The development of robotics courses for young children under vector space model

Area of Science:

• Early Childhood Education
• Educational Technology
• Artificial Intelligence in Education

Background:

• Robotics education is vital for developing children's cognitive, practical, and innovative skills.
• Existing research lacks focus on applying robotics education specifically for young children.
• Stimulating children's interest and improving learning quality are key objectives.

Purpose of the Study:

• To analyze the application of the Vector Space Model (VSM) in developing robotics courses for senior kindergarten students.
• To design an automatic document classification system using VSM to aid in compiling robotics teaching materials.
• To assess the feasibility and teacher support for integrating AI and robotics into early childhood education.

Main Methods:

• Survey of robotics course offerings in 38 kindergartens in Baoji City.
• Development of an automatic document classification system based on the Vector Space Model (VSM).
• Performance testing of the VSM system against Chinese documents and Term Frequency-Inverse Document Frequency (TF-IDF) methods.

Main Results:

• Only 24% of surveyed kindergartens currently offer robotics courses.
• A significant majority (70.14%) of teachers support the establishment of children's robotics courses.
• The VSM-based classification system demonstrated superior performance with accuracy, recall, and F1 scores exceeding 85%.

Conclusions:

• The developed robotics course provides an enhanced teaching environment for introducing AI and robots to young children.
• Robotics education is a significant area within current curriculum reforms, crucial for early childhood education innovation.
• The VSM system effectively assists in the development and compilation of robotics teaching resources.