| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
DFRobot |
DFRDUINO MEGA KIT |
0 |
|
|
DFRobot |
GRAVITY 37 PCS SENSOR SET |
24 |
|
|
DFRobot |
STARTER KIT ARDUINO UNO R3 |
0 |
|
|
DFRobot |
CIRCUIT SCRIBE BASIC KIT |
0 |
|
|
DFRobot |
BOSON INVENTOR KIT |
0 |
|
|
DFRobot |
BOSON SCIENCE KIT |
0 |
|
|
DFRobot |
GRAVITY: D3 KIT - A COMPREHENSIV |
0 |
|
|
DFRobot |
M1 DOCK AI DEVELOPMENT KIT |
0 |
|
|
DFRobot |
STARTER KIT ARDUBLOCK GRAVITY |
0 |
|
|
DFRobot |
CIRCUIT SCRIBE MAKER KIT |
0 |
|
|
DFRobot |
STARTER KIT INTEL EDISON/GALILEO |
0 |
|
|
DFRobot |
ENVIRONMENT SCIENCE BOARD FOR MI |
18 |
|
|
DFRobot |
GRAVITY: SENSOR KIT FOR INTEL JO |
0 |
|
|
DFRobot |
RASPBERRY PI 3 MODEL B+ STARTER |
0 |
|
|
DFRobot |
GRAVITY STARTER KIT FOR GENUINO |
0 |
|
|
DFRobot |
BARE CONDUCTIVE TOUCH BOARD STAR |
0 |
|
|
DFRobot |
M1W DOCK AI DEVELOPMENT KIT |
0 |
|
|
DFRobot |
CIRCUIT SCRIBE BASIC KIT (WITH B |
0 |
|
|
DFRobot |
HORNED SUNGEM ARTIFICIALINTELLI |
10 |
|
|
DFRobot |
GRAVITY IOT STARTER KIT FOR MICR |
10 |
|
Educational Kits refer to modular learning systems combining hardware and software components for STEM (Science, Technology, Engineering, Mathematics) education. These kits enable hands-on experimentation and prototyping, fostering technical literacy and innovation skills. Their importance in modern education stems from bridging theoretical knowledge with practical implementation, aligning with Industry 4.0 requirements for digital literacy and system integration.
| Type | Functional Features | Application Examples |
|---|---|---|
| Electronic Prototyping Kits | Microcontrollers, sensors, breadboards, and circuit modules | Arduino Starter Kits for robotics projects |
| Mechanical Engineering Kits | Gears, linkages, motor drivers, and structural components | VEX Robotics Competition systems |
| Programming & AI Kits | Single-board computers, ML accelerators, vision modules | Raspberry Pi AI Vision Kits |
| STEM Foundation Kits | Basic electronics, simple machines, and measurement tools | LEGO Education Simple & Powered Machines |
Typical educational kits contain: - Core Controller: Microcontroller (Arduino Uno, ESP32) or SBC (Raspberry Pi) - Peripheral Modules: Sensors (temperature, light, motion), actuators (motors, servos), and display units - Structural Elements: Laser-cut acrylic panels or 3D-printable components - Power System: Battery packs and voltage regulation circuits - Software Suite: IDEs (Arduino IDE, Scratch), firmware libraries, and simulation tools
| Parameter | Description | Importance |
|---|---|---|
| Component Compatibility | Standardized interfaces (I2C, SPI, GPIO) | Enables system expansion and third-party integration |
| Processing Power | Clock speed (16MHz-1.5GHz), RAM (256KB-4GB) | Determines complexity of programmable tasks |
| Sensor Resolution | Analog/digital precision (8-16bit) | Affects measurement accuracy and control granularity |
| Power Requirements | Operating voltage (3.3V-12V) | Dictates safety classification and peripheral selection |
| Educational Standards | NGSS (Next Gen Science Standards) alignment | Ensures curriculum relevance and pedagogical effectiveness |
Primary sectors include: - K-12 Education: Classroom STEM labs with LEGO Mindstorms or Codey Rocky - Higher Education: Engineering prototyping using NI myRIO devices - Maker Spaces: Community workshops with open-source hardware platforms - Corporate Training: Industrial automation simulation with Festo Didactic systems
| Manufacturer | Flagship Product | Key Features |
|---|---|---|
| Arduino | Arduino Education Starter Kit | Modular electronics with Tinkercad integration |
| Raspberry Pi Foundation | Raspberry Pi 400 Kit | Keyboard computer with Python programming environment |
| Makeblock | Makeblock Ultimate 2.0 | 10-in-1 robotics platform with mechanical/electronic components |
| Parallax | Parallax ActivityBot | Propeller multicore processor with robotics curriculum |
Key considerations: - Educational Objectives: Match complexity to learning outcomes (basic electronics vs. AI) - User Proficiency: Choose age-appropriate abstraction levels (Scratch vs. C++) - Budget Constraints: Balance performance with cost (entry-level vs. professional kits) - Scalability: Verify expansion options through available shields/accessory modules - Support Ecosystem: Evaluate documentation quality and community resources
Case Study: A university engineering department adopted the Texas Instruments MSP432 LaunchPad for embedded systems courses due to its industry-grade ARM Cortex-M4F processor and integration with MATLAB/Simulink for control system modeling.
Emerging developments: - AI Integration: Embedded machine learning modules (Google Coral TPU integration) - Web-Based Platforms: Browser-accessible IDEs with real-time collaboration - Cross-Disciplinary Focus: Biotechnology kits merging electronics with living systems - Sustainability Emphasis: Recyclable components and energy efficiency monitoring - Cloud Connectivity: IoT-enabled kits with AWS/GCP integration for remote data analysis