Grade 5 Curriculum

This grade 5 curriculum focuses on AI, Robotics, Drone Science, Data Science, and Machine Learning. It builds on the foundation of previous years while introducing more advanced and interdisciplinary concepts.

Subject: Mathematics
1. Advanced Data Analysis: Teach students how to analyze and interpret data using more advanced statistical techniques, including measures of central tendency, variability, and data representation using spreadsheets.
2. Geometry and Coding: Introduce advanced geometry, including concepts like angles, symmetry, and tessellations, and relate them to coding and robotics.
3. Algebraic Thinking: Introduce basic algebraic concepts, such as variables, equations, and expressions, with a focus on problem-solving.
4. Real-world Math Applications: Apply mathematical concepts to real-world problems, such as calculating areas and volumes for architectural projects or budgeting for technology upgrades.

Subject: Science
1. Advanced Scientific Inquiry: Deepen the understanding of the scientific method, hypothesis testing, and experimental design. Encourage students to propose and conduct their experiments.
2. Life Sciences: Explore ecosystems, adaptation, and biodiversity in greater depth. Focus on ecological relationships and the impact of human activity on the environment.
3. Physical Sciences: Study advanced concepts in physics, such as forces, energy, and motion. Use robotics to conduct experiments that demonstrate these principles.
4. Earth and Space Sciences: Investigate geological processes, natural disasters, and space exploration with an emphasis on interdisciplinary connections with technology.

Subject: Technology and Engineering
1. Robotics Programming: Advance robotics programming skills, including designing and building robots to complete complex tasks. Explore the role of sensors in robotics.
2. Coding Mastery: Teach students text-based programming languages and introduce concepts like loops, functions, and conditionals to create more complex programs.
3. AI and Machine Learning: Provide an in-depth understanding of machine learning, supervised and unsupervised learning, and how it is used in various industries.

Subject: Drone Science
1. Drone Design and Engineering: Teach students about the design and engineering of drones. Encourage them to design and build their drones.
2. Advanced Mapping and Data Analysis: Explore advanced mapping techniques, remote sensing, and GIS applications. Analyze data collected from drones for various real-world purposes.
3. Drone Regulations and Ethics: Discuss the legal and ethical aspects of drone use, including privacy, safety, and airspace regulations.

Subject: Data Science
1. Advanced Data Collection: Expand data collection methods to include sensor data, surveys, and more complex experimental setups.
2. Big Data and Data Handling: Teach students how to work with larger data sets, emphasizing data cleaning, transformation, and storage.
3. Advanced Data Visualization: Create complex data visualizations using specialized software and tools.

Subject: Machine Learning (Intermediate to Advanced)
1. Machine Learning Algorithms: Dive into machine learning algorithms like decision trees, regression, and clustering, and explore how they are used in real-world scenarios.
2. AI Ethics and Bias: Discuss the ethical considerations and potential biases in AI and machine learning, and engage in discussions about responsible AI development.
Integrated Projects and Activities:

• STEM Research Projects: Encourage students to conduct independent research projects that integrate math, science, technology, and engineering.



• Robotics Competitions: Organize robotics competitions or challenges with a focus on coding and programming complex tasks.
• AI Projects: Challenge students to create AI projects that address specific problems, such as chatbots for customer service or image recognition for security.
• Cross-curricular Projects: Promote projects that integrate multiple subjects to solve real-world problems, such as designing and coding an eco-friendly drone for environmental monitoring.

Assessment and Evaluation:
• Implement a mix of formative and summative assessments, including research reports, project presentations, coding challenges, and quizzes to evaluate students' skills and understanding.
Cross-Curricular Connections:

• Emphasize the interconnections between subjects, showing how math, science, technology, and engineering collaboratively solve complex real-world issues.
This grade 5 curriculum is designed to continue building students' STEM knowledge and critical thinking skills, preparing them for more advanced studies in AI, Robotics, Drone Science, Data Science, and Machine Learning in the following years. It encourages students to think critically, problem-solve, and apply their knowledge to real-world scenarios.