Learning Sciences | NYU MakerSpace

Learning Science Theory: NYU MakerSpace

Educational Communication and Technology, New York University
Foundations of Learning Science, Fall 2024

Overview

Learning science constructivism is a theory that suggests people learn best by actively constructing their own understanding rather than passively absorbing information. It emphasizes hands-on experiences, problem-solving, and social interactions to build knowledge. Learners integrate new information with their prior knowledge, refining their mental models through exploration, reflection, and collaboration. This approach is foundational in modern education, encouraging inquiry-based learning, project-based activities, and scaffolding to support deeper comprehension.

About the NYU MakerSpace

Located at the Tandon School of Engineering - NYU MakerSpace is a 10,000 sq ft lab established in 2016. MakerSpace is free of charge and open to all current NYU students and faculty members. The space offers seven distinct areas for learners to engage in presentations, workshops and to work collaboratively or independently on projects for school or hobby. MakerSpace proudly features the Design Lab, a collaborative student-led organization that provides NYU students with the opportunities to brainstorm, test, create models, and develop their ideas. Students and faculty have weekly opportunities to partake in the workshops at the Design Lab. Relevant examples of recent events are: learning how to sew, how to make a solar panel, creating custom vacuum chocolate molds, and how to ace a technical interview for a job. 

NYU Makerspace serves as a central hub for Tandon students and is the de facto meeting place for innovation. All of the students are expected to use this facility while enrolled in the engineering school. Students come to the MakerSpace to connect with peers and teachers on school related projects, and to learn or find new hobbies at the Design Lab. The reputation of the lab is that it’s a haven for creative “techies” looking to collaborate or study in a creative atmosphere. Going into this project the assumption was that most students who congregate in the MakerSpace are from the engineering school. However, it was discovered that students and faculty from all of the NYU schools came here to learn and innovate. I witnessed a collection of diverse learning experiences observed through the theoretical lens of Constructionism. 

Additionally, entering a relatively foreign space, referring to both its technology and layout, exploring the MakerSpace felt intimidating, but in the end, it was a fruitful and insightful experience. 

At first glance, I knew the NYU MakerSpace was a great choice to observe learning, but did somewhat struggle with identifying what it was I was hoping to observe and analyze while there. For example, upon observation we realized there are some topics that are not easily identifiable due to their complexity like cognitive processes, identity, and cultural influence, which can be difficult to observe or capture effectively.

With that said, I aimed to examine Constructionism, delving into how hands-on participation based learning, the creation of public artifacts, and reflective practices enable students to build and retain knowledge.

Research Methods

Throughout the course of study, learning was observed at the MakerSpace, Design Lab workshops, and one off-site location outside of the MakerSpace. To ensure transferability, detailed field notes were taken, incorporating thick descriptions of what was seen and heard. Several workshops were examined, each featuring a variety of materials for project fabrication and the introduction to software and machinery. Audits of machines, materials, and the surrounding environment were conducted to assess their influence on hands-on participatory learning.

As dozens of students engaged in the learning process through social interactions and artifact creation, follow-up interviews were arranged with select individuals and groups to gather additional insights on concepts that are more difficult to observe, such as metacognition. This included two semi-structured interviews of students who attended the workshops or were there for school related projects. Additionally, independent desk research was performed to further explore key focus areas. Upon completion of the research, qualitative analysis revealed several noteworthy insights.

Constructionism Theory

My exploration on Constructionism focused on how participation-based learning proved more effective for knowledge development than rote learning, as highlighted by Sawyer (2022). My research on Constructionism (see Figure 1) conducted in-person. Students come to the MakerSpace to connect with peers and facilitators on school-related projects. The MakerSpace proudly features the Design Lab, a collaborative space that provides NYU students with opportunities to brainstorm, test, create models, and develop their ideas. Relevant examples of events I observed were Intro to Draping, Vacuum Form Chocolate Molds, and the observation of a team of engineering students testing their software for quadruped crowd navigation using an UNITREE GO1 Pro Robotic Dog. Additionally, I had the opportunity to interview three students I met and observed at the MakerSpace. The combination of observation, interview testimonies, and secondary research has provided me with deep insights examining Seymour Papert’s theory of Constructionism (1980), which builds on Jean Piaget’s Constructivism (1954).

From my research, I hypothesized that Constructionism Theory fosters a deep, lasting knowledge through the process of hands-on experiences led by a facilitator. I believed that creating public artifacts, articulating what’s been learned, and reflecting on a project fosters understanding and long-term retention of knowledge.

To support my claims, I focused on answering the following questions:

1. How does participation-based learning help students construct knowledge?

2. How does having access to the MakerSpace and a teacher or peers’ as a facilitator shape the student’s learning?

3. Why are public artifacts and articulation helpful for learning?

4. How does reflecting on a project help students remember what they learned?

Claim 1: Students who utilize the MakerSpace with access to a facilitator demonstrated effective learning of new concepts through participation-based learning.

A detailed look at my field notes and interview synthesis revealed key insights that support Constructionism Theory. Each interviewee mentioned it was their first time attending a workshop at the MakerSpace, and they were at a beginner’s level for each subject. During Intro to Draping, 16 participants were provided with all the necessary instructions and materials to create two clothing patterns. The instructor scaffolded the curriculum by dividing the class into four progressive sections: beginning with drawing zones of the body to draft clothing patterns, moving to basic draping and pinning, advancing to more complex techniques, and finally transferring the draped fabric into paper patterns. Between class sections, the instructor moved around the table, offering guidance and feedback to help each participant understand the materials and learn the draping process. This approach appeared to boost participants' confidence. As Sawyer (2022) noted, the role of a facilitator nurtures an environment where students feel empowered to explore and take ownership of their learning. One participant stated, “If I tried to take this same workshop online, like on YouTube, I would have been discouraged and might have given up. I need access to the materials and an instructor for guidance.” They preferred in-person classes for these reasons.

Learners embodied active learning, aligning with Papert’s Constructionism Theory, where knowledge is built through hands-on experience and reflective practice. As one participant noted, “Holding and observing the materials helps me understand what improvements are needed.” Without this hands-on experience, they would not fully grasp the process. As tactile learners, they benefited from hands-on movement, testing, and trial and error, retaining information through a non-traditional learning environment.

Similarly, in Vacuum Form Chocolate Molds (see Figure 2), six participants were given a plastic PETG sheet and an object made with the MakerSpace laser cutter to create a custom vacuum mold. The vacuum form has two components: a heating mechanism and a vacuum mechanism. While the machine is straightforward, the subtle ways in which the PETG sheet bends when heat is applied, the positioning of the handle, and the speed of pulldown require some precision for the mold to form correctly. One participant reflected, “Having the TA show me how to use the vacuum mold machine was necessary to understand the process. He explained how the plastic would start to get a glossy sheen when it was ready to mold, and to then turn on the vacuum mechanism.”

Another interviewee shared, “Building something hands-on is the only way I achieve true understanding and lasting knowledge.” She went on to explain that the process gave her an appreciation for the technical skill involved, something she wouldn’t have gained through traditional rote learning. Without a learning environment like the MakerSpace knowledge would be limited to just theory alone. 

Claim 2: Externalizing artifacts and articulation promotes deep understanding by organizing thoughts, making ideas clearer, and revealing gaps in understanding.

Observations and personal testimony provided empirical evidence supporting Constructionism Theory. A team of NYU Tandon computer software engineering students performed algorithm testing outside the MakerSpace, using a UNITREE GO1 robotic dog for quadruped crowd navigation. The experiment was for Introduction to Machine Learning (see Figure 3). The goal of the project was to instruct a robotic dog to efficiently navigate through large crowds of people without collisions using computer vision. During the performance testing, the group gathered data through live data visualization: showing sensor data, such as distance from obstacles, detected object types, and path predictions. The students also constructed a dashboard to show algorithmic flowcharts, which would make the internal workings of their code more transparent and allow for collaborative feedback between peers. The externalized visualizations required the engineers to break down complex layers of code, which ultimately revealed gaps in the model’s behavior. For instance, unexpected sensor readings or logic loops in flowcharts can expose areas needing code refinement. When asked what the engineer gathers from analyzing the algorithm outputs, he says, “I’m thinking about the different parts of the output and what’s happening in the code, as I'm watching the robot's responses and trying to simultaneously think of a debugging strategy”. When the engineer analyzed the algorithm outputs, he was encouraged to engage in metacognition, reflecting on his thought process to critically evaluate his logic, refine strategies, and iteratively improve the code based on observed outcomes. Through this iterative process, his code continued to be refined and updated.

Externalizing artifacts in a dashboard facilitated the reflective process of identifying issues early, allowing engineers to adjust their algorithms or troubleshoot problems that might otherwise have remained hidden in a theoretical model. Papert suggests a similar sentiment when students learned LOGO the programming language. He argues that when students recognize patterns, and learn to debug their own thinking, is when transformative knowledge takes place through metacognition (1980). 

Engaging in creation encourages exploration, critical thinking and reflection, which helps students foster comprehension and retention. During Intro to Draping, one student completed building her bodice, while her peer struggled to apply the same advanced techniques for draping fabric onto a dress form. When the struggling student asked for help, her peer presented her completed work using it as reference. When later interviewed, she stated, “explaining the bodice construction techniques to my partner helped me understand the intricacy of draping techniques much better. When I described how to vertically align the darts around the bodice, and match the side seams of fabric, it helped reinforce the principles of advanced draping techniques.” Additionally, she went on to say, “when I was put in a position to explain my work, I felt a sense of responsibility to learn quickly and accurately, which helped enhance my knowledge. These findings support that in expressing ideas, or giving them form, we make them tangible and shareable which, in turn, helps shape and sharpen these ideas (Ackermann, 2004). 

Creating public artifacts and articulation represents active engagement in learning, transforming knowledge that reinforces comprehension. This approach shifts theory into practice and empowers learners to remember through externalization. 

Claim 3: Students who reflected on artifact creation transformed their knowledge into coherent concepts by using metacognition to encode ideas into long-term memory.

For my final claim, I evaluated how constructionism inherently supports metacognition for students that actively monitor cognitive strategies, like planning, evaluating, monitoring, and adjusting their thinking. Focused attention is crucial for processing and organizing new information into long-term memory. Through observation, the initial stage of encoding knowledge took place when participants experienced deep concentration.

For example, during Intro to Draping (see Figure 4), the instructor advanced the class from foundational skills towards working on a complex pattern for a bodice, which required students to actively engage in metacognition by evaluating their understanding and applying their knowledge. While observing the progression of the two-hour class, all of the participants began the class sitting but were now standing, leaning in towards their materials and dress forms. The participants that were previously engaged in conversation had now become quiet and still, focusing only on eye-to-hand coordination to complete the task. Their eyes were fixated on matching a front and back piece of fabric and constructing darts to frame a bodice.

Throughout these observations, participants stopped to record and track progress in notebooks or take pictures on devices. The more engaged the participants were in the process of hand-on learning, and recording of the process, the closer their bodice resembled the instructor's example.

The act of deep concentration and self-monitoring supports the claim that active learning engages higher-order cognitive processes, reinforcing memory retention and promoting a deeper understanding of concepts compared to surface-level learning. Additionally, many participants had to make multiple attempts at creating the bodice, failing at first and then refining their work through iteration, participants took this chance to reflect and refine their work. This aligns with Papert’s theory of Constructionism, where learners build knowledge through hands-on experience, learning by doing, and refining their artifacts. Papert emphasized that learning happens most effectively when individuals actively engage with the task, make mistakes, and reflect on how to make improvements (Papert, 1980).

Another example of reflection and self-monitoring approaches, was observed during the UNITREE GO1 Pro Robotic Dog algorithm testing outside the MakerSpace for quadruped crowd navigation. After the performance testing was completed, one engineer explained, “coding is just very error prone. There’s many layers of abstraction in software development, because ultimately it’s all just binary, you know, just literally ones and zeros. Then there’s a layer of abstraction above that, which is assembly language, which is basic commands that a CPU can understand, for example, telling the computer where to look in memory for values, basic stuff like that.” As Kafai (2005) states, reflection encourages learners to think about their own thinking and learning processes. 

Additionally, Kafai explains that reflecting on knowledge construction leads to appropriation—a process by which learners make knowledge their own and begin to identify with it. Appropriation took place after completing the Vacuum Form Chocolate Molds workshop. One student described how they planned to make her own vacuum form chocolate molds using Disney figurines. This example showed how one learner began to build upon their knowledge, and shift it towards their personal frameworks and identity.  

Conclusion

Reflecting on my research, my analysis revealed that when a learner has access to an environment like the NYU MakerSpace, combined with the opportunity to engage with a facilitator, they experience an enriched acquisition of knowledge. Hands-on participation based learning moved students beyond surface-level learning, fostering deeper encoding of information through metacognitive processes. NYU MakerSpace provided beneficial insights, strengthened by evidence-based testimonies, that reinforces Papert’s Constructionism theory. 

Future Research

Based on the insights gained from our observation, we propose the following topics for future research: 

1. Conduct long-term research on how participating in MakerSpace impacts students’ professional skills and how it influences their academic and career development. 

2. Compare and analyze the learning effects between the traditional classroom environment and MakerSpace environment. 

3. Investigate the role of different cultural backgrounds and prior knowledge in shaping students’ participation and study efficiency in MakerSpace activities. 

References

Ackermann, E. (2004). Constructing knowledge and transforming the world. 

Kafai, Y. B. (2005). Constructionism. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. Cambridge University Press.

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.

Piaget, J. (1954). The construction of reality in the child. Basic Books.

Sawyer, R. K. (2022). An introduction to learning sciences. Cambridge University Press.