate. Also if a student/group had a snafu with experimentation they are able to efficiently correct the error or possibly add to the discussion what results were unearthed by doing the simulation in an alternate manner.
When conducting investigations I utilize the NGSS Science and Engineering Practices. Students analyze and interpret data, ask questions and define problems, develop and use models, plan and carry out investigations, and all the other SEPâs that are required to complete the investigation and understand the concept. It is different from the experimental design enabled with computational models because the majority of these investigations are not simulated models that allow for quick, easy, and inexpensive investigations.
Experimental design process will supplement our class experiments; enable student so run 5,10,20 trials without needing extra time or expenses. A simple chance,probability or random event is a great and easy example. The likelihood of camouflaged vs noncamouflaged organisms to survive in a system can easily be demonstrated in hands-on activity or a experiment on the computer
Human error,time and money. Sounds like this is the solution to all of our experimental issues
Computer models are more cost efficient and would allow students to be more imaginative without being hazardous.
I think that computer-based models, especially like the ones used on the University of Coloradoâs Physicâs site (www.phet.edu)(***my site may be incorrect) are make the variables in the online experiments more carefully managed. In addition, the experiments can be held at the speed at which the experiment wishes to work, changing the variables as they need and at the quantities and speed at which they wish to do so. However, computer based models donât always replace field based experiments.
âŚandâŚbeing able to replicate with many classes, year after year, without the lab set up/clean up time
Using computational models are similar to conducting experiments because data is still gathered and variables are involved where you are studying a relationship. The difference is more can be done using computational models and it saves a person time and money.
Both collect data and variables are involved. The difference is the changing of the variables and the large amount (more data) so you can have a more accurte picture.
When I teach experimental design, I have students preform experiments that they normally only preform once. With computational models they can preform the experiment multiple times and alter the variables a lot easier.
The scientific process and thinking remain, but the students are able to conduct multiple experiments with little to no constraints.
Thereâs more visualization and multisensory involvement with physical experiments. I loved doing chemistry experiments. There was the matter of holding chemicals and seeing them react that is not quite the same in a simulation. Simulations do give you an easier and quickier look at perhaps multiple variables at work.
Iâve used the same website. Very helpful, but I found to be less than impressive visually for students versus some of the more dynamic models out there.
computer modeling is like our classroom experimentation but with more flexibility, e.g. it is easier to run multiple trials that would not be possible with limited materials and time.
Computational models eliminate the human error, coupled with multiple trials, makes it easier for students to see the outcome of the experiment. They also encourage creating new hypotheses as students change variables to try and answer new questions that arise from running a computational model.
with computation models,experiments can be run multiple times at little cost. For example, a simple chemistry reaction could be modeled and experimented on multiple times with different parameters without the cost of the materials being factored.
agreed but also at the same it doesnât allow for other random variables like environment factors that we canât always control like the weather changing