Category Archives: Engineering

Supporting Computationally Rich Communication During Remote Learning: Lessons Learned

January 14, 2022Computer Science, EducatorCIRCLS, Engineering, Guest Author, STEMPati Ruiz

By Colin Hennessy Elliott & the SchoolWide Labs Team

This post was written by a member of the SchoolWide Labs research team, about their experience during the pandemic and what they learned from middle school science and STEM teachers as part of a larger Research-Practice Partnership between a university and a large school district in the United States. The post was reviewed by practicing Educator CIRCLS members. The purpose of the blog is to help open the door between the worlds of research and practice a bit wider so that we can see the differing perspectives and start a dialogue. We are always looking for more practitioners and researchers who want to join us in this work.

The COVID-19 pandemic pushed many school communities online last school year in the US. Teachers were charged with accommodating so many needs while holding levels of care and compassion for students and their families. As a multi-year research project aimed at supporting teachers in integrating computational thinking in science and STEM learning, we worked with renewed senses of compassion, creativity, and struggle. We witnessed how students and teachers innovatively developed computationally rich communication using the technologies from our project while teaching and learning remotely. Below we share a few moments from the 2020-21 school year that have helped us learn what it takes to engage middle school students in computational practices (i.e. collaborating on programming a physical system, interpreting data from a sensor) that are personally relevant and community-based. These moments offer lessons on how collaboration and communication are key to learning, regardless of whether the learning takes place in person or remotely.

Who we are

The SchoolWide Labs research team, housed at the University of Colorado Boulder with collaborators at Utah State University, has partnered with Denver Public Schools (DPS) for over five years. We work with middle school science and STEM teachers to co-develop models for teacher learning that support the integration of computational thinking into science and STEM classrooms. The team selected, assembled, and refined a programmable sensor technology with input from teachers on what would be feasible in their classrooms and in collaboration with a local electronics retailer (SparkFun Electronics). This collaboration focused particularly on programmable sensors because they offer opportunities for students to develop deeper relationships with scientific data, as producers rather than just data collectors.¹ This aligns with modern scientific practice where scientists often tinker with computational tools to produce the data they need to answer specific questions.

The Data Sensor Hub (DaSH) is a low cost physical computing system used in the curriculum and professional learning workshops developed by the SchoolWide Labs team. Ensuring the DaSH would be low cost was a priority of the team as an issue of access and equity. The DaSH consists of the BBC Micro:bit, a connection expander called the gator:bit, and an array of sensors that can be attached to the micro:bit and gator:bit with alligator clips (see Figure 1). Students can easily assemble the DaSH themselves to experience the physical connections and hard wiring. Students and teachers can write programs for the DaSH using MakeCode, a block-based programming environment that can be accessed via a web browser, making it easy to use with various computer setups. For students with more programming experience, MakeCode has the option to use python or javascript to program the micro:bits.

Figure 1.The Data Sensor Hub (DaSH). The picture on the left depicts the components of the DaSH used with the Sensor Immersion Unit including the micro:bit, Gator:bit and three sensors (top to bottom: soil moisture sensor, microphone sensor, environmental sensor). The picture on the right shows a teacher and student interacting with the DaSH set up just for the microphone sensor.

Before the COVID-19 pandemic, our research team co-designed curricular units with teachers interested in using the DaSH to engage middle school students in scientific inquiry. Currently there are four units available on our website, three that use the DaSH and one that uses a 3-D printer. The Sensor Immersion Unit – the only unit teachers implemented remotely in the 2020-21 school year – has students explore the DaSH in use via a classroom data display, learn basic programming, and create their own displays that collect environmental data (sound, temperature, carbon dioxide levels, or soil moisture) to address a question of their choice. For example, one group of students decided to investigate climate change by measuring atmospheric carbon dioxide levels in their neighborhoods and exploring the impact of plants and trees. The goal is for students to develop ownership of the DaSH as a data collection tool by wiring the hardware and programming the software. In the process, they engage in computational thinking and computationally rich communication when they discuss their use of the DaSH with peers and the teacher.

In the 2020-21 school year most middle schools in Denver Public Schools were remote. Several STEM teachers, with more curricular flexibility, decided to provide DaSHs to students who wanted the responsibility of having them for a period of time. Having the DaSHs in students’ homes offered opportunities to make the barriers between home and school less visible, as students conducted place-based investigations and emergently took on the role of data producers. For example, some students shared temperature data and carbon dioxide levels in and around their homes with the class. In these moments, students emergently took on the role of data producers. Below, we share two examples from observing student and teacher interactions in virtual mediums which helped our research team learn about what is possible using the DaSH. We also developed new supports to help teachers facilitate extended student collaboration and communication when using the DaSH.

Lesson Learned 1: Increasing student collaboration in virtual settings

One middle school STEM teacher, Lauren (a pseudonym), had the opportunity to teach different cohorts of eighth graders in the first two quarters of the 2020-21 school year. A new SchoolWide Labs participant, she was enthusiastic about implementing the Sensor Immersion Unit with her first cohort in the first quarter. She navigated the logistical challenges of getting DaSHs to over half her students along with the pedagogical challenges of adapting the curriculum to a remote setting. After her first implementation, she shared that she was disappointed that her students rarely collaborated or shared their thinking with each other when they were online. We heard from other teachers that they had similar struggles. Before Lauren’s second implementation, we facilitated several professional learning sessions with the aim of supporting teachers to elicit more student collaboration in remote settings. Through our work together, we identified the importance of establishing collaboration norms for students, offering continued opportunities to meet in small groups virtually, and modeling how to make their work visible to each other. In Lauren’s second implementation with new students during next quarter, she intentionally discussed norms and roles for group work in “breakout rooms,” or separate video calls for each group (her school was not using a software that had the breakout room functionality). One of the resulting virtual rooms with three eighth graders during the Sensor Immersion Unit was especially encouraging for both Lauren and our research team. Without their cameras on at any point, the three boys shared their screens (swapping depending on who needed help or wanted to show the others) and coordinated their developing programs (on different screens) in relation to the DaSHs that two students had at home. Their collaboration included checking in to make sure everyone was ready to move on (“Everyone ok?”) and the opportunity to ask for further explanation from others at any point (“hold on, why does my [DaSH]…”). With their visual joint attention on the shared screen, the three successfully navigated an early program challenge using their understanding of the programming environment (MakeCode) and hints embedded in it (developed by the research team).

Lesson Learned 2: Adapting debugging practices to a virtual environment

Many science and STEM teachers new to our projects have struggled with finding confidence in supporting students as they learn to program the DaSH. They specifically worry about knowing how to support students as they debug the systems, which includes finding and resolving potential and existing issues in computer code, hardware (including wiring), or their communication. This worry was further magnified when learning had to happen remotely, even with some students having the physical DaSH systems at home. Common issues teachers encountered in student’s setup were consistent with the bugs that we have identified over the course of the project including: 1) code and wiring do not correspond, 2) problems in the students’ code, 3) the program is not properly downloaded onto the micro:bit, and more.²

Being unable to easily see students’ physical equipment and provide hands-on support made some teachers wary of even attempting to use the SchoolWide Labs curriculum in a remote environment. However, those teachers who were willing to do so made intriguing adaptations to how they supported students in identifying and addressing bugs. These adaptations included: 1) meeting one on one briefly to ask students questions about their progress and asking them to hold up their systems to the camera for a hardware check, 2) holding debugging-specific office hour times during and outside of class time, and 3) having students send their code to teachers to review as formative assessments and debugging checks. Although debugging required more time, patience, and creativity from teachers and students, these activities were generally successful in making the DaSHs work and helping students become more adept users.

Future Directions

As teachers and students have gone back to attending school face-to-face, the lessons learned during remote instruction continue to inform our work and inspire us as a SchoolWide Labs community. As these two examples show, the ingenuity of teachers and students during a tough shift to virtual learning led to new forms of computational thinking, communication, and collaboration. It became more clear that there was a critical need for at least some students to have the physical systems at home to deeply engage in the process of being data producers, which is indicative of the curriculum being so material rich. Yet, simply having the the DaSH in hand did not ensure that students would participate in the kinds of communication important for their engagement in and learning of the targeted science and computational thinking practices. While we continue to explore the complexity of teacher and student interactions with the DaSH, this past summer and fall we have been working with participating teachers (new and returning from last year) to develop a more specific set of norms, which include small group communication norms and roles. Additionally, we have begun to consider other strategies that may support students to learn programming and debugging skills, such as the use of student-created flowcharts to represent their view of the computational decisions of the DaSH. The shift to virtual learning, and now back to face-to-face instruction, has required us to more deeply reflect on the professional learning that best supports teachers in using the DaSH and accompanying curriculum in a variety of instructional settings, with both anticipated and unanticipated constraints. We welcome the opportunity to continue learning with and from our colleagues who are similarly engaged in this type of highly challenging but extremely rewarding endeavor to promote computationally rich and discourse-centered classrooms.

More Information

More on SchoolWide Labs work:

Visit our website.
Gendreau Chakarov, A., Biddy, Q., Hennessy Elliott, C., & Recker, M. (2021). The Data Sensor Hub (DaSH): A Physical Computing System to Support Middle School Inquiry Science Instruction. Sensors, 21(18), 6243. https://doi.org/10.3390/s21186243
Biddy, Q., Chakarov, A. G., Bush, J., Hennessy Elliott, C., Jacobs, J., Recker, M., Sumner, T., & Penuel, W. (2021). A Professional Development Model to Integrate Computational Thinking Into Middle School Science Through Codesigned Storylines. Contemporary Issues in Technology and Teacher Education, 21(1), 53–96.
Gendreau Chakarov, A., Recker, M., Jacobs, J., Van Horne, K., & Sumner, T. (2019). Designing a Middle School Science Curriculum that Integrates Computational Thinking and Sensor Technology. Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 818–824. https://doi.org/10.1145/3287324.3287476

Important references for our work

Hardy, L., Dixon, C., & Hsi, S. (2020). From Data Collectors to Data Producers: Shifting Students’ Relationship to Data. Journal of the Learning Sciences, 29(1), 104–126. https://doi.org/10.1080/10508406.2019.1678164

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
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¹ See Hardy, Dixon, and Hsi(2020) for more information about data producers and collectors.
² Table 2 in Gendreau Chakarov et al. (2021) has a full list of common DaSH bugs students have encountered across the project.

Book Review: You Look Like a Thing and I Love You

October 5, 2020AI in Education, Engineering, Learning, STEM, Teachers & Researchers, Written by: Judi FuscoJudi Fusco

By Judi Fusco

During CIRCL Educators’ Summer of Artificial Intelligence (AI), I read the book You Look Like a Thing and I Love You: How AI Works and Why It’s Making the World a Weirder Place¹, by Dr. Janelle Shane. I got the recommendation for it from fellow CIRCL Educator, Angie Kalthoff.

I found the book helpful even though it is not about AI in education. I read and enjoyed the e-book and the audio version. As I started writing this review, I was driving somewhere with one of my teenagers and I asked if we could listen to the book. She rolled her eyes but was soon laughing out loud as we listened. I think that’s a great testament to how accessible the book is.

Teaching an AI

Many of us use AI products like Siri or Alexa, on a regular basis. But how did they get “smart?” In the book, Dr. Shane writes about the process of training machine learning², systems to be “intelligent”. She tells us how they certainly don’t start smart. Reading about the foibles, flailings, and failings that she has witnessed in her work helped me understand why it is so important to get the training part right and helped me understand some of what needs to be considered as new products are developed.

Dr. Shane starts out comparing machine learning and rule-based AI systems, which are two very different types of AI systems. Briefly, a rule-based system uses rules written by human programmers as it works with data to make decisions. By contrast, a machine learning algorithm³ is not given rules. Instead, humans pick an algorithm, give a goal (maybe to make a prediction or decision), give example data that helps the algorithm learn⁴, and then the algorithm has to figure out how to achieve that goal. Depending on the algorithm, they will discover their own rules (for some this means adjusting weights on connections between what is input and what they output). From the example data given to the algorithm, it “learns” or rather the algorithm improves what it produces through its experience with that data. It’s important to note that the algorithm is doing the work to improve and not a human programmer. In the book, Dr. Shane explains that after she sets up the algorithm with a goal and gives it training data she goes to get coffee and lets it work.

Strengths and Weaknesses

There are strengths and weaknesses in the machine learning approach. A strength is that as the algorithm tries to reach its goal, it can detect relationships and features of details that the programmer may not have thought would be important, or that the programmer may not even have been aware of. This can either be good or bad.

One way it can be good or positive is that sometimes an AI tries a novel solution because it isn’t bogged down with knowledge constraints of rules in the world. However, not knowing about constraints in the world can simultaneously be bad and lead to impossible ideas. For example, in the book, Dr. Shane discusses how in simulated worlds, an AI will try things that won’t work in our world because it doesn’t understand the laws of physics. To help the AI, a human programmer needs to specify what is impossible or not. Also, an AI will take shortcuts that may lead to the goal, but may not be fair. One time, an AI created a solution that took advantage of a situation. While it was playing a game, an AI system discovered there wasn’t enough RAM in the computer of its opponent for a specific move. The AI would make that move and cause the other computer to run out of RAM and then crash. The AI would then win every time. Dr. Shane discusses many other instances where an AI exploits a weakness to look like it’s smart.

In addition, one other problem we have learned from machine learning work, is that it highlights and exacerbates problems that it learns from training data. For example, much training data comes from the internet. Much of the data on the internet is full of bias. When biased data are used to train an AI, the biases and problems in the data become what guide the AI toward its goal. Because of this, our biases, found on the internet, become perpetuated in the decisions the machine learning algorithms make. (Read about some of the unfair and biased decisions that have occurred when AI was used to make decisions about defendants in the justice system.)

Bias

People often think that machines are “fair and unbiased” but this can be a dangerous perspective. Machines are only as unbiased as the human who creates them and the data that trains them. (Note: we all have biases! Also, our data reflect the biases in the world.)

In the book, Dr. Shane says, machine learning occurs in the AI algorithms by “copying humans” — the algorithms don’t find the “best solution” or an unbiased one, they are seeking a way to do “what the humans would have done” (p 24) in the past because of the data they use for training. What do you think would happen if an AI were screening job candidates based on how companies typically hired in the past? (Spoiler alert: hiring practices do not become less discriminatory and the algorithms perpetuate and extend biased hiring.)

A related problem comes about because machine learning AIs make their own rules. These rules are not explicitly stated in some machine learning algorithms so we (humans aka the creators and the users) don’t always know what an AI is doing. There are calls for machine learning to write out the rules it creates so that humans can understand them, but this is a very hard problem and it won’t be easy to fix. (In addition, some algorithms are proprietary and companies won’t let us know what is happening.)

Integrating AIs into our lives

It feels necessary to know how a machine is making decisions when it is tasked with making decisions about people’s lives (e.g., prison release, hiring, and job performance). We should not blindly trust how AIs make decisions. AIs have no idea of the consequences of its decisions. We can still use them to help us with our work, but we should be very cautious about the types of problems we automate. We also need to ensure that the AI makes it clear what they are doing so that humans can review the automation, how humans can override decisions, and the consequences of an incorrect decision by an AI. Dr. Shane reminds us that an “AI can’t be bribed but it also can’t raise moral objections to anything it’s asked to do” (p. 4).

In addition, we need to ensure the data we use for training are as representative as possible to avoid bias, make sure that the system can’t take shortcuts to meet its goal, and we need to make sure the systems work with a lot of different types of populations (e.g., gender, racial, people with learning differences). AIso, an AI is not as smart as a human, in fact, Dr. Shane shares that most AI systems using machine learning (in 2019) have the approximate brainpower of a worm. Machine learning can help us automate tasks, but we still have a lot of work to do to ensure that AIs don’t harm or damage people.

What are your thoughts or questions on machine learning or other types of AI in education? Tweet to @CIRCLEducators and be part of the conversation.

Thank you to James Lester for reviewing this post. We appreciate your work in AI and your work to bring educators and researchers together on this topic.

See a recent TED Talk by author Janelle Shane.

Notes:

Read the book to find out what the title means!
Machine learning is one of several AI approaches.
Machine Learning is a general term that also includes neural networks and the more specialized neural network class of Deep Learning. Note also, a famous class of ML algorithms that use rules are decision-tree algorithms.
Some algorithms “learn” with labeled examples and some without, but that’s a discussion beyond the scope of this post.

How to Encourage Young Women and Marginalized People to Participate in CS and Engineering (part two)

February 15, 2019Book Club, Computer Science, Engineering, Learning, STEM, Teachers & Researchers, Uncategorized, Written by: Pati RuizPati Ruiz

by Pati Ruiz

This is the second post in a two part series based on my dissertation which focused on encouraging the participation of women and African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives in computing. The first post focused on modeling an interest and passion for CS and creating safe spaces for students. This post focuses on building community, introducing students to careers, and making interdisciplinary connections.

Build Community and Connect Students with Mentors

Family support is important! Young adults encouraged and exposed to CS by their parent(s) are more likely to persist in related careers (Wang et al., 2015). And did you know that women are more likely than men to mention a parent as an influencer in their developing a positive perception of a CS-related field, more often citing fathers than mothers as the influencers (Sonnert, 2009)? Unfortunately, parents’ evaluation of their children’s abilities to pursue CS-related fields differs by gender; parents of boys believe that their children like science more than parents of girls (Bhanot & Jovanovic, 2009). Nevertheless, family support is crucial for young women and supportive family members — whether or not they are connected to the tech world — play a critical role in the encouragement and exposure that young women get to the field.

Helping parents understand the role that they can play is important. As educators, we can model for them how to encourage their children as well as how to dispel misconceptions and harmful stereotypes that their children might have heard. Sometimes parents and family members themselves might unknowingly be perpetuating harmful computer science world misconceptions with the comments they make to their children. As teachers, we can provide parents with training that might help them understand how to encourage and expose their children to the field in positive ways. After all, the research shows that this support can be provided by anyone – not just educators.

All of the young women in my study described the value of mentors. Even seeing representations of female role models in the media can encourage a young woman to pursue a CS-related degree. It’s important for young women to see representations of people who look like them in the field and to have real-life female mentors and peers who can support them in their pursuit of CS-related degrees and careers. As a result of the low number of women in the field, mentors and role models for women are primarily men. While this can be problematic, it does not have to be. Cheryan et al. (2011) found that female and male mentors or role models in computing can help boost women’s perceived ability to be successful if those role models are not perceived to conform to male-centered CS stereotypes. The gender of the role model, then, is less important than the extent to which that role model embodies current STEM stereotypes.

The actionability of some of the factors described above, then, allows educators and others to positively influence and encourage young women in high school to pursue CS degrees in college (Wang et al., 2015).

Introduce Careers

In their recent report titled Altering the Vision of Who Can Succeed in Computing, Couragion and Oracle Academy described the importance of introducing youth to careers in technology. They find that:

“It is critical to improve the awareness and perception of a breadth of careers in computing to meet the demands of our workforce and the desires of our students. We need to elevate high demand and high growth computing fields such as user experience (UX) and data science – that when understood, appeal to and attract underrepresented populations.“

What this report found is what I found in my research; many African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives students don’t know people working in the computing field and don’t know what career options can look like. Couragion is working to change this by providing inclusive, work-based learning experiences that prepare students for jobs of the future. What I like about Couragion’s approach is that students are able to use an app to explore careers and engage with role models through text, activities, and videos. As they work their way through different career options, students take notes and reflect using a digital portfolio. I think this is a great way for students to develop career consciousness, something I wish I had when I was in school (as a student and teacher)!

As a teacher, the way I would connect my students with industry careers was to connect with local groups like GirlDevelopIt and invite speakers to my classroom. I also had college students visit my classroom – it usually works well to have recent graduates come back to talk to students because students relate well to recent high school graduates. I also introduced computer scientists in the news. If I were teaching right now, I would highlight 2018 MacArthur Fellow Deborah Estrin. In her Small Data Lab at Cornell, Dr. Estrin and her team are designing open-source applications and platforms that leverage mobile devices to address socio-technological challenges in the healthcare field. Or, I might direct them to this recent article written by Clive Thompson titled The Secret History of Women in Coding.

Some participants in my study mentioned that they ended up majoring in CS because of a mentor. One participant talked about how one of her high school teachers “dragged her to” a Technovation event. There, she ended up seeing a young woman who she “saw herself” in so she decided to apply to the same college that the mentor attended, got in, and went. This participant envisioned herself there because of this near-peer. She said that she didn’t connect with her mentor once she got to the university that they both attended for a year together, but just seeing her ahead of her in the program was motivating.

Again, the idea here is to create opportunities for students to connect with people in the field – to see themselves and to see the possibilities. Some groups that my students have worked with include Girls Who Code, Black Girls Code and Technolochicas – there are many others. Which ones do your students work with?

Make Interdisciplinary Connections

Finally, we have the idea of making interdisciplinary connections. CIRCL Educator Angie Kalthoff wrote a post for EdSurge discussing this very topic. Angie encourages teachers to ask their students: What are you doing outside of school that you want to tell other students about? She and a group of Minnesota educators organize student-powered conferences where middle schoolers showcase what they’re really interested in learning about. Check out her post because getting together with other educators to organize your own student-powered conference might be an excellent way you support and recruit young women and African Americans/Blacks, Hispanic/Latinx, and Native Americans/Alaskan Natives!

Interdisciplinary connections can be facilitated by teachers and it’s important to note that all of my study participants were very thankful to their K-12 teachers for having encouraged their pursuit of a technical field – even if they didn’t know they had. As one participant described, “a teacher who’s clearly passionate” is particularly encouraging.

One resource that can help you make interdisciplinary connections with students iss Connected Code: Why Children Need to Learn Programming by Yasmin B. Kafai and Quinn Burke. Join the CIRCL Educators book club to discuss this book starting in April!

Please note that the featured image for this post was created by #WOCinTech Chat, check them out! We’d love to hear from you — Tweet to @CIRCLEducators or use #CIRCLEdu.

Cyberlearning Community Report: Practical Impact in My Classroom

October 5, 2017Cyberlearning, Cyberlearning Report 2017, Engineering, Written by: Sarah HamptonSarah Hampton

By Sarah Hampton

In my last post, I talked about four reasons we should read the Cyberlearning Community Report: The State of Cyberlearning and the Future of Learning With Technology. I really believe that what you learn from the report will make you a more effective educator. Let me give you one concrete example of how the Community Report has already helped improve my teaching by demonstrating the significant value of learning opportunities outside the classroom and how they can be leveraged. (I had the privilege of sneak previewing the report over the summer so I have had a few months to implement what I learned!) Check out this excerpt from the report:

“The central ongoing research question in this work (from the Expressive Construction section) is how to interconnect appealing, playful environments through self-expression to deeper learning goals. The dimension of time is important: how can play result in learning at timescales of minutes, or weeks, or months or years? The dimension of context also needs more investigation: how do unique aspects of homes, museums, playgrounds or classrooms contribute to or block learning? Strengthening our understanding of the social dimension is also critical as these activities often involve complex ecologies of support from peers, parents, and informal and formal educators — and are not as simple as typical teacher-student interactions…This research is demonstrating how important learning can occur through playful experience, often outside of the school setting. Yet what students are learning clearly relates to existing curricular subject matter, such as engineering, and emerging subjects, like data science and computational thinking. Studying learning in playful and constructive settings can lead to new discoveries about when, where, and how children can learn important ideas and these discoveries can guide policy about when, where, and how these important topics are taught.”

In past years, I would plan a unit and then take my students on a field trip only if the exhibit(s) aligned at that time. This fall (after reading the report), the technology teacher and I planned an entire unit around a Smithsonian traveling exhibit called Things Come Apart that is currently housed in the Birthplace of Country Music Museum, a museum near our school. The exhibit consists of dozens of common objects that have been taken apart to reveal their inner workings. We tied this into physical science concepts like electricity, circuitry, and engineering. Before we visited the museum, students reverse engineered their own objects such as mechanical pencils, clocks, calculators, speakers, and flashlights. They also built circuits using PhET simulations, snap circuits, and then batteries, wire, light bulbs, motors, etc.

Student exploring circuits using PhET simulations

After that, we recruited local experts who donated their time, knowledge, and materials so our students could dismantle iPhone 5s phones. When the students later visited the exhibit, they recognized most of the components in the pieces and were able to ask and answer more informed questions because of their classroom work leading to the trip. Reading the report persuaded me that rich, authentic learning is fostered when connections are made between multiple environments, situations, and people, and it made me more intentional about offering opportunities across contexts. I would definitely describe this unit as a richer learning experience for my students than the ways I have approached it in the past.

Student dismantling the iPhone 5s

Going even further, as part of their final assessment, students are creating infographics on five electronic components and how they are used in one of the pieces from the museum exhibit. This was a suggestion from the technology teacher, and I jumped at the idea after reading about the STEM Literacy through Infographics project in the community report. Our students will present their infographics and dismantled objects at our school STEAM Fair in November.

I hope you take the time to read the report, and I hope it impacts your practice as much as it already has mine. I would love to hear your thoughts after you have had a chance to read it! What did you find most interesting? What innovations are you most excited about? Do you think you might look into one of the projects for your classroom? Post in the comments section below!