I am very interested in staying connected to your project and eventually attending training. Regretfully, my summer 2018 is already full. However, I will be an on-site coordinator for a Bootstrap in Algebra session in San Antonio in June. Now that I see there are ways to incorporate Bootstrap in physics, I'm jazzed as I teach physics to HS freshman. I'll be sure to bookmark the resources you have provided and perhaps attend training in 2019 if offered.

Dear Joules,

Thank you for the message. Regretfully, our summer workshop is full as well. :-)

But, we will be hosting a workshop in 2019. Keep your eyes open at this link: http://aapt.org/K12/Computational-Modeling-in-P... (If you complete the application now -- just leave things like essay responses blank -- it will be a reminder to me to reach out to you once the applications open again).

Your perspective as someone who is proficient in Bootstrap:Algebra would make you an exceptionally good participant. We are actually hoping to expand some of our work into measuring the impacts of this work on mathematical thinking as well.

Sincerely,

Rebecca

Scot,

Thanks for the great questions! Here are some answers as best I can provide them right now:

1) Thus far, we've done two years of curriculum development workshops that have consisted of experienced Modeling Instruction teachers of physics. Only a handful of these people, however, had any computational modeling or programming experience, and, at first glance, we actually don't see any correlation between prior experience and ability to engage with Pyret as computational modeling as a representative tool in teaching and learning physics. This summer, we're doing training workshops with physics teachers who are a combination of Modeling Instruction teachers (experienced and novice) and those who have no experience in Modeling Instruction. I'd argue that Modeling teachers in general have lots of computational thinking skills, but so do nearly all well-prepared teachers of physics! And, if teachers are struggling with content, I actually think this is a great way to learn. :-) Thus far, what we've found is that teachers who are confident in using computational modeling can do INCREDIBLE things with freshmen (these are regular kids -- high and low-achieving and everything in-between), because they don't know physics any other way. If the kids can do this, I highly anticipate it won't be a problem for these new teachers this summer.

2) We are awaiting our first batch of pre/post assessments from students this year on an Integrated Computational Thinking in Physics Survey, as well as a Computational Thinking Attitudes Survey. (Quantitatively, we don't have data yet). Qualitatively, however, we've seen high levels of engagement in multiple Physics First classrooms that our team has observed, across the spectrum. I would argue, however, that Modeling Instruction even without computational modeling isn't quite a "traditional classroom," so there's already a typically huge boost to student performance on diagnostic measures (of content) just because they have a Modeling Instructor. We are also collecting data on student growth with the Force Concept Inventory, and will be able to compare it to a national sampling of non-Modeling teachers.

3) Can you identify the specific pieces in the video you are talking about? There are a lot of snippets going on, many disjointed from one another, but I'd be happy to explain if you provide specific examples. 

Jessica,

Actually, I'd say that this approach drives Modeling Instruction (both with and without the computer), but the computer is unique in that there is zero error in interpretation. Modeling Instruction is heavily dependent upon student discourse and consensus-building (typically using whiteboards), so students teaching other students and explaining their thinking is pretty standard. What is unique about bringing the computer into this is that the speaker and listener can't share common misconceptions, as is often the case in student-student discourse. 

"Teaching the computer" is really just another metaphor for "telling the computer what to do," in this case, which can be as straight-forward as creating a function to cause the image of a car move across the screen one way or another. However, in this process, students must be very explicit about all parts of the function --> (1) What are the initial conditions? (Humans often assume this to be zero). (2) How is velocity represented in the function? (Representing this in code doesn't allow students to use sloppy language without understanding expressly that this means a differential change for each computer tick OR a rate as a function of time). What is really nice about the computer, however, is that code can be evaluated for their elegance and their ability to be modified to fit various scenarios. This isn't something we frequently do or think about with standard physics.

Perhaps another layer is that the computer asks "perfect questions." Pyret does an amazing job at providing "feedback" (error messages). Students use these to correct their thinking and respond to the computer to re-teach in a clarified way.

We're entirely aiming for integration --> one of the things we've actually been monitoring and will use a formal rubric to evaluate the curricular materials is where computing comes in: (1) model development and/or (2) model deployment. We're aiming to hit both, but, at least anecdotally, my sense is that many teachers with prior computing experience actually want it to be more of (2). It takes a totally new way of thinking about physics and computer science education to integrate it into (1).

So, as a concrete example, the first unit in Physics First is qualitative energy, where students create series of bar charts to represent changes in systems. This becomes tedious, and makes the case for wanting a computer for efficiency's sake. In the second unit, we look at the value of using discrete changes in position as a foundational way for doing a "tumble buggy" lab to develop ideas of constant velocity that don't bludgeon to death students' natural tendencies to think about "step by step" and push them straight toward "fit a best-fine line to your data and get a slope," which is actually what we (firstly meaning "I" has a recent practitioner) would often do to my students. Doing that places kids in something of a mental box -- my mental box. We're also integrating it into constant acceleration -- suddenly, kids are writing functions for acceleration by looking at x + dx and v + dv, rather than needing to understand a jumble of algebraic equations (i.e., xf = 0.5 a t^2 + vi*t + xi, or vf^2 = sqrt(2*a*d + vi^2)) that makes little sense to a novice learner of physics who is struggling with algebra, too.

So, we're teaching these together. Computing simply becomes another to way represent and build understanding about what motion MEANS...not just to "apply understandings of motion to make a computer simulation."

David,

Thanks for the feedback. The purpose of this project (and by proxy, the purpose of the video), is to investigate how two evidence-based instructional methods in physics and computing can enhance the teaching and learning of both disciplinary areas. There are a lot of layers here, so here are some of the bits and pieces:

1) The computing: The power is in the Design Recipe pedagogical strategy, not the language alone. However, we are using Pyret (https://www.pyret.org/), which was developed specifically with the early learner in mind. The pedagogical strategy is somewhat founded on How to Design Programs (http://www.htdp.org/), out of which the Bootstrap:Algebra curriculum evolved (http://www.bootstrapworld.org/). Our goal was not to teach a language, per se, but to teach CT skills that allow participants to think differently about how they are representing physical systems (most notably, as in thinking about motion as a smooth motion -- which is emphasized by algebraic equations, or as a differential motion -- which is emphasized by computational representations). There are very distinct advantages to both.

2) The Modeling Instruction in physics approach: You can learn more about it here -- it has been funded by 7 different NSF grants since the mid 1990's...https://modelinginstruction.org/ There is a robust section on publications on the method. Part of the struggle we have with a 3-minute video is that the instructional approach, which emphasizes "big models" as physics content, "model development and deployment" as a skill, and paradigm lab experiences and student discourse as the primary medium to building learning, takes teachers about 2-3 weeks of professional development to really hone in on using it.

So, why in the world are we doing this? Check out this simple white paper from the EARLY part of our project (caveat: it's not all-inclusive!) http://aapt.org/K12/upload/Why-CM-in-Physics.pdf