Correct. Being able to do this would help students understand how much data is needed to draw concrete conclusions.
Pretty much similar in their overall adhesion to the rules of experimentation. Computer models allow greater flexibility in changing behaviors, repeatability, and scalability. It also eases the ability to develop and test multiple hypothesis.
The biggest difference I noticed is the flexibility in changing things around, therefore adding more complexity to the design. Students are quickly able to make a prediction, run the simulations, multiple times if needed, and adjust based on what they see. In the actual lab, these adjustments cannot be made as easily or quickly.
Computational models allow for ease in repetition. Students can develop their hypothesis based on their observations of the simulation as opposed to before experimentation. Cost and time are also benefits of computational models verses lab experimentation.
Usually students conduct experiments in the classroom once due to time and budget limitations. With Computer computer models students can repeat their experiments and explain the results based on their data collected.
Thanks for the website recommendation. With the new NGSS emphasis on modeling, it will be useful.
ED with computational models allows for a greater number of trials than what can be completed in the classroom. In addition, the ability to eliminate or greatly reduce human error provides much more accurate data. With NGSS, student are supposed to observe a phenomena that brings them to their question/hypothesis and further investigation. I see that as similar to the use of the computational model to develop hypotheses for data collection and analysis.
I really see this as a great opportunity and an argument for why computational models should be used in the classroom! Rachael
In many of the experiments my students conduct, they are just verifying the scientific principle. While computational models can have the students test their own ideas and develop conclusions. The students are able to fully experience going through the scientific method.
Usually when running an experiment the hypothesis is done before. However in the video we learned that with computational models the students build their hypothesis based on the observations they make during the simulation. Also it is known that in order to increase accuracy you must have multiple trials. The computational models allow for repetition and minimal if any human error. The computational model reduces time the experiment takes as well as the inconvenience.
I agree with many here. Currently in science, we conduct traditional labs with hypothesis first, and then run the lab. Usually we only have time for 1-3 trials depending on time and complexity. Often, I will gather each groups’ data to illustrate multiple trials. With computational models, we can spend a day or two developing the model, and then a day running trials and collecting data. I think students will also be more engaged using computers, building the model, and using it as a tool.
Conducting experiments in class can be similar to computational models in that there are controlled variables in which to be focused on.
Also, the students can return again and again to the computational model whereas with an in class experiment in a shared room with many moving parts, it is difficult to continue working on a model day after day.
The experimental design process described here is very similar to the process I have used in class. What is different and exciting is that using computer models includes randomness and adds another layer of critical thinking as students identify and justify pertinent variables.
“If” everything is set up and the students do everything as directions dictate, then the similarities are great. That being said, as teachers we know that perfectly executed lab work is rarely ever the case. Computational models not only limit error but allow the students time to experiment by adjusting the variables and observing the different reactions.
There are a few differences I see that would make computational models more effective than physical labs. The time to set up the experiment would be reduced, leading to more time to run the experiment. Setting up/tearing down labs takes up a lot of class time! Also, the amount of human error would be reduced. It doesn’t matter how many time you explain procedures and have students practice, there will always be mistakes.
This will be my first year teaching science in the elementary setting, so I have not had much exposure to specific experiments, however, from my own experience as a student and even the experiments I had my students do last summer, we generally create our hypothesis first as a way to prove or disprove our thinking. This model allows for the hypothesis to be formed after experimentation has taken place.
“Another layer of critical thinking” is the part I’m most looking forward to for my students. They struggle with this in every subject area, so maybe this increased amount necessary for computer science will then transfer to ELA & mathematics as well!
science experiments can be costly to set up and maintain. materials can expire and become old. computational experiments or computer simulations can be repeated and updated as needed. however, nothing digital can yet compare to the tangibility of hands-on experiments when real-life environment sometimes dictates the unpredictability of the outcomes, which could be unexpectedly positive and applicable.
Many of the lessons in our curriculum includes models but they are predetermined by the curriculum. Students rarely have the opportunity to create their own models and set their own variables. With computational models, students will be able to set different parameters/variables and create hypotheses and conclusions.