Hi, Quentin, 

 The discuss of the use of integrating sensors into curriculum hit me where I live!  Please visit our video as we use existing commercial sensors yet know we should be stretching into your approach.  I agree that the type of project that build and individually engage students has tended to be set up into the afterschool program.  What strategies are you using to engage domain educators to add in projects?  Has the virus closure of buildings enabled an option to extend your support to those involved with the program?    

Betsy, We are building on our long-standing research+practice partnership with Denver Public Schools, who are partners in this project. Our strategy is based on a co-design model: a structured process whereby teachers, district leaders, and researchers work together to develop, test, and refine curriculum. 

 

Betsy, 

The district we are working in has an interest in incorporating CT not only into their MS science classrooms, but also in their STEM classes - which is a new elective course that they are bringing into all of the middle schools. So a challenge for our project is working with both types of teachers and classes. But we have found that the storylines work well in both settings, and the kids have responded very positively to using micro:bits & sensor technology to investigate a variety of topics.  In terms of COVID, we did run into buildings closing before teachers had fully carried out some of the units - although all of them did at least one unit this fall.

Thank you for this view of your COVID challenges.  As we loan out specific loggers aligned with senses to investigate classrooms (HVAC & lighting) we come at the learning from a different approach.  We are currently  in the process of integrating in bluetooth designs sensors that can offer immediate evidence of progress so all during is period, we are receiving data from Key West, Florida with pictures and map data.  This "southernmost" data builds engagement and challenges out to other locations.   While  we are more flexible, building into the classroom is only part of our goal.   We are encouraging collection of data to improve sustainable energy solutions.

It is interesting to read this. Just before schools closed in NY due to COVID-19, teachers in our Noyce MTF program had designed studies to research their classrooms using sensors. I sent out mainly temperature, light, and occupancy sensors. This was part of a module about the role of data in STEM. We wanted teachers to experience big and messy data sets and work through them to find meaning. Unfortunately, the sensors have been deployed but are locked up in schools right now. So many computational thinking and data science skills can be supported with sensors along with authentic study questions.

Michael,

 Glad to hear of your research with the temp, light and occupancy sensors.  Don't worry about the closures, if you preset they loggers they are ticking away and give you one "messy" piece to discuss!   Visit our video site and/or be in touch as I'd be glad to discuss more. 

Betsy Stefany 

Sharon, 

Thank you for taking interest in this project. One of our main goals is to support teachers in building their capacity to integrate Computational Thinking into mainstream science and STEM courses.  Central to how we approach this challenge is our model of professional development which includes collaborative design (co-design) (Voogt et al., 2015) of coherent storylines (Reiser, Novak, and Fumagalli, 2015; Reiser et. al., 2016) aligned with the NGSS and focused on intentionally integrating and engaging students in CT. 

 

To date we have developed 4 storylines that the participating teachers have implemented in their classrooms. All of these storylines investigate a scientific phenomenon that anchors the unit and elicits questions from students that can be investigated using CT and programmable sensors. The storylines all explicitly note which Disciplinary Core Ideas and Performance Expectation(s) are targeted, as well as the Science and Engineering Practices, Crosscutting Concepts, and Computational Thinking students are expected to engage in as they investigate their questions. At the moment, one of the storylines is available on our website and we expect to make the other 3 available this summer: https://www.colorado.edu/program/inquiryhub/curricula/stem-science-computational-thinking 

 

From the beginning of this project the question of how to define CT is something our research team has thought a lot about and we have had frequent discussions with our partner teachers around this topic. When we began the project we initially used the CT framework developed by Weintrop and colleagues (2016) to guide our work. Over the past year we have begun developing our own framework that denotes what each Science and Engineering Practices could look like when infused with CT.  This framework is still in process, but if anyone is interested in reviewing it more closely please let us know. We’d certainly appreciate feedback and insights from anyone who is interested.

 

Michael,

 

Thank you for your comment. Yes, we have found that middle school, especially middle school science is a great context for integrating CT. One of our goals has been to show that integrating CT would enhance students’ science learning and increase their engagement. We have some preliminary evidence from classroom observations and from teacher interviews that students are learning concepts more deeply compared to units without the CT integration. 

 

We are also using “practical measures” that are embedded within the curriculum such as student interest surveys (to help select anchoring phenomena for the storylines), an incremental model tracker (for students to construct and revise explanatory models about the phenomenon under investigation), student electronic exit tickets (surveys measuring their perceptions of coherence, contribution, and relevance), and 3D transfer tasks (to determine if students can apply their learning about the Disciplinary Core Ideas to a new context and related phenomenon). Although we are still analyzing this data, we have found the student models to be especially useful in gaining insights into what students are thinking and their evolving understanding of the concepts. 

 

A central aspect of this project is our partnership with the teachers and school district administrators. Over the past 3 years the teachers have provided significant input into the design and implementation of the curricula. Our team has also been developing a model of PD that provides support for both returning and new teachers that involves co-designing storylines, planning for implementation, onsite support during implementation, and reflection through viewing video and looking at student artifacts. 

 

We have a paper in press that we’d be happy to send you or any interested others that provides more info about our PD model and evidence of teachers’ growth in their abilities to use and support CT in their own classrooms.

Jonathan, 

To date we have developed 4 storylines that the participating teachers have implemented in their classrooms. All of these storylines investigate a scientific phenomenon that anchors the unit and elicits questions from students that can be investigated using CT and programmable sensors. One is focused on how sensor system work and serves as a sensor immersion experience for the students to begin to understand how sensors work and how they can be used to answer their questions about phenomena. The other storylines focus on organisms dependence on their environment (MS-LS2-1) using Mold Growth as the anchoring phenomena, magnetic fields (MS-PS2-3 & MS-PS2-5) using a Magnetic Levitation Train as the anchoring phenomena, and another focuses on Energy and Matter Cycling (MS-LS2-1 & MS-LS2-3) within a Worm Compost bin using urban farming and food deserts as the anchoring phenomenon.  At the moment, one of the storylines is available on our website and we expect to make the other 3 available this summer: https://www.colorado.edu/program/inquiryhub/curricula/stem-science-computational-thinking 

 

Students use CT throughout the units, even when they are not necessarily using the sensors (e.g. thinking about what tools to use, developing a plan, analyzing data). One of most challenging parts for us is highlighting that CT is happening (or supposed to be happening) during much of the storyline. To this end, we have been working on a framework that explicitly ties the SEPs to CT.

 

Thank you for these great questions, Rebecca. In terms of focusing on CT practices to support science practices vs science content learning, we’ve tried to be intentional with finding a balance between all the components. As you might expected, it is indeed quite challenging and we’d be very interested to learn how you and others are thinking about this topic.

Our co-designed storylines vary in terms of their emphasis on science practices and science content. Some storylines, such as the maglev storyline, more heavily emphasize using CT to support the science content. In that storyline the CT and sensors are utilized by the students more as tools in the service of content learning. 

 

However our sensor immersion storyline is much more focused on CT and science practices, since it is designed as an initial experience for students to investigate how programmable sensor systems work and how they can be used to answer questions about phenomena within their classrooms. 

 

As our project has progressed over the past several years we have tried to more intentionally identify the CT practices in our storylines. We would like to highlight CT as a way of thinking that goes beyond just using the sensor or tool (e.g. how can we frame our question so we can answer it using CT?) - and we want this focus to be visible for teachers so that they can thoughtfully promote CT in their classrooms. We are still trying to find the right balance and the right resources. Have you or others had success in this type of effort? 

 

As you pointed out, partnership work with district administrators and teachers is invaluable to these sorts of efforts and we are extremely grateful to build on an strong, established relationship between Denver Public Schools and CU Boulder. We have been gradually increasing the number of teachers we are working with. During the 2019-20 school year (our third year of the project) we have a group of 10 middle school science and STEM teachers who are part of the project. We anticipate adding up to 10 additional teachers in the coming year. The teachers meet with us regularly in the summer and throughout the school year and take part in a variety of activities including: co-designing the storylines from start to finish, collaboratively planning their implementation, reflecting on their experiences and revising the storylines. 

 

In the first year of the project we started with a static environmental sensor, but for the last 2 years we have shifted to the Micro:bit -  which is more affordable for schools and allows students to program the system to collect and display data according to their questions and goals.  Our project is conducted in partnership with Sparkfun Electronics, which recently created the Gator:bit that enables multiple sensors to be alligator clipped on to the Micro:bit, thereby expanding the available pins on the Micro:bit. Using the Micro:bit and Gator:bit together has helped us increase the options for what phenomenon students can investigate, how they undertake those investigations, and how they can communicate what they are finding. 

 

Dear Jennifer and Team,

Thanks so much for sharing these storylines. I'm quite impressed with how you are presenting the lessons -- it's a really attractive formatting.

What I do still have questions about, however, is are the CT practices. In the maglev storyline, I'm not seeing the CT practices listed (currently they are blank, which I'm assuming is because this is a draft in progress). I also realize that even Weintrop's definition of CT in science is quite broad, and isn't always explicitly pulled out. In the storyline, I see a very neat application of understandings about forces, magnetism, and electromagnetic interaction, but the only component I explicitly see that involves computational thinking involves possibly using the Micro:bit as a magnetometers, but just using the sensor does not necessarily mean it is using CT, right? In this case, how does this module distinguish itself from good physics/science teaching that does not use CT? I'm seeing systems thinking practices, but I'm not seeing the use of data or computation. (I guess what I mean is that unless you are interacting with data in that system in some way, or using a computational environment, I don't see how systems thinking is uniquely CT, as opposed to just scientific thinking).

As to finding the right balance, integration is NOT easy. One of my own projects, Computational Modeling in Physics First with Bootstrap, used Weintrop's definition of CT as a starting point (with an emphasis on computational modeling & simulation and computational problem solving practices -- with an emphasis on programming). We specifically decided to focus on those practices because we found that they were not present in most even exemplary physics teaching using Modeling Instruction, whereas other elements were far more frequently present (data practices and systems practices). You can see our actual student-facing curricular materials here, as well as the concept maps for each unit that lay out the physics and the computational modeling elements.

Looking deeper at the descriptions, the lesson implies that there is some programming involved in using the micro:bit...this is the info I'm curious about! I'm also curious to know the advantage of teaching the programming over just using something like a smartphone to make the magnetometer measurements. (Of course, I am not suggesting there's no value in programming, but I do ask, as a devil's advocate, why code? Physics teachers we have worked with want to have an explicit reason why they need to teach coding if it's a far less efficient way to just get the physics done and there are alternatives that distract less from the physics. In my own project, we've tried to integrate programming in such a way that it helps students understand the physics concepts they are learning (i.e., better understand acceleration by writing it in a differential form through code.) Part of the way we've also resolved this problem is to explicitly include programming/CT goals as part of the unit objectives. I'm curious to see what yours will look like when they are ready!

Rebecca,

 

Thank you for your thoughtful comments and questions. When we started the project we relied primarily on Weintrop’s framework for CT practices. We too were finding that it was very tool centric and from that lens our teachers were also having a challenging time identifying CT beyond when we used the sensors. We want to expand the way we are thinking about CT so that we can be more explicit about how CT is utilized throughout most of the units. 

 

We are currently working on creating a framework for connecting CT to all of the SEPs. We are working through what that would look like from a high level practice view as well as offer specific instantiations of what each SEP could like using CT tools such as sensors or some other tool (e.g. 3D printers). We are currently referring to these as “CT infused SEPs” and our hope is that such a framework can help us more towards encouraging and recognizing CT beyond when students use computational tools. 

 

Here is one example that we are working on that relates to the Maglev storyline. In part of the Maglev storyline students first explore magnetic fields using more traditional methods (e.g. iron filings) to create a visual model of magnetic fields. Students then use the micro:bit to make quantitative measurements of the magnetic fields. Finally students use all of the information they have collected to create a more accurate model to explain how the fields interact with each other. 

 

This is what a lesson level Performance Expectation with and without the CT infused SEP looks like. 

 

Without an emphasis on CT:

Lesson Level Performance Expectation: Students use mathematical and computational thinking to analyze and interpret data about magnetic fields to reveal patterns and construct scientific explanations that suggest relationships between distance/orientation of magnets and the strength of the magnetic field(s). 

 

With an emphasis on CT:

Lesson Level Performance Expectation infused with Computational Thinking: Students use computational thinking and methods (e.g. using programmable sensors and/or  computational data to create models and representations) to collect, analyze and interpret data about magnetic fields to reveal unseen patterns and create models (e.g. computational representations) that can be used to construct scientific explanations about the relationships between the distance/orientation of magnets and the strength of the magnetic field(s). 

 

We welcome any reactions you (or others) have about this approach to investigations of phenomena with an explicit focus on incorporating CT. 

 

As you can see, we are heavily focused on investigation, data collection, and analysis. This emphasis is primarily due to the fact that these practices are central to science and scientific investigation and furthermore, they require students to use what they discover to construct explanatory models. 

 

Thank you for playing devil’s advocate about programming! Raising the question helps us reflect more deeply on what we are doing and why. One of the main reasons that our storylines include programming is to ensure an explicit focus on integrating central components of CT (creating code, troubleshooting and debugging, etc). Also by having the students create and program their own sensor instruments they are provided with agency in deciding what data is to be collected, how it should be collected, and how to communicate or display it. 

 

In the first year of our project we utilized a static environmental data collection system that was not programmable and only allowed the students to collect and analyze data in a highly specified manner. When we switched to the Micro:bit/Gator:bit system we saw more engagement from the students, more student ownership of the data, and more complex explanations of the phenomena.

 

By using a storyline approach to science curriculum we have tried to be very intentional in ensuring ample opportunity for student autonomy. As part of this approach, lessons are driven by students’ ideas and questions. By giving students the opportunity to create and program the systems to determine what evidence to gather and how to communicate findings from the data, they are empowered to answer their own questions. We have found that programming serves as one of the central CT-integrated experiences that ultimately enables students to explain the phenomena in a way that makes sense to them and requires them to take full ownership of the investigative process. 

 

Lastly our research team has been interested in exploring programming as a form of modeling. Used in this way programming supports students to develop a deeper understanding of the scientific phenomena by collecting relevant data in a manner most likely to help them construct detailed explanations and models. This type of approach closely aligns to how modern scientific investigations are conducted now and in the future.  

 

Including CT specific goals in the unit objectives as you described sounds like a great way to forefront CT for the students and the teacher. We would be interested in what that looks like in your curriculum and in practice. As you can see, we are still grappling with these issues and how to make CT explicit in our curriculum. You have given us some great ideas to mull over and we should continue thinking about this as a community. We appreciate your continued dialogue on these topics!

 

Yes, we have found that both teachers and students are very excited about sensors!  We have taken the teachers on tours of Sparkfun Electronics, where they can see thousands of sensor components and also get inspiration from their many data display options. We encourage anyone in the Denver/Boulder area to get in touch with Sparkfun -- they are open for tours (well, maybe sometime in 2021!). For students, we co-designed the sensor immersion storyline that strives to take away the "black box” around sensor technology. Even before kids understand what sensors do, they get to see what the components look like - including Micro:bits, different sensors, wires, etc. Our goal is for them to want to ask questions and explore the technology, and eventually transfer their budding interest and skills to new contexts where sensors can be relevant and useful.

Michael, 

 

Thank you! The dialogue with this community has been wonderful and has been helpful for us to think more deeply about our project and CT within the broader community. The degree to which students have engaged in CT in our storylines and the complexity of the data they engage with has been evolving as our project has progressed. We are still trying to find what is an appropriate level for the students we serve. It is difficult as the experience of our students varies greatly across the schools and grade levels (in our case ranging from grades 5-8). 

 

We also are using a tiered approach by attempting to create a tiered scaffolding to support students as they program the sensors. Currently we have created three levels of scaffolded support and tutorials to help guide students during their investigations, mostly focused on programming but also on data analysis. The lowest level support provides minimal guidance for students that can work mostly independently. The mid-level support provides tips, hints, and prompts to guide students that need some help to make continued progress. The highest level scaffold includes not only tips, hints, and prompts, but also includes starting points and templates for students that need more help getting started. These resources were co-created with the teachers during the co-design of the units and are available for them to provide to their students as needed. However, the resources are still evolving as teachers field test them and consider what students need as they progress through the units over time. 

 

In regards to analyzing large sets of data, several of the storylines include resources such as video tutorials made by the teachers that support students in analyzing and representing data in ways that promote sensemaking. For example the tutorials guide students to organize and visualize their data so that they can make sense of what the data represents and what questions it helps to answer. We are just now field testing (or were before the disruptions of the pandemic) a new unit that involves students collecting large sets of long term data streams with multiple variables (e.g. temperature, humidity, soil moisture, CO2, and Volatile Organic Compounds). In this storyline students investigate the cycling of matter and energy in worm composting bins to determine the ideal conditions for creating rich compost soil. This unit has made clear the challenges of supporting teachers in helping students make sense of phenomena when data variability and uncertainty are factors. Hopefully this is an opportunity for productive student discourse as they engage in CT. Any insight in these areas from others engaged in similar work is definitely welcome!

Sabrina, 

Thank you! Currently the teachers we work with have taught our co-designed CT integrated storylines in graded 5-8. Teachers have reported increased student engagement during implementation of these storylines. It would be interesting to see how these approaches would translate to younger grade levels and what adaptations would be required.   

Thank you Michael!  We were fortunate to be awarded funding recently from the James S. McDonnell foundation to continue this line of work. Over the next year we are planning to conduct more data analysis and finalize resources to share with the broader community.