Abstract

The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture for building and running smart classrooms is essential for a smart campus. However, most studies related to the smart classroom are focused on studying or addressing particular technical or educational issues, such as networking, AI applications, lecture quality, and user responses to technology. In this study, an architecture for building and running context-aware smart classrooms is proposed. The proposed architecture consists of three parts including a prototype of a context-aware smart classroom, a model for technology integration, and supporting measures for the operation of smart classrooms in this architecture. The classroom prototype was designed based on our study results and a smart classroom project in Ming Chuan University (MCU). The integration model was a layered model uses Raspberry Pi in the bottom layer of the model to integrate underlying technologies and provide application interfaces to the higher layer applications for the ease of building context-aware smart classroom applications. As a result, application interfaces were implemented using Raspberry Pi based on the proposed technology integration model, and a context-aware energy-saving smart classroom application was implemented based on the proposed classroom prototype and the implemented web application interface. The result shows that, in terms of technology, the proposed architecture is feasible for building context-aware smart classrooms in smart campuses.

Highlights

  • Classroom is a space for teaching and learning

  • In Reference [80], a smart campus internet of things (IoT) framework was proposed to address issues related to energy consumption, classroom functionality, safety, and cybersecurity, and machine-learning algorithms were employed in the proposed framework

  • After control commands corresponding to actions on devices are implemented and tested on Raspberry Pi boards in layer 1, layer 2 basic operations can be defined as a web application interface calls and implemented with Python and Flask

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Summary

Introduction

Classroom is a space for teaching and learning. A general classroom usually has a blackboard, a lectern, tables, and chairs. The smart classroom is a concept related to utilizing information and communication technologies to produce a superior teaching and learning environment. Computing concepts such as sensor network, internet of things, context-aware computing, ubiquitous computing, pervasive computing, data mining and artificial intelligence are often mentioned while discussing topics about smart classroom. We propose a context-aware smart classroom architecture for building and running smart classrooms in higher education institutes. Essential elements of a context-aware smart classroom are defined based on our study results, including a novel technology integration model for building and managing multiple smart classrooms in the university campus, and supporting measures for physically implementing the model.

Background and Related Works
Physical Environment and Smart Classrooms
Technologies in Smart Classrooms
Context-Aware and Energy Saving Related Studies
The MCU Smart Classroom Project
Smart Campus
Our Proposed Context-Aware Smart Classroom Architecture
The Context-Aware Smart Classroom Prototype
Basic Requirements of a Technology-Rich Classroom
Swivel screen tablet chairs for dynamic seat rearrangement:
From a Technology-Rich Classroom to a Smart Classroom
Swivel
Smart Classroom with Context Awareness
The Smart Classroom Technology Integration Model
Supporting Measures for the Proposed Architecture
An Implementation of the Proposed Architecture
Device Integration Layer and Basic Operation Layer
Combo Operation Layer and Smart Classroom Application Layer
Context-Aware
In-Campus Facial Recognition Service Integration
Implementation
Findings
Conclusions
Full Text
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