Hi Aman, thanks for your questions! After defining the engineering problem and doing conceptual modeling of the science phenomenon, they did an unplugged CT activity to gain familiarity with the central CT concepts needed for the computational model (e.g., variables, expressions, conditionals) in a non-scientific context. Then the computational model development is heavily scaffolded--we decomposed this into 3 separate lessons based on the different logical conditions that structure the runoff model (is rainfall equal to, less than, or greater than the absorption limit of the surface material). They develop and test each part of the model based on test cases they identify based on the conceptual model. Let us know if this makes sense--my teammates can also follow up on this.

The engineering project is basically the same for different students/classrooms, but there was some flexibility we were able to introduce across districts. For instance, the project is actually build around the needs of one of our participating districts, so those students were working on a problem they actually experienced in their own school. Also we left it up to individual teachers to decide to what extent to set the solution criteria for students, or for students to come up with their own criteria, so this enabled a certain degree of personalization. Our hope in the future is that the curriculum could be customized so that all students could be doing it in the context of their own school. Completely agree with you that giving students agency over the engineering problem is highly desirable.

Thanks Aman for the questions! I wanted to follow-up on Kevin's response about how we scaffolded the computational modeling or 'learning to program' aspect for teachers. We have also seem some hesitation among elementary science teachers with respect to leading the parts of the curriculum unit tied to computational modeling and some have expressed interest in offloading that part of the instruction to the project team. We have tried to address this by developing various teacher supports including a teacher guide, annotated lesson plans, slides that teachers can use in class, formative assessments, and educative videos. The videos are primarily focused on the programming aspect and walk teachers through the steps in the computational modeling environment. Of course, during PD, we spend a big chunk of time on these computational modeling lessons as well. We have teachers go through the programming activity as if they are students, and we even have debugging exercises for teachers. With these resources, we have been successful in having elementary teachers lead instruction for all the SPICE lessons, including the ones that involve programming. 

Hi Kevin and Satabdi,

Thank you for responding to my question. Glad to see you are providing scaffolds for teachers to bring this to their classrooms and are thinking about teacher/student agency to adapt the tools for their context. 

Thanks Sarah! Yes, like I wrote in my response to Aman, we have definitely seen some initial hesitation among elementary science teachers with respect to leading the parts of the curriculum unit tied to computational modeling. We have tried to support teachers both through modifications to the curriculum unit and through the design of teacher supports. Over the years, we have iteratively simplified the computational model of water runoff to make it easier on both students and teachers. We have also structured the activity to enable incremental development and iterative testing. In terms of teacher supports, we have developed a teacher guide, annotated lesson plans, slides that teachers can use in class, formative assessments, and educative videos. The videos are primarily focused on the programming aspect and walk teachers through the steps in the computational modeling environment. Of course, during PD, we spend a big chunk of time on these computational modeling lessons as well. We have teachers go through the programming activity as if they are students, and we even have debugging exercises for teachers. We've shown teachers examples of common struggles students in previous studies had, and have even prepared a document with examples of common student challenges and how teachers can respond in such situations.  During PD, just like students, we've seen varied reactions among teachers. Some go through challenges similar to students while others need less support. 

Yes, thanks Sarah! Just to add to Satabdi's response, feedback from the teachers working through the computational modeling in PD has been invaluable to refine and revise the PD experiences to emphasize just what you brought up - that teachers being open and honest about their own difficulties trying to make the jump from conceptual to computational models can help students feel more confident and engaged during that transition. We have also seen in case studies of implementation that teachers provided more sensemaking supports alongside pragmatic supports to help students engage in computational modeling - that is, teachers were constantly providing reminders to students about what the variables, operators, etc. meant in terms of the scientific concepts and interactions in their conceptual models. Both the teachers and students have noted in interviews how this kind of synergistic learning is very different than learning science or CS/CT by itself. Students in particular have noted that it is very different and more meaningful than the hour of code or other CS activities they had prior to this point with similar block-based programming environments.

Thanks! It is great to hear that learning science and CS/CT in an integrated instead of isolated way makes sense for students as well! Projects like this one will open up ways to teach more how disciplines relate! I am sure this will make the relevance of different disciplines or skills more obvious to young students.

That's amazing, I didn't know Snap! is in WISE, what a great advance. Satabdi and I described some of our work with teacher in our response to Aman above. We build a layer on top of Snap! (domain specific modeling language) with custom blocks representing key science concepts which makes model building more tractable. Does WISE do anything like that or is it Snap! out of the box? I would love to see this in action, is there a project I could preview?

Hi Nathan,

We've learnt a lot during every iteration of this project and have refined our curriculum unit considerably since when we started. I'll try to list a few things we learnt. It's very important to be intentional about the science and engineering learning goals you are targeting, and what intersections they have with computational modeling. Also, students should feel that computational modeling is consequential to their problem-solving instead of being just another thing they are learning and doing because their teacher is asking them to. Consistency in notations, variable names and representations across science, engineering and computational modeling is also critical. Another thing we learnt is the importance of keeping the computational modeling age appropriate and simple enough in the service of science and engineering learning. We simplified our model considerably over different iterations of our curriculum and built in various curricular scaffolds to help both students and teachers. Finally, I can't overemphasize the need for robust teacher supports in order to make implementation of such integrated curricula a reality in elementary classrooms. 

Hi Kevin and Satabdi, Thanks to both of you for your responses and congrats on this project. You both report learning lessons similar what we have learned with the Precipitating Change project, which is also a STEM+C project. Students learn CT skills and scientifically-based prediction of the weather through embodied activities, models, and other activities such as rule refinement. We also found we had to really hone the list of CT skills and provide a lot of support for teachers. And yes, there is a big tension between teaching the science and the CT skills. Ultimately, I'm beginning to think that having a better understanding of the CT skills will be a good precursor to learning science more easily particularly the emphasis on pattern recognition. In the final years of the project, and with our work in Alaska, this project has became more and more focused on designing a culturally appropriate curriculum for indigenous students and figuring out how best to assess the CT learning for different cultures.

Thanks for watching our video, Valerie! The project aligns with NGSS performance expectations for 5th grade in earth science and engineering. We have also had 6th graders use it. There are no immediate plans to expand to other grade levels, but given the opportunity we would be excited to do so.

Thank-you Laura!

Thanks David! We are nearing the end of our project, so we are no longer actively recruiting. But, we would love for these tools and resources to be used by educators even after the project funding period is over. We'd encourage interested educators to contact us via our website: https://spiceprojects.org/contact/ and we'll make sure that they have the student and teacher facing resources needed to implement this curriculum unit. We are also looking into making all the resources available online on our website. 

Thanks Chad! We'd have to collect more data to speak to the impact of personal relevance. The project is actually built around the needs of one of our participating school districts, so those students were working on a problem they actually experienced in their own school. We superimposed a grid structure on a satellite image from their schoolgrounds and the surrounding areas. In other districts, teachers referred to flooding incidents that students were aware of (either in their own school or from the news) but framed the problem as one that students were solving for a different school. Till date, we've run studies in Virginia, California and Tennessee, and we haven't noticed any significant difference in student engagement on account of the runoff context. But, it would be interesting to try this with students in an area with very little rainfall and see if they'd relate to the problem similarly.

Thanks Aditi! We framed the engineering design problem as one where students' design had to satisfy schoolyard utility requirements (for example, at least x amount of space needed for a parking lot, at least y amount of space needed for a playground, etc.) and had to simultaneously optimize runoff, cost, and wheelchair accessibility. Students used the computational models for water runoff that they constructed to test their engineering designs. Under the hood, we helped with this process by transforming students' runoff model for an unit area to one for the entire schoolyard and by allowing students to observe the runoff, cost and accessibility of their design as they run the simulation. This feedback was also stored via a simulation history functionality we provided where students could revisit the designs they had tried out and their associated outcome values.

Having said that, I'll mention that our model ignored/abstracted the issue of layout of different materials in the schoolyard and instead performed calculations based on what percentage of the entire schoolyard was created with what kind of material. Students often pointed that out - the fact that some of the designs which seemed more optimal as per the outputs of the computational model are not the best designs in the "real world".