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Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques

Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques

Ahmad Abu-Al-Aish
Copyright: © 2021 |Volume: 17 |Issue: 4 |Pages: 23
ISSN: 1550-1876|EISSN: 1550-1337|EISBN13: 9781799859390|DOI: 10.4018/IJICTE.20211001.oa14
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MLA

Abu-Al-Aish, Ahmad. "Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques." IJICTE vol.17, no.4 2021: pp.1-23. http://doi.org/10.4018/IJICTE.20211001.oa14

APA

Abu-Al-Aish, A. (2021). Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques. International Journal of Information and Communication Technology Education (IJICTE), 17(4), 1-23. http://doi.org/10.4018/IJICTE.20211001.oa14

Chicago

Abu-Al-Aish, Ahmad. "Exploring Technical Quality Factors That Enhance Mobile Learning Applications Services Using Data Mining Techniques," International Journal of Information and Communication Technology Education (IJICTE) 17, no.4: 1-23. http://doi.org/10.4018/IJICTE.20211001.oa14

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Abstract

Mobile learning (m-learning) has become an increasingly attractive solution for schools and universities that utilize new technologies in their teaching and learning setting. This study investigates the technical factors affecting the development of m-learning applications services from students’ perspectives. It presents a model consisting of twelve technical factors, including content usefulness, scalability, security, functionality, accessibility, interface design, interactivity, reliability, availability, trust, responsiveness, and personalization. To evaluate the model, a questionnaire was designed and distributed to 151 students in Jerash University, Jordan. The results indicate that all technical factors have positive affects on learner satisfaction and overall m-learning applications services, however the data mining analysis revealed that security and scalability factors exert a major impact on student satisfaction with m-learning applications services. This study gives insight for the future of developing and design m-learning applications.