Overview

This project explores the integration of IoT-enabled pocket laboratories into control systems education using the 5E instructional model.

The objective is to provide students with a flexible, low-cost and scalable experimental environment capable of connecting theoretical control concepts with real-time experimentation.

The project combines ESP32 microcontrollers, MQTT communication, Python-based Jupyter notebooks and active learning strategies to modernize engineering education.

Pedagogical Context

Traditional control systems courses often face important limitations:

• High cost of laboratory equipment.
• Limited availability of physical laboratories.
• Rigid scheduling.
• Difficulty connecting theory and practice.
• Limited access to real-time experimentation.
• Unequal access to experimental resources.

IoT-enabled pocket laboratories offer an alternative by allowing students to experiment with real systems in flexible, portable and hybrid learning environments.

Main Objectives

The project pursues the following objectives:

• Develop portable IoT-based control laboratories.
• Support active learning in control systems education.
• Integrate the 5E instructional model.
• Facilitate real-time experimentation.
• Improve student understanding of control concepts.
• Support hybrid and remote learning scenarios.
• Democratize access to engineering laboratories.

Methodology

The pedagogical methodology combines IoT infrastructure with the 5E instructional model.

Stage 1 – Engage

Students are introduced to control problems through contextualized experimental challenges.

The objective is to activate prior knowledge and create motivation for the learning activity.

Stage 2 – Explore

Students interact with IoT-enabled experimental systems and observe real-time behavior.

They explore:

• Sensor data
• System responses
• Control parameters
• Dynamic behavior
• Experimental uncertainty

Stage 3 – Explain

Students connect experimental observations with control theory concepts.

This stage supports the understanding of:

• Feedback
• Stability
• Transient response
• Controller tuning
• System identification

Stage 4 – Elaborate

Students extend the activity by modifying control strategies and testing alternative configurations.

This reinforces autonomous experimentation and engineering reasoning.

Stage 5 – Evaluate

Learning outcomes are assessed through surveys, analysis of student productions and qualitative feedback.

Technical Architecture

The experimental environment integrates:

• ESP32 microcontrollers
• MQTT brokers
• Python
• Jupyter notebooks
• Real-time visualization
• Low-cost sensors and actuators
• Portable experimental setups

Educational Contributions

The project contributes to:

• Active learning in control engineering.
• Hybrid laboratory design.
• IoT-based experimentation.
• Portable low-cost engineering education.
• Student-centered learning.
• Flexible access to laboratory activities.

Research Findings

The project evaluates how IoT-enabled pocket laboratories influence:

• Conceptual understanding
• Student engagement
• Self-regulated learning
• Experimental reasoning
• Collaboration
• Theory-practice integration

Long-Term Vision

The project opens perspectives toward intelligent learning environments capable of using real-time interaction data to provide adaptive feedback and personalized support.

Future developments may integrate:

• Learning analytics
• Machine learning
• Automated feedback
• Remote experimentation
• Digital twins for education

Keywords

Control Systems Education · IoT · Pocket Laboratories · ESP32 · MQTT · Jupyter Notebooks · 5E Instructional Model · Active Learning · Engineering Education · Hybrid Laboratories · Industry 4.0

Project Status

Submitted research