Best Coding and Robotics Curriculum for Primary Schools in Gujarat

Best Coding and Robotics Curriculum for Primary Schools in Gujarat

With the national roll-out of the National Education Policy (NEP 2020) and the National Curriculum Framework (NCF), schools across Gujarat have formally integrated Coding, Computational Thinking (CT), and Robotics into the standard academic curriculum for the 2026–27 session.

Rather than treating these modern subjects as voluntary after-school workshops, the updated structure ensures that logical building blocks and engineering frameworks are systematically introduced right from primary education.

1. Grade-Wise Pedagogical Breakdown

The dynamic curriculum adapts seamlessly to the mental development of the student, breaking the learning curve into three critical progression tiers:

Foundational & Preparatory Stage (Grades 3 to 5)

  • Core Focus: Block-Based Coding & Structural Logic.

  • Software Environments: Scratch, Blockly, and mBlock.

  • Key Concept: Students transition from consuming technology to creating it. Instead of typing complex syntax, they use visual "drag-and-drop" coding blocks to build animations, interactive stories, and basic gamified mathematics simulations.

  • Core Competency: Developing pattern recognition, algorithm decomposition, and spatial reasoning without the friction of programming syntax errors.

Middle School Stage (Grades 6 to 8)

  • Core Focus: Physical Computing, Open-Source Hardware, and Smart Sensors.

  • Platforms & Tools: Arduino UNO, Raspberry Pi Pico, and micro:bit.

  • Key Concept: Moving beyond the constraints of a software screen into tangible hardware execution. Students understand electrical pathways, the mechanics of breadboards, and basic input/output components.

  • Practical Work: Interfacing basic peripheral sensors (Ultrasonic, Infrared, Temperature) with processors. Students learn to command a microchip to execute tasks based on environmental inputs—for instance, designing a buzzer alarm that triggers if an object moves closer than 10 centimeters.

Secondary Stage (Grades 9 to 12)

  • Core Focus: Text-Based Programming, IoT, and Artificial Intelligence (AI).

  • Languages & Tech Frameworks: Python, Embedded C, and Machine Learning modules.

  • Key Concept: Deeply aligned with specialized board metrics (such as the CBSE Skill Subject 417 matrix). Students write structural text scripts to process real-time data, optimize motor configurations, and build fully autonomous hardware models.

  • Practical Work: Constructing advanced machinery like automated line-follower vehicles, mechanical arms, and Internet of Things (IoT) driven smart-home configurations that pull and execute weather data from cloud platforms.

2. Structured 24-Session Matrix (Academic Blueprint)

To maintain consistent milestones across terms, schools execute an optimized 24-session annual roadmap (roughly 1 session per week), focusing on project-based outcomes:

PhaseSessionsPrimary TopicHands-On Learning Objective
Phase I: Logic Base1 – 4Algorithmic ThinkingStructuring sequential statements, loops, and conditional arguments in block platforms.
Phase II: Hardware5 – 8Hardware & Embedded ElectronicsMaster breadboard logic, circuit paths, and basic digital/analog pin outputs.
Phase III: Inputs9 – 12Smart Sensor SystemsGathering sensory inputs (light, distance, moisture) to affect physical outcomes.
Phase IV: Actuation13 – 16Mechanical Movement ControlProgramming DC motors, servo motors, and stepper drives for precise handling.
Phase V: Advanced17 – 20Text Compilation & AutomationScripting structured lines in Python or Embedded C to execute complex functions.
Phase VI: Capstone21 – 24Full Project IntegrationCollaborating in groups to develop a functioning model like a Smart Waste Sorter or Solar Tracker.

3. Targeted Learning Outcomes (NCF Framework)

By deploying this systematic framework, educators evaluate student progress based on distinct cognitive and practical benchmarks rather than traditional memory tests:

[COMPUTATIONAL THINKING]
Break down large problems into logical blocks
[SYSTEM HARDWARE INTEGRATION]
Map digital commands to physical components
[SYSTEMATIC TROUBLESHOOTING]
Isolate, debug, and optimize execution errors
  • Algorithmic Formulation: The capacity to break an elaborate, chaotic real-world obstacle down into smaller, bite-sized components and formulate a step-by-step resolution path.

  • Hardware Competence: Eliminating fear around technical systems. Students intuitively grasp how automated machinery operates—understanding the explicit loop connecting code strings to moving parts.

  • Resilience via Debugging: Modifying the mindset around failure. When a circuit shorts or a code script crashes, students actively audit the layout, isolate variables, and systematically patch errors, cultivating deep persistence.

  • Interdisciplinary Synthesis: Fusing principles of spatial physics (gear ratios, load distributions) with analytical mathematics (variables, coordinates) to resolve complex design dilemmas.

4. Modern Lab Infrastructure Requirements

To facilitate these outcomes efficiently under modern classroom standards, schools deploy dedicated Innovation Spaces configured with the following technical resources:

  • Dedicated Workstations: Multi-core laptops or computers paired with modern visual platforms and compilation environments.

  • Virtual Simulation Toolkits: Utilizing open-source tools like Tinkercad Circuits to let students prototype, test, and safely verify complex wiring layouts online before applying physical currents.

  • Hardware Vaults: Managed storage containing shared robotics components, including breadboards, jump leads, microcontroller arrays, sensory modules, and safe mechanical assembly tools.

5. Overcoming Deployment Hurdles

While shifting to a highly practical framework poses clear logistical challenges—particularly upgrading historical computer rooms and upskilling regional staff—educational institutions across Gujarat are making rapid strides. Schools are partnering with specialized technological skill hubs and tech-driven universities to run intensive Capacity Building Programs for educators, ensuring that teachers are fully certified to manage and troubleshoot advanced robotics kits safely.

For a comprehensive look at how these classrooms are being built and deployed across schools in India, you can check out the AI and Robotics Lab Setup and Curriculum Overview. This video highlights real-world lab environments, student projects, and the physical kit integrations used to satisfy the NEP 2020 criteria. 

āŠ°ાāŠ·્āŠŸ્āŠ°ીāŠŊ āŠķિāŠ•્āŠ·āŠĢ āŠĻીāŠĪિ (NEP 2020) āŠ…āŠĻે āŠĻેāŠķāŠĻāŠē āŠ•āŠ°િāŠ•ુāŠēāŠŪ āŠŦ્āŠ°ેāŠŪāŠĩāŠ°્āŠ• (NCF) āŠĻા āŠ…āŠŪāŠēીāŠ•āŠ°āŠĢ āŠļાāŠĨે, āŠ—ુāŠœāŠ°ાāŠĪāŠĻા āŠķૈāŠ•્āŠ·āŠĢિāŠ• āŠŪાāŠģāŠ–ાāŠŪાં āŠāŠ• āŠŪોāŠŸો āŠ…āŠĻે āŠ•્āŠ°ાંāŠĪિāŠ•ાāŠ°ી āŠŽāŠĶāŠēાāŠĩ āŠ†āŠĩ્āŠŊો āŠ›ે. āŠĩāŠ°્āŠ· 2026-27 āŠĻા āŠķૈāŠ•્āŠ·āŠĢિāŠ• āŠļāŠĪ્āŠ°āŠĨી āŠ—ુāŠœāŠ°ાāŠĪāŠĻી āŠŠ્āŠ°ાāŠĨāŠŪિāŠ• āŠ…āŠĻે āŠŪાāŠ§્āŠŊāŠŪિāŠ• āŠķાāŠģાāŠ“āŠŪાં āŠ•ોāŠĄિંāŠ— āŠ…āŠĻે āŠ°ોāŠŽોāŠŸિāŠ•્āŠļ (Coding and Robotics Curriculum) āŠĻે āŠŪાāŠĪ્āŠ° āŠāŠ• āŠŠ્āŠ°āŠĩૃāŠĪ્āŠĪિ āŠĪāŠ°ીāŠ•ે āŠĻāŠđીં, āŠŠāŠ°ંāŠĪુ āŠŪુāŠ–્āŠŊ āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪāŠĻા āŠāŠ• āŠ…āŠĻિāŠĩાāŠ°્āŠŊ āŠ­ાāŠ— āŠĪāŠ°ીāŠ•ે āŠœોāŠĄāŠĩાāŠŪાં āŠ†āŠĩ્āŠŊો āŠ›ે.

āŠ† āŠ†āŠ§ુāŠĻિāŠ• āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪāŠĻો āŠŪુāŠ–્āŠŊ āŠđેāŠĪુ āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“āŠŪાં āŠĻાāŠĻી āŠ‰ંāŠŪāŠ°āŠĨી āŠœ āŠĪાāŠ°્āŠ•િāŠ• āŠĩિāŠšાāŠ°āŠļāŠ°āŠĢી (Logical Reasoning), āŠŠ્āŠ°ોāŠŽ્āŠēેāŠŪ-āŠļોāŠē્āŠĩિંāŠ— āŠļ્āŠ•ીāŠē્āŠļ (āŠļāŠŪāŠļ્āŠŊા āŠĻિāŠĩાāŠ°āŠĢ āŠ•ૌāŠķāŠē્āŠŊ) āŠ…āŠĻે āŦĻāŦ§āŠŪી āŠļāŠĶીāŠĻા āŠŸેāŠ•āŠĻિāŠ•āŠē āŠ•ૌāŠķāŠē્āŠŊોāŠĻો āŠĩિāŠ•ાāŠļ āŠ•āŠ°āŠĩાāŠĻો āŠ›ે.

āŦ§. āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪāŠĻું āŠŪાāŠģāŠ–ું (Structure of Curriculum)

āŠ—ુāŠœāŠ°ાāŠĪ āŠŽોāŠ°્āŠĄ (GSEB) āŠ…āŠĻે āŠķાāŠģાāŠ“ āŠĶ્āŠĩાāŠ°ા āŠ† āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪāŠĻે āŠŽાāŠģāŠ•āŠĻી āŠ‰ંāŠŪāŠ° āŠ…āŠĻે āŠŪાāŠĻāŠļિāŠ• āŠ•્āŠ·āŠŪāŠĪાāŠĻે āŠ§્āŠŊાāŠĻāŠŪાં āŠ°ાāŠ–ીāŠĻે āŠŪુāŠ–્āŠŊ āŠĪ્āŠ°āŠĢ āŠĪāŠŽāŠ•્āŠ•ાāŠŪાં āŠĩāŠđેંāŠšāŠĩાāŠŪાં āŠ†āŠĩ્āŠŊો āŠ›ે:

āŠ. āŠŠ્āŠ°ાāŠ°ંāŠ­િāŠ• āŠļ્āŠĪāŠ° (āŠ§ોāŠ°āŠĢ āŦĐ āŠĨી āŦŦ): āŠŽ્āŠēોāŠ•-āŠ†āŠ§ાāŠ°િāŠĪ āŠ•ોāŠĄિંāŠ— (Block-Based Coding)

āŠ† āŠļ્āŠĪāŠ°ે āŠŽાāŠģāŠ•ોāŠĻે āŠ…āŠ˜āŠ°ા āŠŸેāŠ•્āŠļ્āŠŸ āŠ•ોāŠĄિંāŠ—āŠĻે āŠŽāŠĶāŠēે āŠ°āŠŪāŠĪ-āŠ°āŠŪāŠĪ āŠļાāŠĨે āŠĩિāŠ્āŠŊુāŠ…āŠē āŠŠ્āŠ°ોāŠ—્āŠ°ાāŠŪિંāŠ— āŠķીāŠ–āŠĩāŠĩાāŠŪાં āŠ†āŠĩે āŠ›ે.

  • āŠŸૂāŠē્āŠļ: āŠļ્āŠ•્āŠ°ૅāŠš (Scratch), āŠŽ્āŠēોāŠ•āŠēી (Blockly) āŠ…āŠĻે āŠāŠŪāŠŽ્āŠēોāŠ• (mBlock).

  • āŠ…āŠ§્āŠŊāŠŊāŠĻ āŠŪુāŠĶ્āŠĶાāŠ“: āŠāŠĻિāŠŪેāŠķāŠĻ āŠŽāŠĻાāŠĩāŠĩું, āŠļાāŠĶી āŠ—ેāŠŪ્āŠļ āŠĄિāŠાāŠ‡āŠĻ āŠ•āŠ°āŠĩી, āŠ…āŠĻે 'āŠĄ્āŠ°ેāŠ— āŠāŠĻ્āŠĄ āŠĄ્āŠ°ોāŠŠ' āŠŠāŠĶ્āŠ§āŠĪિāŠĨી āŠ•ોāŠĄિંāŠ—āŠĻા āŠŪૂāŠģāŠ­ૂāŠĪ āŠĻિāŠŊāŠŪો (āŠœેāŠŪ āŠ•ે āŠēૂāŠŠ્āŠļ āŠ…āŠĻે āŠ•āŠĻ્āŠĄિāŠķāŠĻ્āŠļ) āŠļāŠŪāŠœāŠĩા.

āŠŽી. āŠŪāŠ§્āŠŊāŠŪ āŠļ્āŠĪāŠ° (āŠ§ોāŠ°āŠĢ āŦŽ āŠĨી āŦŪ): āŠ“āŠŠāŠĻ-āŠļોāŠ°્āŠļ āŠ‡āŠēેāŠ•્āŠŸ્āŠ°ોāŠĻિāŠ•્āŠļ āŠ…āŠĻે āŠļ્āŠŪાāŠ°્āŠŸ āŠļેāŠĻ્āŠļāŠ°્āŠļ

āŠ§ોāŠ°āŠĢ āŦŽ āŠĨી āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“ āŠ•āŠŪ્āŠŠ્āŠŊુāŠŸāŠ° āŠļ્āŠ•્āŠ°ીāŠĻāŠĻી āŠŽāŠđાāŠ° āŠĻીāŠ•āŠģીāŠĻે āŠĩાāŠļ્āŠĪāŠĩિāŠ• āŠđાāŠ°્āŠĄāŠĩેāŠ° āŠļાāŠĨે āŠ•ાāŠŪ āŠ•āŠ°āŠĩાāŠĻું āŠķāŠ°ૂ āŠ•āŠ°ે āŠ›ે.

  • āŠŸૂāŠē્āŠļ: āŠ†āŠ°્āŠĄુિāŠĻો (Arduino UNO), āŠ°ાāŠļ્āŠŠāŠŽેāŠ°ી āŠŠાāŠ‡ (Raspberry Pi Pico) āŠ…āŠĻે āŠŽેāŠિāŠ• āŠ‡āŠēેāŠ•્āŠŸ્āŠ°ોāŠĻિāŠ• āŠ•āŠŪ્āŠŠોāŠĻāŠĻ્āŠŸ્āŠļ.

  • āŠ…āŠ§્āŠŊāŠŊāŠĻ āŠŪુāŠĶ્āŠĶાāŠ“: āŠļેāŠĻ્āŠļāŠ°્āŠļ (āŠ…āŠē્āŠŸ્āŠ°ાāŠļોāŠĻિāŠ•, IR āŠļેāŠĻ્āŠļāŠ°, āŠĪાāŠŠāŠŪાāŠĻ āŠļેāŠĻ્āŠļāŠ°) āŠĻું āŠ‡āŠĻ્āŠŸāŠ°āŠŦેāŠļિંāŠ—, āŠāŠēāŠ‡āŠĄી (LED) āŠŽ્āŠēિંāŠ•િંāŠ— āŠ…āŠĻે āŠļાāŠĶા āŠ“āŠŸોāŠŪેāŠķāŠĻ āŠŠ્āŠ°ોāŠœેāŠ•્āŠŸ્āŠļ āŠŽāŠĻાāŠĩāŠĩા. āŠ† āŠļ્āŠĪāŠ°ે āŠŽ્āŠēોāŠ• āŠ•ોāŠĄિંāŠ—āŠŪાંāŠĨી āŠŸેāŠ•્āŠļ્āŠŸ-āŠ†āŠ§ાāŠ°િāŠĪ (C/C++ āŠĻા āŠļાāŠĶા āŠŦોāŠ°્āŠŪેāŠŸ) āŠĪāŠ°āŠŦ āŠļ્āŠĨāŠģાંāŠĪāŠ° āŠĨાāŠŊ āŠ›ે.

āŠļી. āŠ‰āŠš્āŠš āŠļ્āŠĪāŠ° (āŠ§ોāŠ°āŠĢ āŦŊ āŠĨી āŦ§āŦĻ): āŠāŠĄāŠĩાāŠĻ્āŠļ āŠ°ોāŠŽોāŠŸિāŠ•્āŠļ āŠ…āŠĻે āŠ†āŠ°્āŠŸિāŠŦિāŠķિāŠŊāŠē āŠ‡āŠĻ્āŠŸેāŠēિāŠœāŠĻ્āŠļ (AI)

āŠ† āŠĪāŠŽāŠ•્āŠ•ે āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“ āŠĩ્āŠŊાāŠĩāŠļાāŠŊિāŠ• āŠ•āŠ•્āŠ·ાāŠĻી āŠŸેāŠ•āŠĻોāŠēોāŠœી āŠ…āŠĻે āŠŠ્āŠ°ોāŠ—્āŠ°ાāŠŪિંāŠ— āŠēેંāŠ—્āŠĩેāŠœ āŠĪāŠ°āŠŦ āŠ†āŠ—āŠģ āŠĩāŠ§ે āŠ›ે.

  • āŠŸૂāŠē્āŠļ: āŠŠાāŠŊāŠĨોāŠĻ āŠŠ્āŠ°ોāŠ—્āŠ°ાāŠŪિંāŠ— (Python), āŠ†āŠŊોāŠŸી (IoT - Internet of Things) āŠŪોāŠĄ્āŠŊુāŠē્āŠļ āŠ…āŠĻે āŠāŠĄāŠĩાāŠĻ્āŠļ āŠ°ોāŠŽોāŠŸિāŠ• āŠ•િāŠŸ્āŠļ.

  • āŠ…āŠ§્āŠŊāŠŊāŠĻ āŠŪુāŠĶ્āŠĶાāŠ“: āŠēાāŠˆāŠĻ āŠŦોāŠēોāŠ…āŠ° āŠ°ોāŠŽોāŠŸ, āŠļ્āŠŪાāŠ°્āŠŸ āŠđોāŠŪ āŠļિāŠļ્āŠŸāŠŪ્āŠļ, āŠĄેāŠŸા āŠļાāŠŊāŠĻ્āŠļāŠĻા āŠŽેāŠિāŠ•્āŠļ āŠ…āŠĻે āŠŪāŠķીāŠĻ āŠēāŠ°્āŠĻિંāŠ— (Machine Learning) āŠĻા āŠŪોāŠĄāŠē્āŠļāŠĻો āŠŠāŠ°િāŠšāŠŊ.

āŦĻ. āŠ‡āŠĻ્āŠŦ્āŠ°ાāŠļ્āŠŸ્āŠ°āŠ•્āŠšāŠ° āŠ…āŠĻે āŠēેāŠŽ āŠļેāŠŸāŠ…āŠŠ (Lab Infrastructure)

āŠ•ોāŠĄિંāŠ— āŠ…āŠĻે āŠ°ોāŠŽોāŠŸિāŠ•્āŠļāŠĻા āŠŠ્āŠ°ાāŠŊોāŠ—િāŠ• āŠœ્āŠžાāŠĻ āŠŪાāŠŸે āŠ—ુāŠœāŠ°ાāŠĪāŠĻી āŠķાāŠģાāŠ“āŠŪાં āŠ–ાāŠļ āŠ‡āŠĻોāŠĩેāŠķāŠĻ āŠēેāŠŽ્āŠļ (Innovation Labs) āŠļ્āŠĨાāŠŠāŠĩાāŠŪાં āŠ†āŠĩી āŠ°āŠđી āŠ›ે.

  • āŠļ્āŠŪાāŠ°્āŠŸ āŠŠ્āŠ°ોāŠœેāŠ•્āŠŸāŠ° āŠ…āŠĻે āŠĄિāŠļ્āŠŠ્āŠēે: āМેāŠĻી āŠŪāŠĶāŠĶāŠĨી āŠķિāŠ•્āŠ·āŠ•ો āŠœāŠŸિāŠē āŠļāŠ°્āŠ•િāŠŸ āŠĄાāŠŊાāŠ—્āŠ°ાāŠŪ āŠ…āŠĻે āŠ•ોāŠĄિંāŠ— āŠ†āŠ°્āŠ•િāŠŸેāŠ•્āŠšāŠ° āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“āŠĻે āŠļāŠ°āŠģāŠĪાāŠĨી āŠĩિāŠ્āŠŊુāŠ…āŠēાāŠˆāŠ āŠ•āŠ°ાāŠĩી āŠķāŠ•ે.

  • āŠđાāŠ°્āŠĄāŠĩેāŠ° āŠļ્āŠŸેāŠķāŠĻ્āŠļ: āŠĶāŠ°ેāŠ• āŠēેāŠŽāŠŪાં āŠŠૂāŠ°āŠĪી āŠŪાāŠĪ્āŠ°ાāŠŪાં āŠļેāŠĻ્āŠļāŠ°્āŠļ, āŠļāŠ°્āŠĩો āŠŪોāŠŸāŠ°્āŠļ, āŠŽ્āŠ°ેāŠĄāŠŽોāŠ°્āŠĄ્āŠļ, āŠœāŠŪ્āŠŠāŠ° āŠĩાāŠŊāŠ°્āŠļ āŠ…āŠĻે āŠ•āŠŪ્āŠŠ્āŠŊુāŠŸāŠ°્āŠļ/āŠēેāŠŠāŠŸોāŠŠ્āŠļ āŠ‰āŠŠāŠēāŠŽ્āŠ§ āŠ•āŠ°ાāŠĩāŠĩાāŠŪાં āŠ†āŠĩે āŠ›ે.

  • āŠĩāŠ°્āŠš્āŠŊુāŠ…āŠē āŠļિāŠŪ્āŠŊુāŠēેāŠŸāŠ°્āŠļ: āŠ­ૌāŠĪિāŠ• āŠļાāŠ§āŠĻો āŠĻ āŠŽāŠ—āŠĄે āŠĪે āŠŪાāŠŸે āŠķāŠ°ૂāŠ†āŠĪāŠŪાં Tinkercad āМેāŠĩા āŠ“āŠĻāŠēાāŠˆāŠĻ āŠļિāŠŪ્āŠŊુāŠēેāŠŸāŠ° āŠŸૂāŠē્āŠļ āŠŠāŠ° āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“ āŠļāŠ°્āŠ•િāŠŸāŠĻું āŠŸેāŠļ્āŠŸિંāŠ— āŠ•āŠ°āŠĪા āŠķીāŠ–ે āŠ›ે.

āŦĐ. āŦĻāŦŠ-āŠļેāŠķāŠĻ āŠŠ્āŠēાāŠĻિંāŠ— (A Sample 24-Session Matrix)

āŠķાāŠģાāŠ“āŠŪાં āŠ†āŠ–ા āŠĩāŠ°્āŠ· āŠĶāŠ°āŠŪિāŠŊાāŠĻ āŠĩ્āŠŊāŠĩāŠļ્āŠĨિāŠĪ āŠķિāŠ•્āŠ·āŠĢ āŠ†āŠŠāŠĩા āŠŪાāŠŸે āŦĻāŦŠ āŠļેāŠķāŠĻāŠĻું āŠāŠ• āŠ†āŠĶāŠ°્āŠķ āŠŪાāŠģāŠ–ું āŠĻીāŠšે āŠŪુāŠœāŠŽ āŠ•ોāŠ·્āŠŸāŠ•āŠŪાં āŠĶāŠ°્āŠķાāŠĩેāŠē āŠ›ે:

āŠļેāŠķāŠĻ āŠ•્āŠ°āŠŪāŠŪુāŠ–્āŠŊ āŠĩિāŠ·āŠŊ / āŠŸોāŠŠિāŠ•āŠŠ્āŠ°ાāŠŊોāŠ—િāŠ• āŠŠ્āŠ°āŠĩૃāŠĪ્āŠĪિ (Hands-on Activity)
1 - 4āŠ•ોāŠĄિંāŠ— āŠ…āŠĻે āŠēોāŠœિāŠ•āŠĻા āŠŪૂāŠģāŠ­ૂāŠĪ āŠļિāŠĶ્āŠ§ાંāŠĪોāŠļ્āŠ•્āŠ°ૅāŠš (Scratch) āŠŪાં āŠĩાāŠ°્āŠĪા āŠ…āŠĻે āŠāŠĻિāŠŪેāŠķāŠĻ āŠŽāŠĻાāŠĩāŠĩું.
5 - 8āŠ‡āŠēેāŠ•્āŠŸ્āŠ°ોāŠĻિāŠ•્āŠļ āŠ…āŠĻે āŠ†āŠ°્āŠĄુિāŠĻોāŠĻો āŠŠāŠ°િāŠšāŠŊArduino āŠŽોāŠ°્āŠĄ āŠļાāŠĨે LED āŠēાāŠ‡āŠŸāŠĻે āŠ•ંāŠŸ્āŠ°ોāŠē āŠ•āŠ°āŠĩી (Blinking).
9 - 12āŠļેāŠĻ્āŠļāŠ°્āŠļ āŠļાāŠĨે āŠ•ાāŠŪ āŠ•āŠ°āŠĩું (Inputs)āŠ…āŠē્āŠŸ્āŠ°ાāŠļોāŠĻિāŠ• āŠļેāŠĻ્āŠļāŠ°āŠĻી āŠŪāŠĶāŠĶāŠĨી āŠ…ંāŠĪāŠ° āŠŪાāŠŠāŠĩાāŠĻું āŠļાāŠ§āŠĻ āŠŽāŠĻાāŠĩāŠĩું.
13 - 16āŠŪોāŠŸāŠ°્āŠļ āŠ…āŠĻે āŠāŠ•્āŠŸ્āŠŊુāŠāŠŸāŠ°્āŠļ (Outputs)āŠļāŠ°્āŠĩો āŠŪોāŠŸāŠ° (Servo Motor) āŠĻો āŠ‰āŠŠāŠŊોāŠ— āŠ•āŠ°ી āŠļ્āŠŪાāŠ°્āŠŸ āŠ—ેāŠŸ āŠŽāŠĻાāŠĩāŠĩો.
17 - 20āŠŠાāŠŊāŠĨોāŠĻ āŠ…āŠĻે āŠāŠĄāŠĩાāŠĻ્āŠļ āŠ•ોāŠĄિંāŠ—āŠŠાāŠŊāŠĨોāŠĻ āŠēેંāŠ—્āŠĩેāŠœāŠŪાં āŠļાāŠĶું āŠ•ેāŠē્āŠ•્āŠŊુāŠēેāŠŸāŠ° āŠ…āŠĻે āŠēોāŠœિāŠ•āŠē āŠŠ્āŠ°ોāŠ—્āŠ°ાāŠŪ્āŠļ āŠēāŠ–āŠĩા.
21 - 24āŠ•ેāŠŠāŠļ્āŠŸોāŠĻ āŠŠ્āŠ°ોāŠœેāŠ•્āŠŸ (Capstone Project)āŠļ્āŠŪાāŠ°્āŠŸ āŠĄāŠļ્āŠŸāŠŽિāŠĻ āŠ…āŠĨāŠĩા āŠ“āŠŸોāŠŪેāŠŸિāŠ• āŠŠ્āŠēાāŠĻ્āŠŸ āŠĩોāŠŸāŠ°િંāŠ— āŠļિāŠļ્āŠŸāŠŪ āŠŽāŠĻાāŠĩāŠĩી.

āŦŠ. āŠŪુāŠ–્āŠŊ āŠ…āŠ§્āŠŊāŠŊāŠĻ āŠĻિāŠ·્āŠŠāŠĪ્āŠĪિāŠ“ (Expected Learning Outcomes)

āŠ† āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪ āŠŠૂāŠ°્āŠĢ āŠ•āŠ°્āŠŊા āŠŠāŠ›ી, āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“āŠŪાં āŠĻીāŠšે āŠŪુāŠœāŠŽāŠĻા āŠšોāŠ•્āŠ•āŠļ āŠŠāŠ°િāŠĢાāŠŪો (Outcomes) āŠ…āŠŠેāŠ•્āŠ·િāŠĪ āŠ›ે:

  • āŠ•āŠŪ્āŠŠ્āŠŊુāŠŸેāŠķāŠĻāŠē āŠĨિંāŠ•િંāŠ— (Computational Thinking): āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“ āŠ•ોāŠˆāŠŠāŠĢ āŠŪોāŠŸી āŠļāŠŪāŠļ્āŠŊાāŠĻે āŠĻાāŠĻા-āŠĻાāŠĻા āŠ­ાāŠ—ોāŠŪાં āŠĩāŠđેંāŠšી (Decomposition) āŠĪેāŠĻું āŠĪાāŠ°્āŠ•િāŠ• āŠļોāŠē્āŠŊુāŠķāŠĻ āŠķોāŠ§ી āŠķāŠ•āŠķે.

  • āŠđાāŠ°્āŠĄāŠĩેāŠ° āŠ…āŠĻે āŠļોāŠŦ્āŠŸāŠĩેāŠ°āŠĻું āŠœોāŠĄાāŠĢ: āŠŽાāŠģāŠ• āŠŪાāŠĪ્āŠ° āŠ—ેāŠŪ્āŠļ āŠ°āŠŪāŠĩાāŠĻે āŠŽāŠĶāŠēે āŠļāŠŪāŠœી āŠķāŠ•āŠķે āŠ•ે āŠļોāŠŦ્āŠŸāŠĩેāŠ° āŠ•ોāŠĄ āŠĶ્āŠĩાāŠ°ા āŠŦિāŠિāŠ•āŠē āŠđાāŠ°્āŠĄāŠĩેāŠ° (āŠœેāŠŪ āŠ•ે āŠ°ોāŠŽોāŠŸિāŠ• āŠ†āŠ°્āŠŪ) āŠĻે āŠ•ેāŠĩી āŠ°ીāŠĪે āŠ•āŠŪાāŠĻ્āŠĄ āŠ†āŠŠી āŠķāŠ•ાāŠŊ.

  • āŠ­ૂāŠēો āŠķોāŠ§āŠĩાāŠĻી āŠ•્āŠ·āŠŪāŠĪા (Debugging): āŠ•ોāŠĄāŠŪાં āŠ•ે āŠļāŠ°્āŠ•િāŠŸāŠŪાં āŠ†āŠĩāŠĪી āŠ­ૂāŠēોāŠĻે āŠœાāŠĪે āŠķોāŠ§ીāŠĻે āŠĪેāŠĻે āŠļુāŠ§ાāŠ°āŠĩાāŠĻી āŠ•્āŠ·āŠŪāŠĪા āŠ•ેāŠģāŠĩાāŠķે, āŠœેāŠĻાāŠĨી āŠ§ૈāŠ°્āŠŊ āŠ…āŠĻે āŠ†āŠĪ્āŠŪāŠĩિāŠķ્āŠĩાāŠļ āŠĩāŠ§āŠķે.

  • āŠŸીāŠŪāŠĩāŠ°્āŠ• āŠ…āŠĻે āŠ‡āŠĻોāŠĩેāŠķāŠĻ: āŠ—્āŠ°ુāŠŠ āŠŠ્āŠ°ોāŠœેāŠ•્āŠŸ્āŠļ āŠĶ્āŠĩાāŠ°ા āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“ āŠļાāŠĨે āŠŪāŠģીāŠĻે āŠ•ાāŠŪ āŠ•āŠ°āŠĪા āŠķીāŠ–āŠķે āŠ…āŠĻે āŠĩાāŠļ્āŠĪāŠĩિāŠ• āŠœીāŠĩāŠĻāŠĻી āŠļāŠŪāŠļ્āŠŊાāŠ“ (āŠœેāŠŪ āŠ•ે āŠŸ્āŠ°ાāŠŦિāŠ• āŠ•ંāŠŸ્āŠ°ોāŠē, āŠĩેāŠļ્āŠŸ āŠŪેāŠĻેāŠœāŠŪેāŠĻ્āŠŸ) āŠĻા āŠŸેāŠ•āŠĻિāŠ•āŠē āŠ‰āŠŠાāŠŊો āŠĩિāŠšાāŠ°āŠķે.

āŦŦ. āŠŠāŠĄāŠ•ાāŠ°ો āŠ…āŠĻે āŠ­āŠĩિāŠ·્āŠŊāŠĻી āŠĶિāŠķા (Challenges & Future Road)

āŠ—ુāŠœāŠ°ાāŠĪāŠŪાં āŠ† āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪāŠĻા āŠļāŠŦāŠģ āŠ…āŠŪāŠēીāŠ•āŠ°āŠĢ āŠļાāŠŪે āŠ•ેāŠŸāŠēાāŠ• āŠŠāŠĄāŠ•ાāŠ°ો āŠŠāŠĢ āŠ›ે, āŠœેāŠŪ āŠ•ે āŠ—્āŠ°ાāŠŪીāŠĢ āŠĩિāŠļ્āŠĪાāŠ°ોāŠŪાં āŠ‡āŠĻ્āŠŦ્āŠ°ાāŠļ્āŠŸ્āŠ°āŠ•્āŠšāŠ° āŠŠāŠđોંāŠšાāŠĄāŠĩું āŠ…āŠĻે āŠķિāŠ•્āŠ·āŠ•ોāŠĻે āŠ† āŠĻāŠĩી āŠŸેāŠ•āŠĻોāŠēોāŠœી āŠŪાāŠŸે āŠļāŠœ્āŠœ āŠ•āŠ°āŠĩા. āІ āŠŪાāŠŸે āŠ—ુāŠœāŠ°ાāŠĪ āŠļāŠ°āŠ•ાāŠ° āŠ…āŠĻે āŠļ્āŠ•ેāŠēિંāŠ— āŠļંāŠļ્āŠĨાāŠ“ (āŠœેāŠŪ āŠ•ે STEMpediaKaushalya The Skills University) āŠĶ્āŠĩાāŠ°ા āŠŪોāŠŸા āŠŠાāŠŊે 'āŠŸીāŠšāŠ°્āŠļ āŠŸ્āŠ°ેāŠĻિંāŠ— āŠŠ્āŠ°ોāŠ—્āŠ°ાāŠŪ્āŠļ' āŠšāŠēાāŠĩāŠĩાāŠŪાં āŠ†āŠĩી āŠ°āŠđ્āŠŊા āŠ›ે.

āŠĻિāŠ·્āŠ•āŠ°્āŠ·: āŠĩāŠ°્āŠ· 2026-27 āŠĻો āŠ† āŠĻāŠĩો āŠ•ોāŠĄિંāŠ— āŠ…āŠĻે āŠ°ોāŠŽોāŠŸિāŠ•્āŠļ āŠ…āŠ­્āŠŊાāŠļāŠ•્āŠ°āŠŪ āŠ—ુāŠœāŠ°ાāŠĪāŠĻા āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ“āŠĻે āŠŪાāŠĪ્āŠ° āŠĄિāŠœિāŠŸāŠē āŠ—્āŠ°ાāŠđāŠ• (Consumers) āŠŪાંāŠĨી āŠŽāŠĶāŠēીāŠĻે āŠĩૈāŠķ્āŠĩિāŠ• āŠļ્āŠĪāŠ°āŠĻા āŠĄિāŠœિāŠŸāŠē āŠļāŠ°્āŠœāŠ• (Creators) āŠŽāŠĻાāŠĩāŠķે. āŠ† āŠ­āŠĩિāŠ·્āŠŊāŠĻા āŠļ્āŠŸાāŠ°્āŠŸāŠ…āŠŠ્āŠļ, āŠāŠĻ્āŠœિāŠĻિāŠŊāŠ°િંāŠ— āŠ…āŠĻે āŠ‡āŠĻોāŠĩેāŠķāŠĻ āŠŪાāŠŸેāŠĻો āŠŪāŠœāŠŽૂāŠĪ āŠŠાāŠŊો āŠ›ે.

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