A Survey on Smart Campus Implementation in Malaysia

Musa, Masitah; Ismail, Mohd Norasri; Fudzee, Mohd Farhan Md

doi:10.30630/joiv.5.1.434

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…It is crucial to analyze design principles for cloud environments while utilizing tried-andtrue technologies like sensors and standards, like the W3C semantic sensor network ontology, to create smart campuses given the rapid advancement of IoT technology and the automation of tasks. To ensure continuous operation in the event of connection issues and power outages, IoT integration in smart campuses must handle crucial concerns including encryption, software upgrades, system validation against vulnerabilities, and the development of backup systems (Ghadami et al, 2021;Musa, Ismail, & Fudzee, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

Li,

Palaoag,

et al. 2023

J INFORM SYSTEMS ENG

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In this study, an artificial intelligence (AI)--based smart campus framework is built and optimized with the aim of improving user happiness, raising AI model performance, maximizing resource utilization, and promoting smart campus adoption. The study technique employs a mixed-methods approach that combines quantitative data analysis and qualitative user feedback in order to completely evaluate the effectiveness of the framework. Literature reviews, Questionnaires of 544, interviews of 56 persons, and observations are used to collect data on user satisfaction, AI model performance, optimization strategies, and adoption of smart campuses AI models are built using statistical methodology and AI techniques for performance evaluation. In the Smart Campus Framework based on Artificial Intelligence, we gathered the data by constructing IoT sensor networks for real-time monitoring and merging student data to provide insights into academic performance and student engagement. The findings indicate that, on average, users are satisfied, and the performance ratings for the AI models vary from 7.25 to 8.25. The smart campus framework is effective, as evidenced by the optimization metric's 7.53 average score. A score of 7.4 for smart campus adoption combines user knowledge, perceived utility, and perceived ease of use. The practical implications include better user experience, cost optimization, and smart campus architecture. Theoretical implications include the verification of the mixed-methods strategy and the creation of a framework for AI model optimization. The study's findings act as a model for upcoming smart campus research, spurring creativity and change in institutions of higher learning. The study’s limitations suggest that results can be generalized with minor contextual change and this is the biggest challenge for researchers and policy makers.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

Li,

Palaoag,

et al. 2023

J INFORM SYSTEMS ENG

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“…The smart campus has recently been adopted at several universities around the world. However, each university has its own set of objectives to accomplish via various initiatives [2]. Due to infrastructure issues, the higher education community faces a variety of challenges.…”

Section: Introductionmentioning

confidence: 99%

Smart Campus Geographic Information System (GIS) Mobile Application Reporting System

Syafuan

Husin

Fauzi³

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The Geographical Information System (GIS) mobile application helps the communities in a specific area, the agency related to the maintenance and solving problems related to infrastructure. The purpose of this study is to enhance the location and visualization of the current reporting system. The methodology used in this study starts with a preliminary study to understand the issue, data collection, data processing, and system design using ArcGIS for Desktop, ArcGIS App Studio to design the application, ArcGIS Online to create the computer dashboard, and finished by testing the system. As a result, a mobile geographical information system based on the maintenance and solving problems related to infrastructure was created as a mobile application. Some people tested the Smart Campus GIS Mobile Application Reporting System in the specified area to verify the capabilities. In conclusion, the positive feedback received from the respondent is used to validate the mobile application and is ready to be used by the people in the target area.

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“…Hence, students can gain knowledge and information by joining the learning management system that has been provided by the university at anytime and anywhere. There are many campuses that had been developed and used their own LMS including Universiti Tun Hussein Onn Malaysia (UTHM) [8].…”

Section: Introductionmentioning

confidence: 99%

Student Acceptance Towards Online Learning Management System based on UTAUT2 Model

Musa¹,

Ismail²,

Tahir³

et al. 2022

IJACSA

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Recently, education has changed from physical learning to online and hybrid learning. Furthermore, the outbreak of COVID-19 makes them more significant. An online learning management system (LMS) is one of the most prevalent approaches to online and distance learning. The acceptance of the students towards the LMS is significant and it can give either bad or good responses to ensure the success of LMS. However, the Universiti Tun Hussein Onn Malaysia (UTHM) has not yet implemented any study to examine their LMS. The Unified Theory of Acceptance and Usage of Technology (UTAUT2) model is used in this study to investigate students' Behavioral Intention and Use Behavior when using the LMS in UTHM. This study also introduces a new construct in UTAUT2 named Online Learning Value. 376 respondents took part in this survey. Descriptive Statistics, Reliability Analysis, Pearson Correlation Coefficient, and Multiple Linear Regression analysis were all used to analyze survey data. The outcome of this research is Performance Expectancy (β=0.129, p=0.014), Hedonic Motivation (β=0.221, p=0.000), Online Learning Value (β=0.109, p=0.036) and Habit (β=0.513, p=0.000) has influence on students' intention to use LMS. Besides that, Facilitating Conditions (β=0.481, p=0.000) are the most important factors in students' use behavior toward the LMS followed by Habit (β=0.343, p=0.000) and Behavioral Intention (β=0.239, p=0.000). By utilizing the UTAUT2 model, the constructs of technology acceptance related to students' adoption of LMS have been identified and may become a reference to the stakeholders for future enhancement.

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A Survey on Smart Campus Implementation in Malaysia

Cited by 13 publications

References 28 publications

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

Smart Campus Geographic Information System (GIS) Mobile Application Reporting System

Student Acceptance Towards Online Learning Management System based on UTAUT2 Model

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