This is so true! WIth experiments run in the classroom, students usually conduct the experiments one time even though teachers emphasize the importance of running multiple trials. Additionally, because of time constraints, students are not able to run experiments 100’s or even multiple times. However, computational models do not have this problem.
As others have discussed, students are typically looking for one output, based on evidence collected and predetermined by curriculum. Computational models allow students to have different outputs that are random. This saves time and allows for learning beyond testing just one question.
In actual experiment in the classroom the students ability to engage in inquiry is limited do to supplies, time, etc. Whereas in modeling they can test ideas that occur while experimenting allowing for deeper understanding.
Both in the traditional hands on instruction and the Computer model allow students to fully walk through the scientific method, students can still create a hypothesis based on what they know, collect data, reflect and write a conclusion, however with the addition of the computer model, students are able to create a hypothesis based on their unique model. Often times my students do not have enough time to complete the appropriate number of trials during class, which means either we take another class day to finish, or students make conclusions about incomplete data. The computer model eliminates this as a problem since students can finish their assignment outside of class, This is also an advantage for those students that are absent. Being absent from class may no longer be an excuse for not getting your lab work completed:)
This is so true for smaller districts and schools. I have spent soo much of my own money to buy supplies for the most basic labs due to the lack of funding. Computer modeling would be such a great help in reducing/eliminating this problem.
When I taught 5th grade we had a unit called Land and Water where we built physical models to demonstrate and collect data about erosion.
One lesson in particular creating a computer model would have been more helpful is when the kids had to study and then build dams based on a performance task. They were to build a dam that would help the residents who lived in a flood plane not lose their homes.
Once the kids created a dam and then poured the water they could not modify their design to retest because the soil as too wet to hold up. Also it change the variable of the soil at that point.
A computer model would have been more helpful for the kids to create, test, redesign and retest their theories.
This is exactly how I feel about using the computer models. I am in a smaller district and the funding is a big issue. I also love the fact that the students can challenge the norm and extend their learning.
When conducting experiments in class we always discuss bias and human error. Furthermore, we typically repeat an experiment in multiple pairs or groups and average our results to address bias and human error. Computational models are different because they are able to eliminate human error and bias in many cases.
In a lab, the ability to control precision becomes difficult. Especially for a middle school student. A computer model can allow for greater accuracy in completing labs.
With computational experiments students are able to repeat an experiment over and over to achieve more results and save on time. In a traditional lab setting, students would have to conduct an experiment, record an observation, and start again. It saves time especially when students have to manipulate the experiment.
In the general science lab, after students have completed the lab, they record and analyze data to draw conclusions based on that data to determine their answer to the problem question and to evaluate their hypothesis. It is hoped that with several lab teams completed a lab, and discussions of the variables that might have affected the results, that all students will get a clear understanding of the concept in question. This however, does not always occur with a few, who still may need more. With the computational model, students can repeat the simulation if further clarifications are needed (however in the interest of time, this is rarely an availability). Students can even complete a simulation again at home for further evaluation. Simulating a lab even lends itself to a ‘flipped lab’ where lab work is designed, and completed at home, and the results are assessed, presented, and discussed in class.
In essence, simulations allows an unlimited access to the lab processes and the access to manipulate the variables of that lab until it is clearly understood.
Computational models can overcome the traditional hurdles of time, human error, and scarcity of materials.
In science class, students create an experimental design around a purpose. It may take time to run the experiment and it needs to be okayed by the teacher. Here, in computational models, students can create the design, analyze the results and redesign the experiment to correct some of the variables that they may have missed.
Simulations will allow for my students to plan out investigations that would normally be impossible in the typical science lab. Usually time constraints, location and cost prevent us from doing these kinds of experiments.
Using computer models to create experiements is another way of running an experiment. Computer models leave out, with certainty, human error. The need for variables to be controlled is imperative in experimentation, and with computer models, this need can be easily achieved.
dbloch’s original post and all the replies are spot-on - I must read the rest of the other 200+ comments now, but truly, this first post captures the essence of why we (teachers) want to use computer models in our classes
Even though my students have had science classes before they arrive in 7th grade, they’re all over the place in terms of understanding experimental design. Over the years the kids seems to only “know” that a hypothesis is “an educated guess” and not much beyond that… IV and DV are merely vocabulary terms that are much too easy to confuse (perhaps this is related to the rise in standardized testing and other educational trends??) when they aren’t deeply understood. I am very excited about the potential of using computer models in my classes so that my students can really learn cause and effect authentically.
When I think about the science practices taught in a traditional class, I find them to be somewhat similar in nature, in that they follow the steps of a traditional scientific method. The difference between an classroom experimental design and one embedded with computational models, is that the later allows for more control of variables. When students create and run their own experiments, oftentimes anomalies can occur which might skew the results and conclusion. With computer models, you have a better control of variables.
Many of the posts discuss similar challenges with traditional experiments. Resources become a major issues, primarily when budgets are cut. With computational models, you can still have an engaged audience while following a scientific methodology.
There seems to be a lot more randomness in computational models because in designing experiments, you pay close attention in making sure that all trials have the same exact conditions to ensure the precision of your results. Regardless, both experimental design and computational models require multiple trials.