Future Computing and Informatics Journal
Abstract
This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian Petroleum Sector. After developing and strengthening the ALS using the cloud computing with the benefits of using Function as a Services “FaaS”, the functions are start rapidly in order to allow handling of individual requests by using the Microservice Architecture. This study includes a description of the statistic field study approach (The study’s community and its sample. As well as used tools, methodologies, and their validity and reliability. It also includes used procedures for tools codification and their application. Finally, statistical processes that were relied upon in study analysis).
Recommended Citation
Ibrahim, Abdelsalam Helmy; Eliemy, Mohamed; and Youssif, Aliaa Abdelhalim
(2023)
"An Enhanced Adaptive Learning System based on Microservice Architecture,"
Future Computing and Informatics Journal: Vol. 8:
Iss.
1, Article 4.
Available at:
https://digitalcommons.aaru.edu.jo/fcij/vol8/iss1/4
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