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John Weisenfeld
Science Teacher and Technology Consultant
John Weisenfeld is a highly experienced educator and STEM advocate with a strong background in teaching math, science, and engineering.
He holds a Master's degree in Teaching Math and Science from Seattle Pacific University, a Master of Science in Theoretical and Applied Mechanics from Cornell University, a B.S. in Systems Science and Engineering from Washington University in St. Louis, and a B.A. in Physics with a minor in Mathematics from University of Puget Sound.
Throughout his career, John has held various teaching positions, including as an Adjunct Instructor of Physics at Central Washington University, Science Teacher at Pasco School District, and STEM Specialist at Highline School District.
Additionally, he has worked as a Senior Software Development Engineer in Test (SDET) and Senior Test (QA) Lead at Microsoft, gaining valuable experience in software testing and engineering.
John has also contributed to the educational field as an Independent Contractor at National Geographic Learning, a Visiting Teacher at Pacific Northwest National Laboratory, an Online Academic Evaluator at Washington Aerospace Scholars, and a Math/Science Teacher at Wapato School District.
His passion for inspiring students in STEM subjects is further showcased through his volunteer work at organizations like Kindering and his internships at Procter & Gamble and Caterpillar, Inc.
With a diverse background spanning education, engineering, and software testing, John Weisenfeld brings a unique perspective and skill set to his work in promoting STEM education and fostering a love for math and science among students.
Highlights
I think AI (and Python) just helped crack the "organizing students into groups" problem. Let me explain.
Let's say you have 28 students in a class with 8 desks and want to group them into groups of 4. You're curious if random groups will adequately "mix" students and how many times you will have to randomize.
AI helped me write a "naive randomize" algorithm and heat map visualization to see if 5 days would allow each student to work at least once with every other student. (Hint: 5 randomized setting days isn't enough.)
Cranking up the number of days to 60 days (the length of my trimester) and I still happen to miss two pairings. Which was surprising as some students have worked at the same table with the same person upwards of 15 times.
Now, presenting those seating arrangements to students each and every day for 4 different classes each day was a logistic and probabilistic nightmare...
...until...
You tell AI to make those pairings for 28 students over 60 days AND create a PowerPoint for each day with student IDs and Table Assignments. Done. That frees up a bunch of hours each term for actual planning, instruction and building relationships with students.
I'll put the code into a reply to this post.
One of my favorite AI tools is reaching out to US college students. Take a look!
