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The Algorithm

The scientific paradigm - it epistemology, philosophy, applications and practice - is the crowning glory of our species. We have learned to observe and model behavior of systems across various spatial and temporal scales - the process is iterative, self-correcting, reproducible and has built and continues to add to the collective knowledge-base of modern science. The details and means to do this are as wide as scientific disciplines and their interconnections themselves.

As educators and professional scientists, we strive to impart the notion of this ideology and its facets to engage and motivate the next-generation of student-scientists. Given the rapidly growing body of scientific knowledge (and its rate), getting students to begin a journey into this paradigmatic program of scientific thought  without just absorbing "facts" but instead reasoning things "from first principles" is challenging, to put it mildly.

In physics, this has become near impossible given the fidelity of the models we have built (of our physically observable and experienced universe) and their applications -- not least of which is why I can write this note here today so someone else can read it! Yet, at its heart, the scientific paradigm is embodied in physics - which surely is a reason why it enjoys as a high degree of success for its use and applications.

So, how can we get student learning in physics to naturally lead to into an axiomatic practice of scientific thinking and thought? We need to construct an algorithm. In fact, active learning in physics seeks to provide this.

The steps then are: (1) observations must be made repeatably and objectively (by different users). (2) a hypothesis must be made - which needs to lead into "design of an experiment" - these lead to exploring and demarcating sample spaces. You can return to (1), proceed or terminate with results. (3) The experiment must be conducted to provide observations, go to step (1)
 
Of course, the devil's in the details of getting this to work right...

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