Whitney Dove, Author at Ellipsis Education

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Author: Whitney Dove

A Month in the Life of a Computer Science Advocate: Collaborating with AI to Advance Education

Posted on by Whitney Dove

A Month in the Life of a Computer Science Advocate: Collaborating with AI to Advance Education

December 6, 2024
Whitney Dove CEO

As an educator and leader in K-12 computer science, I have always sought tools that enable meaningful innovation while keeping teachers and students at the center of every decision. Over the past month, I have leaned into AI, not as a replacement for expertise but as a collaborator that challenges, amplifies, and refines my ideas.

The process of working with AI is not passive. It begins with thoughtful inputs: clear goals, strategic prompts, and a vision for the desired outcome. What follows is a back-and-forth dialogue where I critique, shape, and push for better results. My expertise is not just useful in this process; it is essential.

Here are some of the ways AI has supported my recent projects. The inputs I provided, and the decisions I made along the way, were critical to achieving meaningful outcomes.

1. Developing a Professional Development Session

The Input: I needed to create a session to help educators integrate AI into STEM education. The goal was to balance the efficiency AI offers with the need for hands-on conceptual learning. I provided specific objectives: create an outline that contrasts AI-driven tools with traditional teaching methods, emphasizes hands-on coding activities, and includes educator takeaways.

The Process: ChatGPT generated an outline comparing AI website generators with manual HTML coding. While helpful, it lacked depth on the pedagogical implications. I refined the prompts, asking for questions educators might have about balancing AI with foundational learning.

The Output: After refining several drafts, I finalized a session plan that tackles the nuances of AI’s role in classrooms. The plan empowers educators to think critically about where and how to use these tools. My expertise ensured the session would resonate with real-world classroom challenges.

2. Building the Case for Hands-On Learning

The Input: I wanted to articulate why hands-on learning is critical, even in the age of AI, and connect Ellipsis’ curriculum to a larger body of educational research. I shared specific examples from our curriculum, like hand movements and simple dances used to teach algorithms, and asked ChatGPT to tie these activities to research supporting hands-on learning.

The Process: ChatGPT synthesized educational research, highlighting how tactile and kinesthetic activities deepen comprehension. While the theory was sound, I refined the input to better align research findings with our examples. The goal was to emphasize how these activities help students internalize abstract concepts like algorithms, sequences, and loops.

The Output: The final argument framed hands-on learning as essential to conceptual mastery in computer science. The narrative connected Ellipsis’ curriculum to established research, making a compelling case for why these methods are foundational.

3. Building a Logic Model

The Input: I set out to develop a logic model for a K-12 digital literacy and computer science initiative. The goal was to align its vision, activities, and intended outcomes. I provided ChatGPT with information about the initiative’s goals, including improving academic achievement and career readiness, and asked for a framework that tied activities to measurable outcomes.

The Process: ChatGPT drafted a logic model that organized inputs, activities, outputs, and outcomes into a structured table. While helpful, it needed stronger connections between activities and outcomes. For example, I wanted to link teacher training to improved student engagement. I refined the prompts, asking for clearer pathways and actionable short-term and long-term outcomes.

The Output: The final logic model clearly mapped activities to measurable goals, such as:

    • Activities: “Deliver teacher training on digital and financial literacy.”

    • Outputs: “Completion of training; implementation of classroom projects.”

    • Outcomes: “Short-term: Improved teacher confidence. Long-term: Increased academic achievement and career readiness.”

The process ensured the framework was both practical and aligned with the initiative’s vision.

4. Piloting an Updated Curriculum

The Input: Preparing for an upcoming pilot of our updated curriculum, I wanted teacher materials that clearly explained its new features. I shared details about thematic units and differentiated assessments and asked ChatGPT to organize these into a concise overview.

The Process: ChatGPT’s initial draft summarized the features but needed a stronger focus on their benefits. For example, I wanted more emphasis on how thematic units support learning progressions and how frequent assessments provide actionable insights. I refined the prompts to highlight the practical value of these updates.

The Output: The final overview distilled the updates into clear, teacher-friendly messaging, such as:

    • Thematic Units for Intentional Learning: “Each unit connects learning to real-world contexts and ends with a thematic close to reinforce key takeaways.”

    • Differentiated Assessments for Progress Monitoring: “Formative assessments offer regular check-ins, while exit tasks ensure mastery of specific goals.”

The materials equipped teachers to approach the pilot with confidence, ensuring the curriculum’s strengths were communicated effectively.

AI as a Collaborative Partner in Education

What excites me most about working with AI is not just how it boosts productivity. It invites me to think critically and creatively. At every step, my inputs, critiques, and expertise shaped the results.

I have found that using AI invites me to:

    • Lead the Conversation: Clear, strategic inputs guide the AI toward meaningful outcomes.

    • Critique and Evolve Ideas: Each output is a starting point, sparking deeper reflection and refinement.

    • Extend My Thinking: AI offers perspectives I might not have considered, pushing me to explore new possibilities.

As I wrote in my blog, “Navigating AI in the Classroom: A Journey of Discovery,” AI is not a replacement for the human element. It is an amplifier of human potential. This partnership allows me to focus on what matters most: empowering teachers and inspiring students.

Looking Ahead

At Ellipsis Education, we believe technology should enhance, not replace, the transformative role of educators. As we continue to create tools and resources that inspire classrooms, AI will remain a valuable partner in refining and amplifying the work that drives real impact.

If you are curious about how AI can elevate your work, or if you are passionate about advancing K-12 education, let’s connect. Together, we can shape the future of teaching, learning, and innovation.

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The Benefits of Hands-On Learning in Computer Science and STEM Education

Posted on by Whitney Dove

The Benefits of Hands-On Learning in Computer Science and STEM Education

November 27, 2024
Whitney Dove CEO

Introduction

At Ellipsis Education, we believe hands-on learning is at the heart of building deep, lasting understanding in computer science. It’s not just about learning how to code; it’s about really understanding the core concepts that power technology. Through unplugged activities, coding projects, teamwork, and real-world exposure, students build the skills they need to thrive. Here’s why hands-on learning matters—and how it prepares students to use technology with confidence and creativity.

1. Encourages Active Engagement Through Diverse Hands-On Activities

Hands-on learning is all about active involvement. In our classrooms, students dive into activities that help them truly feel the concepts. For example, they might use body movements or hand gestures to represent sequences or loops, making abstract ideas more concrete. Or they might work together to solve coding challenges, participating in real-world team-based problem-solving. By moving beyond the screen, students gain a deeper connection to the material. Engaging in collaborative, interactive activities significantly boosts both retention and understanding (Chen & Liu, 2024). This engagement helps students learn in ways that go far beyond the traditional classroom experience.

2. Builds Problem-Solving Skills Through Active Exploration

Hands-on learning gives students the freedom to tackle real challenges. Whether they’re troubleshooting code or working together to break down a complex problem, they practice the critical thinking skills needed in computer science. Unplugged activities like building algorithms through dance steps or collaborating on coding projects give students a chance to experiment, fail, and try again—just like their adult counterparts: programmers. This iterative process helps them think critically and develop solutions that work. A hands-on approach has been shown to strengthen problem-solving abilities, making students ready to apply their skills to real-world situations (Oyedeji,2024)

3. Promotes Conceptual Understanding with Real-World Connections

Hands-on activities don’t just teach students how to code; they help them understand the why behind the concepts. By physically representing algorithms or building models, students internalize abstract ideas before they even touch a line of code. When they engage with coding challenges or unplugged exercises, they make those concepts stick. And when we bring STEM professionals into the mix—through STEM career lessons or projectbased simulations—students see firsthand how these concepts come to life in the real world (Garofalo, 2024). This connection makes learning feel relevant and gives students a clear vision of their potential career paths.

4. Develops Critical Thinking Through Collaborative Projects

Collaboration is at the core of hands-on learning. Students work together in teams to solve problems, debug code, and share ideas. They practice listening, sharing, and refining their solutions, just as they would in any real-world STEM environment. For example, a group coding project might have students debugging, refining their approaches, and brainstorming solutions together. This collaborative learning process enhances critical thinking and prepares students for the teamwork they’ll experience in the workplace. Research confirms that collaborative projects improve communication and problem-solving skills—skills essential for future success (Holly et al., 2024).

5. Fosters Soft Skills Like Perseverance and Growth Mindset

Hands-on learning teaches students more than just technical skills—it builds the soft skills that matter in every career. In computer science, this means teaching students how to approach challenges with perseverance. Whether they’re debugging a line of code or revising a project based on feedback, students learn that failure is just another
step toward success. This builds a growth mindset, helping students see setbacks as learning opportunities. A flipped classroom model, which prioritizes hands-on, iterative learning, has been shown to help students embrace failure and refine their understanding (Chen & Liu, 2024). These soft skills—perseverance, adaptability, and resilience—will serve them well as they tackle any challenge, in or out of the classroom.

6. Prepares Students for the Future with Real-World STEM Exposure

Hands-on learning isn’t just about solving problems today—it’s about preparing students for the future. By engaging in coding projects or unplugged activities, students gain the practical experience they need to succeed in the tech-driven world. We also make sure they connect with STEM professionals through guest speakers or careerfocused activities. These experiences help students understand how the concepts they learn today are used in the real world, whether that’s designing IoT systems or solving industry challenges with cutting-edge tech (Kawdungta & Maneetien, 2024). With these real-world connections, students can see a clear path toward future careers in computer science and technology.

Conclusion

Hands-on learning is the foundation for developing the critical skills students need to succeed in computer science and beyond. By combining unplugged activities, coding projects, teamwork, and exposure to realworld STEM careers, we help students build the deep understanding and soft skills they need to be successful. At Ellipsis Education, we are committed to empowering teachers with the resources and support they need to give students meaningful, hands-on learning experiences—helping them gain the confidence and creativity to harness technology and shape the future.
Conclusion

References

Chen, J. C., & Liu, C. Y. (2024). Using knowledge building and flipped learning to enhance students’ learning performance in a hands-on STEM activity. Journal of Computer Assisted Learning, 40(1), 15–30. Oyedeji, A. N. (2024). Teaching basic concepts in machine learning to engineering students: A hands-on approach. ResearchGate. Journal of Educational Engineering Innovation, 12(3), 45–60. Garofalo, S. G. (2024). Conceptual understanding of the DNA molecule through model building at the initial learning stage. Journal of Science Education and Technology, 31(2), 123–137. Holly, M., Habich, L., & Seiser, M. (2024). FemQuest: An interactive multiplayer game to engage girls in programming. IEEE Conference Proceedings on Educational Tools, 89–100. Kawdungta, S., & Maneetien, N. (2024). The design and construction of an IoT learning board using ESP32 and FPGA. IEEE Proceedings on Technological Innovation, 55–70.

Free Computer Science Lesson Plans

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