Computer Science in Science PD: Using Computer Models in Science - Discussion


They are similar in that fact that they are both trying to figure something out. They are different because with computational models you can go much further (larger scale, not in same geographic location, amount, time, etc) in study than a classroom experiment.


IN hands on inquiry ;earning, students set the parameters of the experiment by coming up with a hypothesis an variables and then conducting the experiment. This programe seems to address the same. However, I worry about the kinesthetic learners and their approach towards the computational models.Student interest maybe lost in the repeated computing from the real topic at hand.


When teaching science we often follow the scientific method which has a general series of steps to take. Hands-on experimental design in the classroom and computational models are similar in that they are controlled experiments. The primary difference is probably the ability to go back and modify the parameters and run the computer models without necessarily wasting resources.


I agree. There are times when a lab doesn’t turn out the data that was expected due to things like human error and/or the lack of time or materials. Utilizing computer models might make revisiting an investigation more feasible in the classroom setting.


Experimental design is similar in regards to controlled variables. Computational models can be less expensive.


I’m going to check out pHET. Thanks for sharing.


The first topic in science is scientific inquiry which is a
great introduction into science fair projects which students are required to do
in my school. Experimental design allows students to collect data quickly and
if parameters are created correctly then it will yield a sufficient amount of
data. Students will also be able to complete numerous trials within a class
period. They can also be more creative when designing an experiment.


They are similar in that they have hypothesis, variables, data collection and analysis. The experimental design with computational models allows for manipulations that can lead to an hypothesis, instead of the usual order of beginning with an hypothesis. This keeps students thinking. The ability to do multiple trials and look at a variety of possible outcomes is difficult to do with classroom experiments. Time is a major factor. By using the simulations students could run multiple trials, change behavior of components and develop hypothesis based on information from manipulations.


I also like the idea of multiple trials without multiple set ups that are costly and usually cannot be done because of lack of time. Using computer models would allow our students to see a distribution of outcomes.


Computational science is similar to conducting experiments in the lab in that you are identifying variables that you will change or keep constant then look for cause and effect relationships. They are both similar in that they require the scientist to ask questions and generate testable hypotheses. They differ in that computer models allow you to collect data for multiple trials (adding randomness to your experiment).


In agreement with the lack of understanding of changing variables- or getting a real understanding of experimental design.


In class, when conducting experiments, students focus on the fundamental skills for science inquiry. They follow the scientific method closely and record and interpret extrapolated data. In this way, computational models are similar to in class experiments. In both, the steps are the same. The computational model is different because the simulation can be run multiple times and a larger data set can be gathered.


One in-class experiment I do in both astronomy and environmental systems is examining temperature as a function of the angle of incident light. This is ideal for study using a computational model since both angles and light intensity can be more easily manipulated. This lends itself to extending the exploration to investigating also the effects of atmospheric gasses on temperature.


Computational models are similar to conducting experiments because students are changing the independent variable and measuring the dependent varialbe, while controlling everything else. Computational models would allow the students to experiment with objects that they would not otherwise get their hands on. (ie planets, airplane jets, explosions)


Multiple trials are difficult in the classroom with out time constraints.


Labs in class tend to be simplified because of limited time and resources. Computational models allows for a larger scope experiment with larger scope and ability to manipulate more variables.


Computational models allow students to identify and manipulate more variable for a more accurate conclusion and research.


I checked out this website as well and I love it. I look forward to implementing this program into my curriculum.


This year I used an online program called Hockey Scholars. It is a new course launched as part of the Future Goals Program that leverages highly interactive gameplay and the sport of hockey to teach students to important Science, Technology Engineering and Math concepts. Using this computational model allowed students to save time and resources in the classroom. Students were able to work at their own pace using exploratory learning approach and exposing students to scientific thinking and data/graphical analysis.


Computational science would allow the students to run their experiments multiple times and correct for any errors. Traditional science experiments, with the class time allotted, allow for students to only run the experiment once. It becomes difficult with traditional experiments to correct for any errors or to see results based upon different data.