Discussion Points
Collaboration vs. Competition
Student interactions during the stages of these activities fall into several possible dynamics. Discussing the benefits and pitfalls of collaboration or competition is a useful topic to note, particularly early on, when data is at a premium and there are potentially bogus (unverified) data points present.
Verification of Scientific Findings
Many Right Answers, Some Better Than Others
Data Quantity vs. Confidence
Operational Theory, Modified with New Data
If you have multiple groups working independently with their own data sets, providing single copies of bogus cards (earth, air, water, and fire) can trip up those who trust all data as equally valid. Claims of newly discovered elements are not accepted until verified independently by other researchers, and multiple sets of cards can offer the same validation method. It also improves the analysis when fraudulent data can be effectively dismissed.
One creative exercise is to see how many different ways the early set of cards can be organized.
color of circles - 3 groups
odd/even black numbers - 2 groups
numbers in stars - 7 groups
big/medium/small circles - 3 groups
These exercises show that creativity is an important part of addressing new concepts, and that it is OK to be wrong in your trials. Thomas Edison is quoted as saying, “I have not failed. I’ve just found 10,000 ways that won’t work.” This is important as the best answer rarely is the first one you try. Additionally, it is in these trials that patterns emerge - matching the number in the stars will point students to the missing two elements.
Many theories begin with little data. Currently, string theory, dark matter, black holes, and the like have little data, but they are held in regard for what they enable people to predict. A 1914 Nobel Prize was given for work on the inner ear that was later found to be incorrect in microgravity experiments in earth orbit in the 1970’s. The more data available, the more consistent the patterns, if any, appear, and the greater confidence one has in the conceptual understanding from the model.
The definition of scientific theories is that they represent the best model given the available data. They are falsifiable, and future data collection should test the viability of current theory. The result is that models are regularly displaced by better models that fit newer facts more consistently. The Atomic Theory has progressed steadily from indivisible billiard-ball atoms (Dalton 1808) to the Plum Pudding Model (Thompson 1897) to Rutherford’s and Bohr’s models of electron orbits (1909) to our current understanding of quarks, mesons, gluons, and the like. More data can expose weaknesses with current models and can also provide provide a pathway to a better theory. This is a critical aspect of scientific theories that this exercise can describe very effectively. Early models were not wrong; they were the best available and often led to effective scrutiny that, in turn, could inform and improve subsequent models.
