1038 Views
  1. Quentin Biddy
  2. https://www.colorado.edu/program/inquiryhub/quentin-biddy
  3. Research Associate
  4. Presenter’s NSFRESOURCECENTERS
  5. University of Colorado Boulder, Utah State University, Denver Public Schools
  1. Jennifer Jacobs
  2. https://www.colorado.edu/ics/jennifer-jacobs
  3. Associate Research Professor
  4. Presenter’s NSFRESOURCECENTERS
  5. University of Colorado Boulder
  1. Mimi Recker
  2. Professor
  3. Presenter’s NSFRESOURCECENTERS
  4. Utah State University
  1. Tamara Sumner
  2. https://www.colorado.edu/ics/tamara-sumner
  3. Professor
  4. Presenter’s NSFRESOURCECENTERS
  5. University of Colorado Boulder

Collaborative Research: SchoolWide Labs: A real-time sensing platform for int...

NSF Awards: 1742053, 1742046

2020 (see original presentation & discussion)

Grades 6-8

SchoolWide Labs is a research and practice partnership project focused on integrating computational thinking into middle school science and STEM classrooms in a way that complements and enhances students’ science learning. Participating teachers in the partner school district participate in ongoing professional development in which they learn to use sensor technology and NGSS-aligned science instruction to promote their students’ computational thinking. One important aspect of this work includes working collaboratively with the teachers to co-design curricular units or “storylines” that they then implement in their classes. As part of the professional development model, teachers play a central role in designing, planning, implementing and reflecting on their instruction and their students’ learning, particularly as it relates to the integration of computational thinking and scientific concepts and practices.  

This video introduces the SchoolWide Labs project, which is a partnership between the University of Colorado Boulder, Denver Public Schools, Utah State University, and Sparkfun Electronics. The video highlights the project’s goals from the perspectives of all of the partner organizations, including several of the participating teachers. Viewers will see clips from the professional development workshops and classroom lessons, providing a glimpse into the types of work that both the teachers and students engage in as they become increasingly adept at CT integration.



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Original Discussion from the 2020 STEM For All Video Showcase
  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 4, 2020 | 03:07 p.m.

    We’re so glad you stopped by to view our video on the SchoolWide Labs research project!  This project is a collaboration between the University of Colorado Boulder, Denver Public Schools, Utah State University, and SparkFun Electronics. Our goal is to support the integration of computational thinking into middle school science classrooms, particularly through the use of sensor technology. A list of linked publications and presentations from our project can be found using this link. We welcome all questions and comments, and are especially interested in your thoughts about using sensors, designing curriculum, supporting teachers’ professional learning, and promoting computationally rich classroom discourse.

  • May 5, 2020 | 01:32 p.m.

    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?    

  • Icon for: Tamara Sumner

    Tamara Sumner

    Co-Presenter
    May 5, 2020 | 02:43 p.m.

    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. 

     
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    Quentin Biddy
  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 5, 2020 | 02:54 p.m.

     

    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.

  • May 6, 2020 | 10:20 a.m.

    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.

  • Icon for: Michael Daley

    Michael Daley

    Higher Ed Faculty
    May 6, 2020 | 10:29 a.m.

    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.

  • May 6, 2020 | 11:35 a.m.

    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 

  • Icon for: Sharon Lynch

    Sharon Lynch

    Professor
    May 5, 2020 | 11:23 a.m.

    Hi Quentin, 

    How is this project related to the NGSS? I searched on projects with that key word, and yours came up. 

    It would also be helpful to define computational thinking. 

    I also wanted to say that I like that student were putting hardware together and somehow using it to collect data, but I am not collecting the dots, here. Would appreciate more help in understanding this project. Thank you.

     

  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 5, 2020 | 02:39 p.m.

    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.

     

  • Icon for: Michael Haney

    Michael Haney

    Facilitator
    May 5, 2020 | 11:29 a.m.

    Middle school seems like exactly the right level to target, a chance to reorient students to participate in science processes, especially when it involves things they care about in their own community.  The stated emphasis on computational thinking is very encouraging and the final description of students asking deep questions, using modern equipment and caring about the answers (which necessary involves collection, representation and analysis of data) is very good.  

    I assume this project is in an early stage and that is why there are less specific examples included.  Are you hoping for specific and attributable improvements in the student performance on the Next Generation Science Standards?  What assessment will you use to measure success in learning?  How do you anticipate these activities and techniques being embedded in the curriculum?  Will the teachers make those decisions or will they have some training to help them guide students in raising questions to investigate?

     
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    Quentin Biddy
  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 5, 2020 | 02:39 p.m.

    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.

     
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    Michael Haney
  • Icon for: Jonathan Margolin

    Jonathan Margolin

    Facilitator
    May 5, 2020 | 01:25 p.m.

    Hi Quentin, I can tell that your video has already sparked a lot of interest. What topics (i.e., disciplinary core ideas) in science are you focusing on for the integration of computer science? Have you encountered any challenges in making the connection in a way that is authentic--in other words, so that the computer science is directly relevant to the STEM topic? Has this been challenging?

  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 5, 2020 | 02:51 p.m.

    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.

     

  • Small default profile

    Tammy Sumner

    Higher Ed Faculty
    May 5, 2020 | 02:36 p.m.

    Thanks everyone for your interest in our research. I will kick off our conversation until Quentin returns. We used Weintrop's definition of computational thinking in this project as this definition is focused on computational thinking for science.

    With respect to the NGSS, the entire effort is focused on phenomena-based instruction and 3 dimensional learning. Identifying suitable phenomena that are rich in science, place-based, grade-level appropriate, and afford meaningful use of the sensors is quite tricky. We have written a few papers about our experiences navigating these design constraints, and how we have extended the storylining technique to develop NGSS curricula that are enhanced with computational thinking. This 2018 SIGCSE article is a good place to start: https://dl.acm.org/doi/abs/10.1145/3287324.3287476

     
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    Quentin Biddy
  • Icon for: Sharon Lynch

    Sharon Lynch

    Professor
    May 5, 2020 | 04:34 p.m.

    Thanks. Very helpful.

     
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    Quentin Biddy
  • Icon for: Rebecca Vieyra

    Rebecca Vieyra

    Facilitator
    May 5, 2020 | 08:32 p.m.

    Dear Quentin and Team,

    This is a really exciting project, and it seems like you are doing very well to carefully interweave the elements of the NGSS, CT for science, and technology.

    As a prior physics teacher who works on an integrated computational modeling in physics projects, I'm curious to know more about some specific examples of how you integrate CT "in a way that complements and enhances students’ science learning." I'd love to have an illustration of an example storyline or activity. What I'm most interested in is if you are focused more on the CT practices to support science practices, and/or if you think CT practices support science content learning, because the latter is always a bit harder, in my opinion.

    I also want to congratulate you on the practitioner partnership piece. This is critical -- teachers are our best resources for seeding innovative approaches. I am curious, however, how you streamlined this experience. In my own project, it took us about 2 years to get the storylines more or less straight between the science storyline and the computational modeling storyline, even when building off of existing frameworks (Modeling Instruction). How many teacher developers did you work with, and what was the nature of their involvement?

    Lastly, I am curious to know more about the role of sensors, specifically, and how the tools you are using might have affordances over other commercially available probeware, for example.

    Sincerely,

    Rebecca

  • Icon for: Jennifer Jacobs

    Jennifer Jacobs

    Co-Presenter
    May 6, 2020 | 03:06 p.m.

    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. 

     

  • Icon for: Rebecca Vieyra

    Rebecca Vieyra

    Facilitator
    May 7, 2020 | 06:45 a.m.

    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!

  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 7, 2020 | 07:20 p.m.

    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!

     

  • Icon for: Michael Daley

    Michael Daley

    Higher Ed Faculty
    May 6, 2020 | 09:26 a.m.

    Great project! I recently took a group of Noyce fellows to visit labs in our Earth & Environmental Science project. What struck teachers was how much effort is put into instrumentation and the design of sensors. Many months of work go into developing instrumentation for a field campaign that might only last a few weeks. Your project brings students closer to participating in how science is done. I look forward to seeing your storylines this summer.

  • Icon for: Jennifer Jacobs

    Jennifer Jacobs

    Co-Presenter
    May 6, 2020 | 03:15 p.m.

    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.

  • Icon for: Michael Haney

    Michael Haney

    Facilitator
    May 7, 2020 | 02:16 p.m.

    Thanks for the team's thorough attention to responding to the questions raises.  And congratulations on having a number of publications and presentations related directly to this project.  The project is exciting and I am sure involving for staff and students.  I went back and read the NSF summary, which is quite detailed, trying to find out a bit more about what middle school students would be expected to do with data representation and analysis.  Many years ago I was the lead in designing a STEM curriculum for a local high school with very motivated students.  One of the key features of the curriculum was the emphasis on computational thinking and we took a tiered approach and helped students develop this perspective over 4 years.  But once questions are asked that require larger data sets to answer, many more factors come into play.  Even at the middle school level, it seems students could begin to find ways to describe the variability within data sets and be able to state results in terms of some measure of uncertainty.  Maybe technology makes this easier, or maybe just more abstract.  Do you have any thoughts on the appropriateness of this for middle school?

  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 7, 2020 | 07:28 p.m.

    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!

  • Icon for: Sabrina Stanley

    Sabrina Stanley

    Graduate Student
    May 7, 2020 | 07:40 p.m.

    Brilliant program! Your video does a great job of showing students in the work of learning. Does this idea translate well through the various grades? 

  • Icon for: Quentin Biddy

    Quentin Biddy

    Lead Presenter
    Research Associate
    May 7, 2020 | 07:51 p.m.

    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.   

  • Icon for: Michael Haney

    Michael Haney

    Facilitator
    May 7, 2020 | 08:00 p.m.

    Thanks for the detailed reply.  It really seems like you have thought through how to scaffold learning data representation and analysis, which are critical components of modeling.  (It makes me wish I were back in the classroom.). It’s a great project that I hope scales beyond the project funding.   I wish I had some wisdom to impart about our approach but it was a 7 year development effort begun in 1985.  The school still functions, but I fear many of the underlying principles on which it was built have faded and done on the innovations hav atrophied over the years since I left.  It’s a real loss that we didn’t formalize and share much about the design, the process or the outcomes, such are the constraints of working in public education without much buy in from higher education.  Anyway, congratulations on all you are accomplishing.  

  • Icon for: Jennifer Jacobs

    Jennifer Jacobs

    Co-Presenter
    May 8, 2020 | 04:04 p.m.

    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. 



  • Icon for: Karl Kosko

    Karl Kosko

    Higher Ed Faculty
    May 8, 2020 | 02:35 p.m.

    I'm happy to see this kind of work being done! Can you say something about how expensive these types of materials are for schools (as well as how long-lasting)?

  • Icon for: Jennifer Jacobs

    Jennifer Jacobs

    Co-Presenter
    May 8, 2020 | 04:22 p.m.

    Thank you for your comment, Karl. We have elected to use micro:bits because they are relatively inexpensive, are easy to use in a classroom setting, and allow students to engage in basic programming. They are available from Sparkfun Electronics for under $20. Recently we have also been purchasing their gator:bit science kits, which are more expensive at around $90, but allow for a large variety of sensors to be used in combination with the micro:bit. 

     

    We generally purchase a few micro:bits and gator:bits for each teacher, assuming their students will be working together in small groups and that the equipment can be shared by multiple classes. We have found the micro:bits and gator:bits to be quite robust and sturdy - we have not yet dipped into our “replacement parts” budget as no sensor equipment has broken over the past 2 years.

     

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