One of the topics I will be writing about “soon” is about the work that my workgroup at CERN did on data analysis tools and open data.
There are numerous open data sets out on the web for those interested in learning how to do data analysis (data science, big data analysis, etc.). For those interested in particle physics, CERN provides a wealth of data at CERN Open Data Portal. Looking for local data sets is becoming as easy as googling “[your state] Open Data” or “traffic data [your city]”.
Another resource I have recently seen on HackerNews is Kaggle.com data sets . This appears to be a repository or collection of links to datasets that may be of use for those learning about data science. Kaggle.com itself sounds like a potentially useful training site for data analysis, but I’ve only just started to check it out.
I have been involved with the American Association of Physics Teachers since I was a member as a young graduate student, what seems like many ages ago. The membership includes physics teachers from all levels, particularly high school through university. I have been a member continually since becoming a teacher in 2006, and have attended all but one of the annual AAPT Arkansas-Oklahoma-Kansas (AOK) section conferences.
On four of those occasions I have taken the opportunity to present something I thought was a relevant contribution to my colleagues (and once I produced a flyer to offer in lieu of a presentation). It’s a bit intimidating to present at a conference, especially when you still feel like a novice teacher, but it is an experience I recommend — nearly all teachers have something they can share.
2010: Initial Observations of Standards Based Grading in a Modeling Classroom
[Presentation location currently unknown; will update when found.]
2012: Standards Based Grading in the High School Physics Classroom
2015: QuarkNet Opportunities: To Fermilab, Greece, and Beyond (Co-presented with Cyndi Ice)
2017: Computational Physics in the High School (or CC / University) Classroom [Flyer Presentation — sharing link to my resources]
2018: Tools for Computational Methods in High School Physics
Six years ago I stayed up all night to watch the announcement from CERN that was rumored to be about the Higgs boson. I ordered a particle physics textbook that night, having never taken a formal class that went beyond a general historical approach.
Particle physics had always sounded interesting. I increased my participation in QuarkNet, brought particle physics into my physics classes for my students, and a year later I spent a week studying at Fermilab.
Now, six years later, I attend lectures in the very hall where CERN scientists broadcast their discovery to the world, learning from scientists involved then and now in pushing the forefront of knowledge in physics.
I have had the good fortune to spend five of the last six summers working with scientists and students at KU doing particle physics research and projects.
And I have had the immense fortune of meeting many physics teachers around the US and the world who share a passion for physics, learning, and teaching.
I could not have dreamed twelve years ago when I started my teaching journey what amazing opportunities I would find. I’m a little more in awe of it every day. And while this (first?) visit to CERN seems like a pinnacle of experience, I can’t help but wonder what the next six years could bring.
Happy Higgs Day!
Note from 29 Oct 2018: this post was originally drafted in 2014 and fell by the wayside; recent developments in Tracker derivatives on Chrome have brought it back to the forefront of my mind. Tracker is the most powerful, GNU GPL licensed (free and open) video analysis software I have used. It is more powerful than some commercial software with video analysis. The killer feature for me is auto-tracking, which is implemented with some intelligence built in.
Updates to come soon on a browser-based derivative of Tracker that is being developed by Luca Demian as discussed here.
Good video analysis is one of the best things to happen to physics teaching, learning, and understanding, it makes it so much easier to really dig into how objects move and interact.
Things don’t always go perfectly smooth — the auto-fit for this data was completely wrong, so I estimated values for the coefficients and constant in the sinusoidal model and then tweaked them to achieve a good fit. In doing so, it helps reinforce what the different coefficients in the model stand for and do.
In this example, the physical setup is an eleven coil section of a Slinky, salvaged from one that had been hopelessly tangled in the way we all know happens all the time.
The thought from that old draft was never finished, and I may have lost my notes from that experiment — but I think the tool is important and worth sharing. If the notes show up…I’ll continue the thought. -JD 29 Oct 2018