Archive for category Education
A solution for saving migrating birds from disorienting light pollution
Posted by mark in Education, Nature, science, Uncategorized on May 3, 2024
My grandson Archer and his class of sixth graders at Stillwater Middle School advanced to last week’s national Solve for Tomorrow competition in Washington, DC–an amazing accomplishment at their age. The event, sponsored by Samsung, empowers students in grades 6–12 to leverage the power of STEM (science, technology, engineering and math) to create innovative solutions addressing critical issues in their local communities.
Archer and his classmates focused their attention on reducing the impact of light on bird migration patterns in the St. Croix Valley. They developed a very inventive plan that featured bioluminescence; sensors to reduce unnecessary light and a flower-petaled, controllable cover for directing streetlights downward.
Being one of just 10 schools across the country to be named national finalists, they earn $50,000 in Samsung technology and supplies for their classroom. To top it off, Archer and his classmates won an additional $10,000 by winning the Community Choice award based on a popular vote.
I expect Archer and all will go far by their STEM power. Hopefully, the birds will also continue to go far by being better protected from light pollution along their way.
2023 South Dakota Mines (SDM) paper helicopter flyoff
SDM Chemical Engineering Seniors Jarvie Arnold, Gregory Clark and Martin Gaffney, pictured left to right, ran away from the field with their “Team Helicopter?” flying machine.
With a lot of ingenuity and fine-tuning of the paper-helicopter design via a full two-level factorial, they achieved a flight time of 8.66 seconds from the balcony of the Chemical and Biological Engineering and Chemistry (CBEC) building. This nearly broke the all-time record of 8.94 seconds achieved by The Flaming Bagel Dogs in 2013.
Check out this awesome video from the 2011 flyoff and follow the link from there for background on the SDM paper-helicopter experiment, which I’ve been overseeing since 2004.
Kudos to Professor Dave Dixon for championing CBEC’s DOE class throughout the years. This elective rates well above any others in surveys of graduates, who say it was “immensely” helpful for their career advancement. I’m very thankful to be a contributor to this success story for teaching DOE at the college level.
Rock on SDM CBEC!
LEGO bricks used to build regression model
LEGOs are very popular around here in the Twin Cities of Minnesota. They keep our kids, such as my grandson Archer, occupied during the long winter when our cold weather limits outdoor activity. See his creative solar-powered banana-research station pictured. No wonder the local Mall of America features a LEGO Imagination Center!
Thus, naturally, this recent Journal of Statistics and Data Science Education publication on “Building a Multiple Linear Regression Model with LEGO Brick Data” caught my eye. The article lays out a fun class-project by two Iowa State University Statistics Department Associate Professors—Anna Peterson and Laura Ziegler. They developed an “innovative activity that uses data about LEGO sets to help students self-discover multiple linear regressions” that “explore the relationship between the Amazon price and the number of pieces per set for two sizes of bricks, small and large.” The students start with graphical displays, then progress to simple linear regression, and, finally, develop models that uncover interactions of factors.
Using the spreadsheets provided by Profs Peterson and Ziegler, I used the Import tools in Design-Expert® software (DX) to reproduce their results.
First off, Graph Columns revealed a strong correlation (r=0.986) between the total number of pieces and the number of unique pieces per LEGO set—this being a measure of the potential cost for individual molds. Seeing this I decided not to include both factors in my modeling—going forward only with the total number of pieces, as did Peterson and Ziegler.
Next, I did a Design Evaluation of a polynomial model with the main effects of size (A), theme (B) and number of pieces (C), plus their three two-factor interactions (AB, AC and BC), and the quadratic term for the number of pieces (C2). The results revealed an aliasing between size and theme—only the Duplo came in the large size. Thus, theme dropped out of my focus.
I then deployed DX to do a regression on the model A, C, AC and C2. Residual diagnostics revealed via the Box-Cox plot that a log transformation would do significantly better. The only catch in this metric is a high Cook’s Distance for the large-pieced Duplo Modular Playhouse set—not a problem, per se, but curiously influential.
In the end I reproduced the interaction shown in Figure 4 of the publication, but with a bit of flair for some curviness and the addition of confidence bands as seen below.
You can see that the effect on price by the number of LEGO pieces depends greatly on size of the bricks. My conclusion is that going for the small sized LEGOs is by far the most cost-effective way to keep kids busy, provided them being old enough to do so safely and with the exceptional focus needed to make something out of them.
PS While researching this blog, I noticed that in just the few years from when the costs got gathered by Peterson and Ziegler, LEGO prices went way up on Amazon. Given the recent performance of stocks and bonds, you might do well by investing in these toys per January’s Research in International Business and Finance. See the highlights (average long-term return of 11%–better than gold!) at LEGO: THE TOY OF SMART INVESTORS.
The Feynman Technique for mastering concepts
After I watched a statistics webinar on the Stat-Ease channel, YouTube laid out a series of further videos that ‘they’ (the artificial intelligence) thought I might like to see next. They did well by suggesting this simple explanation of “How to Learn Faster with the Feynman Technique”. Being an admirer of this famous physicist and his incredible ability to explain complex concepts, I am happy to now know that his secret is simple: Once you understand something, spell it out as simply as possible to an imaginary listener. In other words, teach what you’ve learned to someone else.
Though, thanks to the AI wizard at YouTube, I only just came across the Feynman Technique, it turns out that I inadvertently applied this approach in my first try at teaching statistical design of experiments (DOE). I understood DOE very well, or so I thought until I had to lay it out the first time for a group of industrial researchers. As soon as the questions started, I realized that I should have rehearsed a lot more with a dummy audience, such as a goldfish.
“The questions of the students are often the source of new research. They often ask profound questions that I’ve thought about at times and then given up on, so to speak, for a while. It wouldn’t do me any harm to think about them again and see if I can go any further now. The students may not be able to see the thing I want to answer, or the subtleties I want to think about, but they remind me of a problem by asking questions in the neighborhood of that problem. It’s not so easy to remind yourself of these things.”
Richard Feynman, Surely You’re Joking, Mr. Feynman!‘
To make matters worse, the class was held at a restaurant on stilts just off the shore of San Francisco Bay. During the first few hours of my class an earthquake hit. The whole building wobbled back and forth for a minute or so. That night another earthquake shook me out of bed.
Somehow, I made through the week-long class relatively intact, but with a vow to never again come in so unprepared for a presentation.
Lesson learned!
Our nest emptying out again with grandkids going back to school
After raising 5 children, my wife and I never imagined that we would again experience the bittersweet beginning of a school year and the ambivalent feelings about the coming peace and quiet. However, the pandemic brought a surprising year-plus of us hosting school for a kindergartener (pictured) and a third grader. On Tuesday these two will advance to their next levels—in person once again.
It seems to me that our at-home school kids did well academically—possibly even better at a distance than in class. But they will do well for overall development by getting back in touch with their peers and teachers…no doubt.
Unfortunately, based on Minnesota Comprehensive Assessments, the disruption in State-wide education caused by the pandemic caused an alarming downturn in students meeting their grade standards, particularly in math and science.* The hit on math education (relative to reading) extended nation-wide as graphically illustrated in this August 15th post by The 74. Alarming!
Let’s hope that our students and teachers can withstand the Delta and newer Covid-19 variants until vaccines become available for all school age children. Now is the time to go full STEAM ahead (not overlooking “arts” in the quest for more science, engineering and math).
*See this 8/27/21 report by MPR News: MN state test scores reveal deep impact to child learning during pandemic
Illuminating results from sparkler experiment
Posted by mark in Education, pop, Uncategorized on April 16, 2021
This video, concluding with the obligatory lighting up of multiple sparklers, lays out the results of another fun and educational experiment by Chemical and Biological Engineering (CBE) students at South Dakota School of Mines and Technology (SDSMT) for their Applied Design of Experiments for the Chemical Industry class.
The testers: Anthony Best, Henry Brouwer, and Jordyn Tygesen, uncovered significant interactions of wind, water and lighting position on the burn time as illustrated by the Pareto chart of effects from Design-Expert software.
I expect these three experimenters will be enjoying extremely sparkly celebrations this summer!
Mentos volcano rocks Rapid City
Posted by mark in design of experiments, Education on April 12, 2021
It was my pleasure to oversee another outstanding collection of fun experiments by the Chemical and Biological Engineering (CBE) students at South Dakota School of Mines and Technology (SDSMT) for this Spring semester’s Applied Design of Experiments for the Chemical Industry class presented by Stat-Ease. They continued on the excellent tradition established by the class of 2020 which I reported in my blog on “DOE It Yourself” hits the spot for distance-learning projects.
As promised, I am highlighting a few of the many A+ projects in StatsMadeEasy, particularly those with engaging videos. My first selection goes to Dakin Nolan, Erick Hoon and Jared Wilson for their “DOE Soda and Mentos Experiment”. They studied the “heterogenous nucleation of gases on a surface” caused by type of soda, its temperature and volume versus the quantity of Mentos. See the results in the video (“the moment you’ve all been waiting for”). Do not miss the grand finale (“The Masterpiece”) that shows what happens if you mix 15 Mentos in a 2-liter bottle of hot Diet Coke.
It’s hard to say how high the cola spouted in the blow out at the end, but it must have made a big sticky mess of the surrounding area. At similar conditions but at a more prudent maximum of 3 Mentos (the highest level actually tested in the DOE), Design-Expert predicts a peak of 310 inches—an impressive 25 feet of magma.
Further work will be needed to optimize the dosage of Mentos. Perhaps 15 of the sugary oblate spheroids may be overkill. There’s always room for improvement, as well as more fun, making volcanoes.
Archer’s Big Bounce Experiment
I am a big fan of University of Minnesota Athletics—even more so now after they sponsored a Science of Basketball project for grade schoolers. My 9-year-old grandson Archer jumped at the chance to put a variety of basketballs to the test with my help. For the results, see the video we submitted to the UMn judges.
Archer’s findings–wood being better than rubber for bounce–stand out in graphics generated with Design-Expert software.
Archer enjoyed doing this science project. I feel sure it helped him understand what it takes to design an experiment, do it properly and analyze the result. My only disappointment is that the high-tech cell-phone app for measuring height, which I used for my experiment on elastic spheres, failed due to too much echo in the gym, most likely.
However, I discovered another intriguing basketball-physics experiment at the Science Buddies STEM website. It determines where a bouncing ball’s energy goes . This requires deployment of an infrared-temperature gun with a laser beam. Awesome! Archer will like that (if he can wrestle the laser gun away from me).
Magic of multifactor testing revealed by fun physics experiment: Part Three—the details and data
Posted by mark in design of experiments, Education, Uncategorized on September 2, 2020
Detail on factors:
- Ball type (bought for $3.50 each from Five Below (www.fivebelow.com)):
- 4 inch, 41 g, hollow, licensed (Marvel Spiderman) playball from Hedstrom (Ashland, OH)
- 4 inch, 159 g, energy high bounce ball from PPNC (Yorba Linda, CA)
- Temperature (equilibrated by storing overnight or longer):
- Freezer at about -4 F
- Room at 72 to 76 F with differing levels of humidity
- Drop height (released by hand):
- 3 feet
- 6 feet
- Floor surface:
- Oak hardwood
- Rubber, 3/4″ thick, Anti Fatigue Comfort Floor Mat by Sky Mats (www.skymats.com)
Measurement:
Measurements done with Android PhyPhox app “(In)Elastic”. Record T1 and H1, time and height (calculated) of first bounce. As a check note H0, the estimated drop height—this is already known (specified by factor C low and high levels).
Data:
| Std # | Run # | A: Ball type | B: Temp deg F | C: Height feet | D: Floor type | Time seconds | Height centimeters |
| 1 | 16 | Hollow | Room | 3 | Wood | 0.618 | 46.85 |
| 2 | 6 | Solid | Room | 3 | Wood | 0.778 | 74.14 |
| 3 | 3 | Hollow | Freezer | 3 | Wood | 0.510 | 31.91 |
| 4 | 12 | Solid | Freezer | 3 | Wood | 0.326 | 13.02 |
| 5 | 8 | Hollow | Room | 6 | Wood | 0.829 | 84.33 |
| 6 | 14 | Solid | Room | 6 | Wood | 1.119 | 153.54 |
| 7 | 1 | Hollow | Freezer | 6 | Wood | 0.677 | 56.17 |
| 8 | 4 | Solid | Freezer | 6 | Wood | 0.481 | 28.34 |
| 9 | 5 | Hollow | Room | 3 | Rubber | 0.598 | 43.92 |
| 10 | 10 | Solid | Room | 3 | Rubber | 0.735 | 66.17 |
| 11 | 2 | Hollow | Freezer | 3 | Rubber | 0.559 | 38.27 |
| 12 | 7 | Solid | Freezer | 3 | Rubber | 0.478 | 28.03 |
| 13 | 15 | Hollow | Room | 6 | Rubber | 0.788 | 76.12 |
| 14 | 11 | Solid | Room | 6 | Rubber | 0.945 | 109.59 |
| 15 | 9 | Hollow | Freezer | 6 | Rubber | 0.719 | 63.43 |
| 16 | 13 | Solid | Freezer | 6 | Rubber | 0.693 | 58.96 |
Observations:
- Run 7: First drop produced result >2 sec with
height of 494 cm. This is >16 feet! Obviously something went wrong. My guess
is that the mic on my phone is having trouble picking up the sound of the
softer solid ball and missed a bounce or two. In any case, I redid the bounce.
- Starting run 8, I will record Height 0 in Comments as a check against bad readings.
- Run 8: Had to drop 3 times to get time
registered due to such small, quiet and quick bounces.
- Could have tried changing setting for threshold provided by the (In)Elastic app.
- Run 14: Showing as outlier for height so it was re-run. Results came out nearly the same 1.123 s (vs 1.119 s) and 154.62 cm (vs 153.54). After transforming by square root these results fell into line. This makes sense by physics being that distance for is a function of time squared.
Suggestions for future:
- Rather than drop the balls by eye from a mark on the wall, do so from a more precise mechanism to be more consistent and precise for height
- Adjust up for 3/4″ loss in height of drop due to thickness of mat
- Drop multiple times for each run and trim off outliers before averaging (or use median result)
- Record room temp to nearest degree
Magic of multifactor testing revealed by fun physics experiment: Part One—the setup
Posted by mark in design of experiments, Education on August 23, 2020
The behavior of elastic spheres caught my attention due to a proposed, but not completed, experiment on ball bounciness turned in by a student from the South Dakota School of Mines and Technology.* I decided to see for myself what would happen.
To start, I went shopping for suitable elastic spheres. As pictured, I found two ball-toys with the same diameter—one of them with an eye-catching Spider-Man graphic.
My grandkids all thought that “Spidey” would bounce higher than the other ball—the one in swirly blue and yellow. Little did they know just by looking that “Swirley” was the one with superpowers, it being made from exceptionally elastic, solid synthetic rubber. Sadly, Spidey turned out to be a hollow airhead. This became immediately obvious when I dropped the two balls side by side from shoulder height. Spidey rebounded only to my knee while Swirley shot all the way back to nearly to the original drop level, which really amazed the children.
My next idea for the bouncy experiment came from Frugal Fun for Boys and Girls, a website that provides many great science projects. Their bouncy ball experiment focuses on the effect of temperature as seen here.
However, I could see one big problem straight away: How can you get an accurate measure of bounce height? That led me an amazing cell-phone app called Phyphox (Physics Phone Experiments) which provided an ingenious way to calculate how high a ball bounces by listening to them hit the floor.** Watch this short video to see how. (If you are a physicist, stay on for how the narrator of the demo, Sebastian Staacks, worked out all his calculations for the Phyphox (In)elastic tool.)
The third factor came easy: Height of drop. To make this obvious but manageable, I chose three versus six feet.
The fourth and final factor occurred to me while washing dishes. We recently purchased a thick rubber mat for easy cleanup and comfortable standing in front of our sink. I realized that this would provide a good contrast to our hardwood floors for bounce height, the softer surface being obviously inferior.
To recap, the four factors and their levels I tested were:
A. Ball type: Hollow or Solid
B. Temperature: Room vs Freezer
C. Drop height: 3 vs 6 feet
D. Floor surface: Hardwood vs Rubber
Using Design-Expert® software (DX) I then laid out a two-level, full factorial of 16 runs in random order. To be sure of temperature being stabilized, I did only one run per day, recording the time the first bounce and its height (calculated by the Phypox boffins as detailed in the videos).
When I completed the experiment and analyzed the results using DX, I was astounded to see that neither the type of ball nor the differing surfaces produced significant main effects. That made no sense based on my initial demonstrations on side-by-side bounce for the two balls on the floor versus the rubber mat.
Keeping in mind that my experiment provided a multifactor test of two other variables, perhaps you can guess what happened. I will give you a hint: Factors often interact to produce surprising results, such as time and temperature suddenly coming together to create a fire (or as I would say as a chemical engineer—an “exothermic reaction”).
Stay tuned for Part 2 of this blog on my elastic spheroid experiment to see how the factors interacted in delightful ways that, once laid out, make perfect sense to even for non-physicists.
*For background on my class and an impressive list of home experiments, see “DOE It Yourself” hits the spot for distance-learning projects.
**I credit Rhett Alain of Wired for alerting me to Phyphox via his 8/16/18 post on Three Science Experiments You Can Do With Your Phone. From there he provides a link to a prior, more detailed, post on Modeling a Bouncing Ball.