Abstract:

Pedagogical innovation is in constant flux, evolving to address the challenges of our dynamic world. A recent trend in this field is the use of robots as educational instruments across various learning setups. The technological boom has made robots and their parts both accessible and affordable, spurring an increasing integration of robotics into the broader educational framework.
These robotic interactions offer hands-on learning experiences for students. Engaging with these machines, they hone essential skills for the 21st century – from critical thinking and problem-solving to teamwork and creativity. The tangible and responsive nature of robots also provides a captivating learning space that heightens students' enthusiasm and engagement.
The core objective of this study is to explore the impact of robotics on young learners, not just as instruments for programming but also as tools for foundational STEM learning.
During the preparation phase, resources are first gathered, a series of progressively complex mazes are designed, ensuring they can be adapted for varied challenges. A suitable coding platform or software, where young learners can input algorithms, is chosen, ensuring it's both suitable for young learners and compatible with the robots. Before the experiment starts, educators are trained in the robotics software and maze challenges to offer smooth guidance. For participant onboarding, all 60 participants are introduced to the course structure, objectives, and expectations.
The course implementation spans 12 weeks. In the first two weeks, both groups are introduced to algorithmic thinking. The control group uses traditional teaching aids, while the experimental group supplements the learning with robots, witnessing the real-time results of their algorithmic logic. The next phase, spanning weeks 3-4, introduces basic maze challenges. The control group solves paper-based mazes using logical reasoning, while the experimental group begins to code robots to navigate these basic mazes, learning through trial and error. From weeks 5-7, intermediate maze challenges are introduced. The control group delves deeper into logical problem-solving techniques on paper, while the experimental group learns and implements new algorithms to navigate robots through more complex mazes. The course then progresses to advanced maze challenges in weeks 8-10. Here, the control group emphasizes collaborative solutions through group discussions on complex algorithms. In contrast, the experimental group forms small teams, collaboratively coding robots to navigate the most intricate mazes. The final two weeks (11-12) focus on recap, refinement, and project development. The control group reviews major concepts, discusses real-world applications of the learned algorithms, and completes a paper-based project. Meanwhile, the experimental group refines their robot's maze-solving abilities and prepares for a final project where they both design a maze and code the robot to navigate it successfully. Upon course completion, a post-course assessment is administered.
Statistical software is used to analyze the data, determining significant outcome differences between the control and experimental groups. The findings, insights, and recommendations based on the experimentation are then consolidated into a comprehensive report.

Keywords:

Robots, Educational Robotics, experiential learning.