A Machine Learning Approach to Intrusion Detection in Cloud-Based e-Health Systems

<p><span>The role of mobile technology in education cannot be over-emphasized. However, previous studies have shown that schools have been slow to adopt these devices as instructional tools for teaching and learning. This study adopts the Technology Acceptance Model (TAM) as a conceptual framework to examine the factors influencing pre-service teachers' perceived ease of use and perceived usefulness of mobile technologies, as well as the barriers that might hinder their adoption. The study was guided by three research questions and one hypothesis. A descriptive survey research design was employed. The study population consisted of students from the Faculty of Education, University of Ibadan. A total of 400 pre-service teachers were selected using simple random sampling from four departments: Science and Technology Education, Special Education, Educational Management, and Adult Education. Purposive sampling was used to select these departments, with 100 pre-service teachers chosen from each department. A structured 24-item questionnaire was used to collect data, and the reliability of the instrument was confirmed with a Cronbach’s alpha value of 0.75. The findings indicated that pre-service teachers had positive perceptions regarding the ease of use and usefulness of mobile technologies for teaching and learning. However, challenges such as insufficient pedagogical knowledge and lack of proper orientation on integrating mobile technologies into the curriculum were noted as potential barriers. It was recommended that policymakers and curriculum planners should organise training and workshops for both students and teachers on how to effectively integrate mobile technologies into instructional delivery and manage associated challenges.</span></p>

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-20
DOI
https://doi.org/10.5281/zenodo.16910610
Primary Topic
Theoretical and Computational Physics
Type
article
Field-Weighted Citation Impact
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article

A Machine Learning Approach to Intrusion Detection in Cloud-Based e-Health Systems

Zenodo (CERN European Organization for Nuclear Research)
Theoretical and Computational Physics
article

A Machine Learning Approach to Intrusion Detection in Cloud-Based e-Health Systems

article en

Abstract

<p><span>The role of mobile technology in education cannot be over-emphasized. However, previous studies have shown that schools have been slow to adopt these devices as instructional tools for teaching and learning. This study adopts the Technology Acceptance Model (TAM) as a conceptual framework to examine the factors influencing pre-service teachers' perceived ease of use and perceived usefulness of mobile technologies, as well as the barriers that might hinder their adoption. The study was guided by three research questions and one hypothesis. A descriptive survey research design was employed. The study population consisted of students from the Faculty of Education, University of Ibadan. A total of 400 pre-service teachers were selected using simple random sampling from four departments: Science and Technology Education, Special Education, Educational Management, and Adult Education. Purposive sampling was used to select these departments, with 100 pre-service teachers chosen from each department. A structured 24-item questionnaire was used to collect data, and the reliability of the instrument was confirmed with a Cronbach’s alpha value of 0.75. The findings indicated that pre-service teachers had positive perceptions regarding the ease of use and usefulness of mobile technologies for teaching and learning. However, challenges such as insufficient pedagogical knowledge and lack of proper orientation on integrating mobile technologies into the curriculum were noted as potential barriers. It was recommended that policymakers and curriculum planners should organise training and workshops for both students and teachers on how to effectively integrate mobile technologies into instructional delivery and manage associated challenges.</span></p>

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 39%
Theoretical and Computational Physics
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A Machine Learning Approach to Intrusion Detection in Cloud-Based e-Health Systems · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS