Imagine that you want to learn a foreign language, and you memorized the whole dictionary, so you can translate – both ways – any individual word. You still will not be able to read, or write, or talk, because you do not know how to compose a correct sentence – for that you also need to know the grammar of the language (and to practice). Exactly the same situation happens, if you learn all coding commands, but cannot develop a correct algorithm which represents the solution of a problem you need to solve.
Appendix II: this appendix represents a short example of critical thinking (a copy of a LinkedIn post)
From WSJ: "Can Throwing Darts Beat Hedge-Fund Managers’ Stock Picks?"
Everyone can often read of hear on a radio or TV something like: "such and such company beat experts' estimates" or "such and such company missed experts' estimates" - and that happens A LOT - I always think that this statement is wrong. It should say that the majority of people called “experts” again made a wrong prediction about company's data".
Then I think: "Why do people call them “experts” if they are CONSTANTLY WRONG?". For example, Steven Spielberg is an expert in directing and producing blockbusters; there is a factual proof of that - he is much more right on the money then the opposite. And that what makes him an expert.
What does make those people called "experts" be experts if they seemingly mostly wrong then right?
What could help is a list of 3 to 5 year predictions of one (each!) expert and comparison with the actual data. But that's a big secret, I suppose.
NB for "critical thinking advocates": this post represents an example of a critical thinking.
Very briefly, the structure of this critical reasoning:
1. A commonly accepted statement.
2. A counter statement - most critical thinking advocates stop here and call this "critical thinking".
But it is NOT. It maybe "critical", but it is not thinking. To make it thinking:
3. An example supporting the counter statement.
4. A proposition of a PROCEDURE which can assess the validity of the statement.
The links to all six my applications to the NSF 2026 Big Idea Machine (from August 31, 2018 to October 26, 2018):
1. Entry125253: High Frequency Data Streams in Education
2. Entry124656: objective measures of physics knowledge
3. Entry125317: National database teacher PD
4. Entry124655: role of NSF in funding education
5. Entry125719: The new type of a science course for science teachers.
6. Entry126205: The development of the uniform standard for measuring content knowledge in physics.
Dr. Valentin Voroshilov
Education Advancement Professionals
The voices of my students
"The Backpack Full of Cash": pointing at a problem, not offering a solution
Essentials of Teaching Science
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