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:
| Phase | Sessions | Primary Topic | Hands-On Learning Objective |
| Phase I: Logic Base | 1 – 4 | Algorithmic Thinking | Structuring sequential statements, loops, and conditional arguments in block platforms. |
| Phase II: Hardware | 5 – 8 | Hardware & Embedded Electronics | Master breadboard logic, circuit paths, and basic digital/analog pin outputs. |
| Phase III: Inputs | 9 – 12 | Smart Sensor Systems | Gathering sensory inputs (light, distance, moisture) to affect physical outcomes. |
| Phase IV: Actuation | 13 – 16 | Mechanical Movement Control | Programming DC motors, servo motors, and stepper drives for precise handling. |
| Phase V: Advanced | 17 – 20 | Text Compilation & Automation | Scripting structured lines in Python or Embedded C to execute complex functions. |
| Phase VI: Capstone | 21 – 24 | Full Project Integration | Collaborating 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
āŠ°ાāŠ·્āŠ્āŠ°ીāŠŊ āŠķિāŠ્āŠ·āŠĢ āŠĻીāŠĪિ (NEP 2020) āŠ āŠĻે āŠĻેāŠķāŠĻāŠē āŠāаિāŠુāŠēāŠŪ āŠŦ્āŠ°ેāŠŪāŠĩāŠ°્āŠ (NCF) āŠĻા āŠ āŠŪāŠēીāŠāаāŠĢ āŠļાāŠĨે, āŠુāŠāаાāŠĪāŠĻા āŠķૈāŠ્āŠ·āŠĢિāŠ āŠŪાāŠģāŠાāŠŪાં āŠāŠ āŠŪોāŠો āŠ āŠĻે āŠ્āŠ°ાંāŠĪિāŠાāŠ°ી āŠŽāŠĶāŠēાāŠĩ āŠāŠĩ્āŠŊો āŠે. āŠĩāŠ°્āŠ· 2026-27 āŠĻા āŠķૈāŠ્āŠ·āŠĢિāŠ āŠļāŠĪ્āŠ°āŠĨી āŠુāŠāаાāŠĪāŠĻી āŠŠ્āŠ°ાāŠĨāŠŪિāŠ āŠ āŠĻે āŠŪાāŠ§્āŠŊāŠŪિāŠ āŠķાāŠģાāŠāŠŪાં āŠોāŠĄિંāŠ āŠ āŠĻે āŠ°ોāŠŽોāŠિāŠ્āŠļ (Coding and Robotics Curriculum) āŠĻે āŠŪાāŠĪ્āŠ° āŠāŠ āŠŠ્āŠ°āŠĩૃāŠĪ્āŠĪિ āŠĪāŠ°ીāŠે āŠĻāŠđીં, āŠŠāŠ°ંāŠĪુ āŠŪુāŠ્āŠŊ āŠ āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪāŠĻા āŠāŠ āŠ āŠĻિāŠĩાāŠ°્āŠŊ āŠાāŠ āŠĪāŠ°ીāŠે āŠોāŠĄāŠĩાāŠŪાં āŠāŠĩ્āŠŊો āŠે.
āŠ āŠāЧુāŠĻિāŠ āŠ āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪāŠĻો āŠŪુāŠ્āŠŊ āŠđેāŠĪુ āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠāŠŪાં āŠĻાāŠĻી āŠંāŠŪāŠ°āŠĨી āŠ āŠĪાāŠ°્āŠિāŠ āŠĩિāŠાāŠ°āŠļāŠ°āŠĢી (Logical Reasoning), āŠŠ્āŠ°ોāŠŽ્āŠēેāŠŪ-āŠļોāŠē્āŠĩિંāŠ āŠļ્āŠીāŠē્āŠļ (āŠļāŠŪāŠļ્āŠŊા āŠĻિāŠĩાāŠ°āŠĢ āŠૌāŠķāŠē્āŠŊ) āŠ āŠĻે āŦĻāŦ§āŠŪી āŠļāŠĶીāŠĻા āŠેāŠāŠĻિāŠāŠē āŠૌāŠķāŠē્āŠŊોāŠĻો āŠĩિāŠાāŠļ āŠāаāŠĩાāŠĻો āŠે.
āŦ§. āŠ āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪāŠĻું āŠŪાāŠģāŠું (Structure of Curriculum)
āŠુāŠāаાāŠĪ āŠŽોāŠ°્āŠĄ (GSEB) āŠ āŠĻે āŠķાāŠģાāŠ āŠĶ્āŠĩાāŠ°ા āŠ āŠ āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪāŠĻે āŠŽાāŠģāŠāŠĻી āŠંāŠŪāŠ° āŠ āŠĻે āŠŪાāŠĻāŠļિāŠ āŠ્āŠ·āŠŪāŠĪાāŠĻે āŠ§્āŠŊાāŠĻāŠŪાં āŠ°ાāŠીāŠĻે āŠŪુāŠ્āŠŊ āŠĪ્āŠ°āŠĢ āŠĪāŠŽāŠ્āŠાāŠŪાં āŠĩāŠđેંāŠāŠĩાāŠŪાં āŠāŠĩ્āŠŊો āŠે:
āŠ. āŠŠ્āŠ°ાāŠ°ંāŠિāŠ āŠļ્āŠĪāŠ° (āŠ§ોāŠ°āŠĢ āŦĐ āŠĨી āŦŦ): āŠŽ્āŠēોāŠ-āŠāЧાāŠ°િāŠĪ āŠોāŠĄિંāŠ (Block-Based Coding)
āŠ āŠļ્āŠĪāŠ°ે āŠŽાāŠģāŠોāŠĻે āŠ āŠāаા āŠેāŠ્āŠļ્āŠ āŠોāŠĄિંāŠāŠĻે āŠŽāŠĶāŠēે āŠ°āŠŪāŠĪ-āŠ°āŠŪāŠĪ āŠļાāŠĨે āŠĩિāŠ્āŠŊુāŠ āŠē āŠŠ્āŠ°ોāŠ્āŠ°ાāŠŪિંāŠ āŠķીāŠāŠĩāŠĩાāŠŪાં āŠāŠĩે āŠે.
āŠૂāŠē્āŠļ: āŠļ્āŠ્āŠ°ૅāŠ (Scratch), āŠŽ્āŠēોāŠāŠēી (Blockly) āŠ āŠĻે āŠāŠŪāŠŽ્āŠēોāŠ (mBlock).
āŠ āŠ§્āŠŊāŠŊāŠĻ āŠŪુāŠĶ્āŠĶાāŠ: āŠāŠĻિāŠŪેāŠķāŠĻ āŠŽāŠĻાāŠĩāŠĩું, āŠļાāŠĶી āŠેāŠŪ્āŠļ āŠĄિāŠાāŠāŠĻ āŠāаāŠĩી, āŠ āŠĻે 'āŠĄ્āŠ°ેāŠ āŠāŠĻ્āŠĄ āŠĄ્āŠ°ોāŠŠ' āŠŠāŠĶ્āŠ§āŠĪિāŠĨી āŠોāŠĄિંāŠāŠĻા āŠŪૂāŠģāŠૂāŠĪ āŠĻિāŠŊāŠŪો (āŠેāŠŪ āŠે āŠēૂāŠŠ્āŠļ āŠ āŠĻે āŠāŠĻ્āŠĄિāŠķāŠĻ્āŠļ) āŠļāŠŪāŠāŠĩા.
āŠŽી. āŠŪāŠ§્āŠŊāŠŪ āŠļ્āŠĪāŠ° (āŠ§ોāŠ°āŠĢ āŦŽ āŠĨી āŦŪ): āŠāŠŠāŠĻ-āŠļોāŠ°્āŠļ āŠāŠēેāŠ્āŠ્āŠ°ોāŠĻિāŠ્āŠļ āŠ āŠĻે āŠļ્āŠŪાāŠ°્āŠ āŠļેāŠĻ્āŠļāŠ°્āŠļ
āŠ§ોāŠ°āŠĢ āŦŽ āŠĨી āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ āŠāŠŪ્āŠŠ્āŠŊુāŠāа āŠļ્āŠ્āŠ°ીāŠĻāŠĻી āŠŽāŠđાāŠ° āŠĻીāŠāŠģીāŠĻે āŠĩાāŠļ્āŠĪāŠĩિāŠ āŠđાāŠ°્āŠĄāŠĩેāŠ° āŠļાāŠĨે āŠાāŠŪ āŠāаāŠĩાāŠĻું āŠķāŠ°ૂ āŠāаે āŠે.
āŠૂāŠē્āŠļ: āŠāа્āŠĄુિāŠĻો (Arduino UNO), āŠ°ાāŠļ્āŠŠāŠŽેāŠ°ી āŠŠાāŠ (Raspberry Pi Pico) āŠ āŠĻે āŠŽેāŠિāŠ āŠāŠēેāŠ્āŠ્āŠ°ોāŠĻિāŠ āŠāŠŪ્āŠŠોāŠĻāŠĻ્āŠ્āŠļ.
āŠ āŠ§્āŠŊāŠŊāŠĻ āŠŪુāŠĶ્āŠĶાāŠ: āŠļેāŠĻ્āŠļāŠ°્āŠļ (āŠ āŠē્āŠ્āŠ°ાāŠļોāŠĻિāŠ, IR āŠļેāŠĻ્āŠļāŠ°, āŠĪાāŠŠāŠŪાāŠĻ āŠļેāŠĻ્āŠļāŠ°) āŠĻું āŠāŠĻ્āŠāаāŠŦેāŠļિંāŠ, āŠāŠēāŠāŠĄી (LED) āŠŽ્āŠēિંāŠિંāŠ āŠ āŠĻે āŠļાāŠĶા āŠāŠોāŠŪેāŠķāŠĻ āŠŠ્āŠ°ોāŠેāŠ્āŠ્āŠļ āŠŽāŠĻાāŠĩāŠĩા. āŠ āŠļ્āŠĪāŠ°ે āŠŽ્āŠēોāŠ āŠોāŠĄિંāŠāŠŪાંāŠĨી āŠેāŠ્āŠļ્āŠ-āŠāЧાāŠ°િāŠĪ (C/C++ āŠĻા āŠļાāŠĶા āŠŦોāŠ°્āŠŪેāŠ) āŠĪāŠ°āŠŦ āŠļ્āŠĨāŠģાંāŠĪāŠ° āŠĨાāŠŊ āŠે.
āŠļી. āŠāŠ્āŠ āŠļ્āŠĪāŠ° (āŠ§ોāŠ°āŠĢ āŦŊ āŠĨી āŦ§āŦĻ): āŠāŠĄāŠĩાāŠĻ્āŠļ āŠ°ોāŠŽોāŠિāŠ્āŠļ āŠ āŠĻે āŠāа્āŠિāŠŦિāŠķિāŠŊāŠē āŠāŠĻ્āŠેāŠēિāŠāŠĻ્āŠļ (AI)
āŠ āŠĪāŠŽāŠ્āŠે āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ āŠĩ્āŠŊાāŠĩāŠļાāŠŊિāŠ āŠāŠ્āŠ·ાāŠĻી āŠેāŠāŠĻોāŠēોāŠી āŠ āŠĻે āŠŠ્āŠ°ોāŠ્āŠ°ાāŠŪિંāŠ āŠēેંāŠ્āŠĩેāŠ āŠĪāŠ°āŠŦ āŠāŠāŠģ āŠĩāŠ§ે āŠે.
āŠૂāŠē્āŠļ: āŠŠાāŠŊāŠĨોāŠĻ āŠŠ્āŠ°ોāŠ્āŠ°ાāŠŪિંāŠ (Python), āŠāŠŊોāŠી (IoT - Internet of Things) āŠŪોāŠĄ્āŠŊુāŠē્āŠļ āŠ āŠĻે āŠāŠĄāŠĩાāŠĻ્āŠļ āŠ°ોāŠŽોāŠિāŠ āŠિāŠ્āŠļ.
āŠ āŠ§્āŠŊāŠŊāŠĻ āŠŪુāŠĶ્āŠĶાāŠ: āŠēાāŠāŠĻ āŠŦોāŠēોāŠ āŠ° āŠ°ોāŠŽોāŠ, āŠļ્āŠŪાāŠ°્āŠ āŠđોāŠŪ āŠļિāŠļ્āŠāŠŪ્āŠļ, āŠĄેāŠા āŠļાāŠŊāŠĻ્āŠļāŠĻા āŠŽેāŠિāŠ્āŠļ āŠ āŠĻે āŠŪāŠķીāŠĻ āŠēāŠ°્āŠĻિંāŠ (Machine Learning) āŠĻા āŠŪોāŠĄāŠē્āŠļāŠĻો āŠŠāŠ°િāŠāŠŊ.
āŦĻ. āŠāŠĻ્āŠŦ્āŠ°ાāŠļ્āŠ્āŠ°āŠ્āŠāа āŠ āŠĻે āŠēેāŠŽ āŠļેāŠāŠ āŠŠ (Lab Infrastructure)
āŠોāŠĄિંāŠ āŠ
āŠĻે āŠ°ોāŠŽોāŠિāŠ્āŠļāŠĻા āŠŠ્āŠ°ાāŠŊોāŠિāŠ āŠ્āŠાāŠĻ āŠŪાāŠે āŠુāŠāаાāŠĪāŠĻી āŠķાāŠģાāŠāŠŪાં āŠાāŠļ āŠāŠĻોāŠĩેāŠķāŠĻ āŠēેāŠŽ્āŠļ (Innovation Labs) āŠļ્āŠĨાāŠŠāŠĩાāŠŪાં āŠāŠĩી āŠ°āŠđી āŠે.
āŠļ્āŠŪાāŠ°્āŠ āŠŠ્āŠ°ોāŠેāŠ્āŠāа āŠ āŠĻે āŠĄિāŠļ્āŠŠ્āŠēે: āŠેāŠĻી āŠŪāŠĶāŠĶāŠĨી āŠķિāŠ્āŠ·āŠો āŠāŠિāŠē āŠļāŠ°્āŠિāŠ āŠĄાāŠŊાāŠ્āŠ°ાāŠŪ āŠ āŠĻે āŠોāŠĄિંāŠ āŠāа્āŠિāŠેāŠ્āŠāа āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠāŠĻે āŠļāŠ°āŠģāŠĪાāŠĨી āŠĩિāŠ્āŠŊુāŠ āŠēાāŠāŠ āŠāаાāŠĩી āŠķāŠે.
āŠđાāŠ°્āŠĄāŠĩેāŠ° āŠļ્āŠેāŠķāŠĻ્āŠļ: āŠĶāŠ°ેāŠ āŠēેāŠŽāŠŪાં āŠŠૂāŠ°āŠĪી āŠŪાāŠĪ્āŠ°ાāŠŪાં āŠļેāŠĻ્āŠļāŠ°્āŠļ, āŠļāŠ°્āŠĩો āŠŪોāŠāа્āŠļ, āŠŽ્āŠ°ેāŠĄāŠŽોāŠ°્āŠĄ્āŠļ, āŠāŠŪ્āŠŠāŠ° āŠĩાāŠŊāŠ°્āŠļ āŠ āŠĻે āŠāŠŪ્āŠŠ્āŠŊુāŠāа્āŠļ/āŠēેāŠŠāŠોāŠŠ્āŠļ āŠāŠŠāŠēāŠŽ્āŠ§ āŠāаાāŠĩāŠĩાāŠŪાં āŠāŠĩે āŠે.
āŠĩāŠ°્āŠ્āŠŊુāŠ āŠē āŠļિāŠŪ્āŠŊુāŠēેāŠāа્āŠļ: āŠૌāŠĪિāŠ āŠļાāŠ§āŠĻો āŠĻ āŠŽāŠāŠĄે āŠĪે āŠŪાāŠે āŠķāŠ°ૂāŠāŠĪāŠŪાં Tinkercad āŠેāŠĩા āŠāŠĻāŠēાāŠāŠĻ āŠļિāŠŪ્āŠŊુāŠēેāŠāа āŠૂāŠē્āŠļ āŠŠāŠ° āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ āŠļāŠ°્āŠિāŠāŠĻું āŠેāŠļ્āŠિંāŠ āŠāаāŠĪા āŠķીāŠે āŠે.
āŦĐ. āŦĻāŦŠ-āŠļેāŠķāŠĻ āŠŠ્āŠēાāŠĻિંāŠ (A Sample 24-Session Matrix)
āŠķાāŠģાāŠāŠŪાં āŠāŠા āŠĩāŠ°્āŠ· āŠĶāŠ°āŠŪિāŠŊાāŠĻ āŠĩ્āŠŊāŠĩāŠļ્āŠĨિāŠĪ āŠķિāŠ્āŠ·āŠĢ āŠāŠŠāŠĩા āŠŪાāŠે āŦĻāŦŠ āŠļેāŠķāŠĻāŠĻું āŠāŠ āŠāŠĶāŠ°્āŠķ āŠŪાāŠģāŠું āŠĻીāŠે āŠŪુāŠāŠŽ āŠોāŠ·્āŠāŠāŠŪાં āŠĶāŠ°્āŠķાāŠĩેāŠē āŠે:
| āŠļેāŠķāŠĻ āŠ્āŠ°āŠŪ | āŠŪુāŠ્āŠŊ āŠĩિāŠ·āŠŊ / āŠોāŠŠિāŠ | āŠŠ્āŠ°ાāŠŊોāŠિāŠ āŠŠ્āŠ°āŠĩૃāŠĪ્āŠĪિ (Hands-on Activity) |
| 1 - 4 | āŠોāŠĄિંāŠ āŠ āŠĻે āŠēોāŠિāŠāŠĻા āŠŪૂāŠģāŠૂāŠĪ āŠļિāŠĶ્āŠ§ાંāŠĪો | āŠļ્āŠ્āŠ°ૅāŠ (Scratch) āŠŪાં āŠĩાāŠ°્āŠĪા āŠ āŠĻે āŠāŠĻિāŠŪેāŠķāŠĻ āŠŽāŠĻાāŠĩāŠĩું. |
| 5 - 8 | āŠāŠēેāŠ્āŠ્āŠ°ોāŠĻિāŠ્āŠļ āŠ āŠĻે āŠāа્āŠĄુિāŠĻોāŠĻો āŠŠāŠ°િāŠāŠŊ | Arduino āŠŽોāŠ°્āŠĄ āŠļાāŠĨે LED āŠēાāŠāŠāŠĻે āŠંāŠ્āŠ°ોāŠē āŠāаāŠĩી (Blinking). |
| 9 - 12 | āŠļેāŠĻ્āŠļāŠ°્āŠļ āŠļાāŠĨે āŠાāŠŪ āŠāаāŠĩું (Inputs) | āŠ āŠē્āŠ્āŠ°ાāŠļોāŠĻિāŠ āŠļેāŠĻ્āŠļāŠ°āŠĻી āŠŪāŠĶāŠĶāŠĨી āŠ ંāŠĪāŠ° āŠŪાāŠŠāŠĩાāŠĻું āŠļાāŠ§āŠĻ āŠŽāŠĻાāŠĩāŠĩું. |
| 13 - 16 | āŠŪોāŠāа્āŠļ āŠ āŠĻે āŠāŠ્āŠ્āŠŊુāŠāŠāа્āŠļ (Outputs) | āŠļāŠ°્āŠĩો āŠŪોāŠāа (Servo Motor) āŠĻો āŠāŠŠāŠŊોāŠ āŠāаી āŠļ્āŠŪાāŠ°્āŠ āŠેāŠ āŠŽāŠĻાāŠĩāŠĩો. |
| 17 - 20 | āŠŠાāŠŊāŠĨોāŠĻ āŠ āŠĻે āŠāŠĄāŠĩાāŠĻ્āŠļ āŠોāŠĄિંāŠ | āŠŠાāŠŊāŠĨોāŠĻ āŠēેંāŠ્āŠĩેāŠāŠŪાં āŠļાāŠĶું āŠેāŠē્āŠ્āŠŊુāŠēેāŠāа āŠ āŠĻે āŠēોāŠિāŠāŠē āŠŠ્āŠ°ોāŠ્āŠ°ાāŠŪ્āŠļ āŠēāŠāŠĩા. |
| 21 - 24 | āŠેāŠŠāŠļ્āŠોāŠĻ āŠŠ્āŠ°ોāŠેāŠ્āŠ (Capstone Project) | āŠļ્āŠŪાāŠ°્āŠ āŠĄāŠļ્āŠāŠŽિāŠĻ āŠ āŠĨāŠĩા āŠāŠોāŠŪેāŠિāŠ āŠŠ્āŠēાāŠĻ્āŠ āŠĩોāŠāаિંāŠ āŠļિāŠļ્āŠāŠŪ āŠŽāŠĻાāŠĩāŠĩી. |
āŦŠ. āŠŪુāŠ્āŠŊ āŠ āŠ§્āŠŊāŠŊāŠĻ āŠĻિāŠ·્āŠŠāŠĪ્āŠĪિāŠ (Expected Learning Outcomes)
āŠ āŠ āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪ āŠŠૂāŠ°્āŠĢ āŠāа્āŠŊા āŠŠāŠી, āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠāŠŪાં āŠĻીāŠે āŠŪુāŠāŠŽāŠĻા āŠોāŠ્āŠāŠļ āŠŠāŠ°િāŠĢાāŠŪો (Outcomes) āŠ āŠŠેāŠ્āŠ·િāŠĪ āŠે:
āŠāŠŪ્āŠŠ્āŠŊુāŠેāŠķāŠĻāŠē āŠĨિંāŠિંāŠ (Computational Thinking): āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ āŠોāŠāŠŠāŠĢ āŠŪોāŠી āŠļāŠŪāŠļ્āŠŊાāŠĻે āŠĻાāŠĻા-āŠĻાāŠĻા āŠાāŠોāŠŪાં āŠĩāŠđેંāŠી (Decomposition) āŠĪેāŠĻું āŠĪાāŠ°્āŠિāŠ āŠļોāŠē્āŠŊુāŠķāŠĻ āŠķોāŠ§ી āŠķāŠāŠķે.
āŠđાāŠ°્āŠĄāŠĩેāŠ° āŠ āŠĻે āŠļોāŠŦ્āŠāŠĩેāŠ°āŠĻું āŠોāŠĄાāŠĢ: āŠŽાāŠģāŠ āŠŪાāŠĪ્āŠ° āŠેāŠŪ્āŠļ āŠ°āŠŪāŠĩાāŠĻે āŠŽāŠĶāŠēે āŠļāŠŪāŠી āŠķāŠāŠķે āŠે āŠļોāŠŦ્āŠāŠĩેāŠ° āŠોāŠĄ āŠĶ્āŠĩાāŠ°ા āŠŦિāŠિāŠāŠē āŠđાāŠ°્āŠĄāŠĩેāŠ° (āŠેāŠŪ āŠે āŠ°ોāŠŽોāŠિāŠ āŠāа્āŠŪ) āŠĻે āŠેāŠĩી āŠ°ીāŠĪે āŠāŠŪાāŠĻ્āŠĄ āŠāŠŠી āŠķāŠાāŠŊ.
āŠૂāŠēો āŠķોāŠ§āŠĩાāŠĻી āŠ્āŠ·āŠŪāŠĪા (Debugging): āŠોāŠĄāŠŪાં āŠે āŠļāŠ°્āŠિāŠāŠŪાં āŠāŠĩāŠĪી āŠૂāŠēોāŠĻે āŠાāŠĪે āŠķોāŠ§ીāŠĻે āŠĪેāŠĻે āŠļુāŠ§ાāŠ°āŠĩાāŠĻી āŠ્āŠ·āŠŪāŠĪા āŠેāŠģāŠĩાāŠķે, āŠેāŠĻાāŠĨી āŠ§ૈāŠ°્āŠŊ āŠ āŠĻે āŠāŠĪ્āŠŪāŠĩિāŠķ્āŠĩાāŠļ āŠĩāŠ§āŠķે.
āŠીāŠŪāŠĩāŠ°્āŠ āŠ āŠĻે āŠāŠĻોāŠĩેāŠķāŠĻ: āŠ્āŠ°ુāŠŠ āŠŠ્āŠ°ોāŠેāŠ્āŠ્āŠļ āŠĶ્āŠĩાāŠ°ા āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠ āŠļાāŠĨે āŠŪāŠģીāŠĻે āŠાāŠŪ āŠāаāŠĪા āŠķીāŠāŠķે āŠ āŠĻે āŠĩાāŠļ્āŠĪāŠĩિāŠ āŠીāŠĩāŠĻāŠĻી āŠļāŠŪāŠļ્āŠŊાāŠ (āŠેāŠŪ āŠે āŠ્āŠ°ાāŠŦિāŠ āŠંāŠ્āŠ°ોāŠē, āŠĩેāŠļ્āŠ āŠŪેāŠĻેāŠāŠŪેāŠĻ્āŠ) āŠĻા āŠેāŠāŠĻિāŠāŠē āŠāŠŠાāŠŊો āŠĩિāŠાāŠ°āŠķે.
āŦŦ. āŠŠāŠĄāŠાāŠ°ો āŠ āŠĻે āŠāŠĩિāŠ·્āŠŊāŠĻી āŠĶિāŠķા (Challenges & Future Road)
āŠુāŠāаાāŠĪāŠŪાં āŠ āŠ
āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪāŠĻા āŠļāŠŦāŠģ āŠ
āŠŪāŠēીāŠāаāŠĢ āŠļાāŠŪે āŠેāŠāŠēાāŠ āŠŠāŠĄāŠાāŠ°ો āŠŠāŠĢ āŠે, āŠેāŠŪ āŠે āŠ્āŠ°ાāŠŪીāŠĢ āŠĩિāŠļ્āŠĪાāŠ°ોāŠŪાં āŠāŠĻ્āŠŦ્āŠ°ાāŠļ્āŠ્āŠ°āŠ્āŠāа āŠŠāŠđોંāŠાāŠĄāŠĩું āŠ
āŠĻે āŠķિāŠ્āŠ·āŠોāŠĻે āŠ āŠĻāŠĩી āŠેāŠāŠĻોāŠēોāŠી āŠŪાāŠે āŠļāŠ્āŠ āŠāаāŠĩા. āŠ āŠŪાāŠે āŠુāŠāаાāŠĪ āŠļāŠ°āŠાāŠ° āŠ
āŠĻે āŠļ્āŠેāŠēિંāŠ āŠļંāŠļ્āŠĨાāŠ (āŠેāŠŪ āŠે STEMpedia, Kaushalya The Skills University)
āŠĻિāŠ·્āŠāа્āŠ·: āŠĩāŠ°્āŠ· 2026-27 āŠĻો āŠ āŠĻāŠĩો āŠોāŠĄિંāŠ āŠ āŠĻે āŠ°ોāŠŽોāŠિāŠ્āŠļ āŠ āŠ્āŠŊાāŠļāŠ્āŠ°āŠŪ āŠુāŠāаાāŠĪāŠĻા āŠĩિāŠĶ્āŠŊાāŠ°્āŠĨીāŠāŠĻે āŠŪાāŠĪ્āŠ° āŠĄિāŠિāŠāŠē āŠ્āŠ°ાāŠđāŠ (Consumers) āŠŪાંāŠĨી āŠŽāŠĶāŠēીāŠĻે āŠĩૈāŠķ્āŠĩિāŠ āŠļ્āŠĪāŠ°āŠĻા āŠĄિāŠિāŠāŠē āŠļāŠ°્āŠāŠ (Creators) āŠŽāŠĻાāŠĩāŠķે. āŠ āŠāŠĩિāŠ·્āŠŊāŠĻા āŠļ્āŠાāŠ°્āŠāŠ āŠŠ્āŠļ, āŠāŠĻ્āŠિāŠĻિāŠŊāŠ°િંāŠ āŠ āŠĻે āŠāŠĻોāŠĩેāŠķāŠĻ āŠŪાāŠેāŠĻો āŠŪāŠāŠŽૂāŠĪ āŠŠાāŠŊો āŠે.

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