Reflect on the Science practices you teach with regards to conducting experiments in class. How is it similar/different from the experimental design enabled with computational models?
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Typically in class the students create their hypothesis prior to running the experiment. With the computational models, students are able to create a design, run it, and then build their hypothesis based on the behavior they have observed from the simulation. Students are also able to repeat their experiments using computational models without as much human error as they would have in doing it themselves which will reduce the amount of bias in their results.
The main differences I see is the ease at which computational models can be repeated and the elimination of many sources of experimental error. This is a great way to reinforce the importance of multiple trials. Many times we aren’t able able to run enough trials to make the data reliable due to time and materials constraints. Computational models also eliminate anomalies in data caused by experimental error.
Having the ability to complete the computer based experiment provides opportunity for growth and exploration. Students sometimes are only allowed to complete an experiment once because of funding and time. By completing the online experiment, now students can follow the experiment exactly or challenge the norm and see and experience what happens. This creates a learning environment where students can soar.
Almost all of my hands-on experiments take place outside. There is considerable set-up and break-down time associated with each experiment. There are also environmental factors, such as wind and rain, that limit my ability to do these experiments. In one case, we were supposed to launch rockets but the weather got cold, and we had to wait until spring to do the experiment. Being able to do experiments over and over at any time would be FABULOUS.
Reflect on the Science practices you teach with regards to conducting experiments in class. How is it similar/different from the experimental design enabled with computational models?
When comparing experiments conducted in class with computational models both are designed have controls and to reduce interfering variables. However, in a class setting the group of 2 or 3 students all bring possible interfering variables sometimes knowingly and sometimes they are unaware. With the computer model; human error is reduced to how the person programs the simulation, frustration, off-task behavior are all controlled. Computer simulations can be preformed multiple times after an actual experiment is completed as a follow up when weather and time are negative factors.
I have always used scientific inquiry in my science lessons. I teach the scientific method at the beginning of each year, and then we use the process as students complete experiments throughout the year. I have used hands on labs as well as simulations. I use a website called pHET that has tons of different computer simulations. I have students go through the simulation using the inquiry process. They change different variables to see how other variables are impacted.
With computational models I could have the students do more inquiry based learning than I do now. Of course I use inquiry based learning but lab supplies are expensive and I think the main difference is cost. I could do more.
Lack of materials often limits the amount of inquiry experiments we do in class. As computers become available in our classroom and computer modeling software and sites work their way into the mainstream, junior high school students will benefit from the ability to explore situations using those resources.
Time is always a factor when doing live labs. It is usually difficult to do labs in one class and there are always issues when trying to do a lab over an extended period of time. Computational models allow for flexibility, the chance to continue another day or extended experiments over time. Eliminating many of the issues that limit the ability to provide labs to students.
Computational models can be an improved method of completing scientific inquiry. Computational models can be easily and quickly repeated as well as eliminate many sources of experimental error. Variables introduced by the environment or human error can be easily eliminated with a computational model though not so with a traditional lab. Materials and time are two other limitations to a traditional lab that the computational model eliminates.
Computational models allow students to repeat experiments and look at different sources of experimental error. In class we can not achieve this. Materials are hard to get and yes the clock is a factor in class.
Students are taught in experimental design the importance of multiple trials however, the reality is that in the classroom this is difficult. Also, they are taught that scientist look at the results then make adjustments and run the experiment again. Once again, this is difficult in the classroom. Using computer models they can run the experiments many times and collect a meaningful amount of data which, they can then make adjustments too. What a great tool!
I see computational models as a tool to help my students understand concepts. I teach 12-13 year olds and I do not want to take away the hands-on aspect of experimentation. At the same time, I see this as a skill that they need to develop in order to compete for high-paying jobs.
Computational experiments are not much different in the process of scientific inquiry however the effectiveness can be far more exact than an experiment done in class because it allows the student to do several repetitions in a short amount of time. This allows the student to continue to learn in ways that setting up there own experiment would be very difficult to do with multiple trials. I can foresee a lot of my students really enjoying this type of experimentation.
I like how in a computer model a student’s hypothesis can be fluid and ever-changing. Often, in a typical lab setting, a student must hypothesize and then complete the task before assessing their own predictions.
Since I teach life science many of the concepts are too widespread (food webs) or take extended periods of time (genetics/evolution) in order to see results. Experiments can be done on cell processes, but these occur at the microscopic level and are hard to see. Computational models will allow students to gather data on processes that they typically cannot see.
The experimental design process is similar in that students need to predict outcomes, choose variables, and gather data. The method of gathering data is quite different and different types of analysis can be done with a computer.
