Archive for category Education
Distance learning vs in-person training—pros and cons
Posted by mark in Education, Uncategorized on January 7, 2025
In March of 2020 when the Covid-19 pandemic came to a head with widespread quarantines, the Stat-Ease training team quickly Zoomed (pun intended) our workshops from in-person (IP) to distance learning (DL). It went amazingly well from the start.
Coincidentally, two of my grandchildren shifted from IP grade-school to DL at our home. The youngest, a kindergartner (Laine), benefited greatly by the oversight of my wife Karen—a retired preschool teacher. I helped the other (Archer), a third grader. It did not start well due to many technical difficulties and troublesome adjustments for teachers and students. We continued our DL home schooling the following school year due to the ongoing quarantine in Minnesota. By the time IP classes resumed, the DL went about as well as could be expected—the most difficult class being physical education, especially in the winter due to our home lacking a gym.
This unplanned experiment on DL across the range of child versus adult revealed a big interaction effect due to the age of the learners—IP being best for grade schoolers and DL being a very viable alternative for mature students. A few weeks ago, I got reinforcement for this observation when teaching cribbage IP as a volunteer to Laine—now in 4th grade—and two of her classmates. This would have been far harder DL.
The reason I’m bringing all this up is that my colleague Shari Kraber, who retired as our workshop manager but continues to provide training, asserts that “in-person training is not as ideal educationally and that the retention of the materials is BETTER using distance learning.”* I’m also a big fan of DL—far easier for me to teach from my home offices in my summer or my winter home (or on the road between). Google’s AI (Gemini) says that there’s no definitive answer on IP vs DL, and that the biggest factor is quality of the teaching and the materials, which makes a lot of sense to me.
Rachel Poleke, our current workshop manager, suggests that another big factor is the preference of individual students for IP versus DL. I totally agree: Ideally the delivery would be tailored to each student. This being impractical, Stat-Ease instead offers on a class-wide basis to deliver private training either way, depending on the preference of our client. For example, one year ago last September I traveled to Netherlands to teach a DOE workshop for a client headquartered in Leiden’s Bio Science Park. That was fun and very gratifying for the great response. It’s nice to take a break from Dl, benefitting by much stronger feedback from students (e.g., the ‘deer in the headlights’ look when clueless) and the ability to watch them work through case studies (our workshops are computer intensive).
Stat-Ease plans to present a rare IP public workshop—Modern DOE for Medical Devices—at our Minneapolis headquarters this year. This brings a huge advantage of DL training immediately to mind: Anyone from anywhere in the world can Zoom in, thus making it far easier for us to achieve a critical mass for class.
One thing I can say for sure—it’s great to have such a viable option for DL nowadays. When I first began working as a trainer of quality-engineering tools in the 1970’s, the technology for DL existed (e.g., PLATO) but, being pre-Internet and all, it could not compete with IP.
It will be interesting to see how things settle out in coming years for IP versus DL, both for corporate training and schooling at primary and secondary levels. Hopefully, the quality of education (based on subjective measures!) will not be lost in the shuffle of convenience for scheduling and the relative costs.
*1/1/25 Stat-Ease blog Ask An Expert: Shari Kraber
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