Question 1: Did you bring several disciplines together in your own teaching? Do you collaborate with teachers in other disciplines? What are the opportunities and challenges.
For this task, I was envisioning the teacher designing something for supporting monitoring noise levels of the classroom – positioning the teacher as creator/designer of classroom tools. The sound recording Arduino is equipped with a noise measuring tool which can be placed at 3 levels which measures very low sound, medium sound, and elevated sound. Even though Im not working with children in this setting, Im modeling how to create things for different purposes as the teacher. Its common in classrooms to have different voice level expectations depending on what learning activity is occurring. For examples, 1) a teacher may choose to have ‘silent reading time’ where measuring low-level sound could be appropriate, 2) a teacher may choose to have ‘partner and group work’ where measuring medium-level sound could be appropriate, and 3) a teacher may choose to have ‘regular voices’ for making, building, playing, etc., where measuring high-level sound (i.e., yelling) could be appropriate. The purpose with the monitor could be a visualization that could be projected on the whiteboard or from screen so children/classes can self-monitor – instead of always having to be told by the teacher ‘be quiet’, you’re too loud, etc.
Question 2: How do you envision a makerspace in your school? How does it look like? If you have one already, how would you modify it.
We currently have the Warrior Fab Lab @ Stan State and we are on the move (literally) to a bigger space to accommodate for the significant numbers of college students, projects, use by faculty, and our continued and growing Pk-12 Outreach programs. What we are looking to do is to improve the pedagogical spaces at a larger scale.
Anna Stetsenko’s perspective on group collaboration builds on her Transformative Activist Stance (TAS), which sees human development as an active, participatory process of agency and social transformation. While participation emphasizes individual engagement and agency highlights personal initiative, collaboration in groups enhances both by fostering interpersonal contribution—where individuals co-create knowledge and social change. Through group collaboration, agency is not just personal but expands into a collective force, enabling deeper transformation. This shift emphasizes that meaningful development occurs through shared endeavors, where individuals shape and are shaped by social interactions, thus making change more impactful and sustainable.
In the context of Fab Labs and Maker Spaces, Jay Dommage’s concepts of group transformative agency and dependency agency help explain how these spaces foster innovation and social change.
· Group Transformative Agency: Fab Labs and Maker Spaces enable collective creativity, where individuals co-design, prototype, and problem-solve together. Through shared expertise, peer learning, and open-source collaboration, these spaces cultivate a collective agency that can challenge traditional production models and democratize technology. Participants are not just individual makers; they become co-agents of transformation, actively shaping knowledge, tools, and social structures.
· Dependency Agency: While these spaces empower individuals, they also rely on institutional, technological, and communal infrastructures—such as funding, open-access tools, and mentorship networks. Rather than seeing this reliance as a limitation, Dommage’s framework suggests that agency is often exercised through these dependencies. Participants leverage the available resources, networks, and expertise to innovate, demonstrating that agency does not exist in isolation but within interconnected systems.
Thus, Fab Labs and Maker Spaces illustrate how transformative change happens through both collective empowerment (group transformative agency) and strategic reliance on existing structures (dependency agency), reinforcing the idea that interdependence is central to creative and social innovation.
From the perspective of Sara Amsler, who emphasizes critical hope and radical possibilities in transformative spaces, Fab Labs and Maker Spaces serve as sites of both technological innovation and socio-political change. These spaces allow individuals to not only create advanced high-tech projects but also to reimagine futures beyond dominant power structures.
1. Robotic Arms for Accessibility & Precision Engineering
a. Makers have designed 3D-printed prosthetic robotic arms with customizable sensors, enabling affordable, open-source solutions for individuals with disabilities.
b. Precision robotic arms used in Fab Labs assist in micro-manufacturing, allowing small-scale innovators to compete with industrial-grade production.
2. Hydroponics & Smart Agriculture Systems
a. DIY automated hydroponic farms integrate IoT sensors, enabling urban farming in small spaces and addressing food security challenges.
b. Some labs experiment with AI-driven plant monitoring, where infrared and humidity sensors optimize plant growth in real time.
3. Infrared & Environmental Sensors for Smart Cities
a. Fab Labs develop low-cost infrared motion sensors for security, automation, and energy-efficient smart homes.
b. Air quality and water purity sensors designed in Maker Spaces empower communities to monitor pollution and advocate for environmental justice.
These projects exemplify how Fab Labs and Maker Spaces are not just hubs of technological creation but also incubators of radical hope. By empowering individuals with the tools to imagine and construct alternatives—whether through assistive robotics, sustainable food systems, or environmental monitoring—these spaces materialize hopeful futures beyond corporate and institutional control.
Thus, in Amsler’s view, Fab Labs and Maker Spaces are not just sites of making but of world-building, where high-tech innovation intersects with social transformation, reinforcing that another future is always possible.
Question 3: After the definition of computational thinking? Are you somehow using computational thinking in your teaching? How? Do you think you can take advantage of computational thinking? How?
Computational thinking is a problem-solving approach that involves breaking down complex problems into manageable parts, recognizing patterns, developing step-by-step solutions (algorithms), and using abstraction to generalize solutions. It is a way of thinking that mirrors how computers process information but is applicable across many fields beyond computing.
Key components of computational thinking include:
1. Decomposition – Breaking a problem into smaller, more manageable parts.
2. Pattern Recognition – Identifying similarities or trends to make problem-solving more efficient.
3. Abstraction – Focusing on essential details while ignoring irrelevant ones.
4. Algorithmic Thinking – Developing a clear, step-by-step solution or set of rules to solve a problem.
Computational thinking is essential in coding, data science, engineering, and even everyday decision-making, as it helps structure problems logically and efficiently. Here's a couple activities I consider for my course and teacher candidates:
Activity: Future teachers guide students in decomposing California’s weather patterns into key components (temperature, precipitation, wind, ocean currents). They identify patterns such as seasonal droughts, wildfire, or coastal vs. inland climate differences. Using abstraction, students focus on key factors like the Pacific Ocean’s influence while ignoring less relevant details. Finally, they develop a step-by-step algorithm to predict weather changes based on given conditions (e.g., “If ocean temperatures rise, then…").
Application: Teachers learn how to integrate computational thinking by having students create weather prediction models using physical charts, simulations, or simple coding tools like Scratch or data spreadsheets. This helps young learners understand how meteorologists analyze and forecast weather.
Activity: Future teachers can guide students in decomposing California’s earthquake data into key components, such as magnitude, depth, location, and frequency. Students can identify patterns in earthquake occurrences (e.g., higher frequency along fault lines like the San Andreas). Using abstraction, they can simplify the data to focus on patterns of magnitude or location while ignoring less significant details. Finally, students develop an algorithm for predicting possible earthquake risk based on data trends (e.g., “If earthquakes increase in magnitude near a fault line, then the likelihood of another event may increase").
Application: Teachers help students visualize and interpret earthquake data using graphs or basic simulations, showing them how to read seismic charts or use simple coding tools (like Scratch) to model seismic activity. This integrates computational thinking into understanding complex natural phenomena and helps students learn about earthquake preparedness.