TY - GEN
T1 - Artificial Intelligence in the Assessment Process of MOOCs using a cloud-computing ecosystem
AU - Reategui, Jose L.
AU - Herrera, Pablo C.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This research shows a flow of open, flexible, and adaptable computational processes to implement a learning assessment solution incorporated into a low-cost Massive Open Online Courses (MOOCs) platform. It considers the selection of questions made by an Artificial Intelligence (AI) engine, which receives suggestions and decisions from teachers, and which the student receives, as a virtual questionnaire in a mobile application, personalizing their learning needs in real-time. The AI is based on a forecasting engine, hosted on the remote Amazon Web Services (AWS) server, the Learning Management System (LMS) controls the assessments and the Course Management System (CMS) controls the process. This computational ecosystem is a solution that reduces the cost and the need for technical support when implementing a technology related to Machine Learning and visualization for any time and place in the LMS - CMS code. To facilitate learning portability, this ecosystem is described from three ecosystem environments, LMS-CMS (Open EDX), remote server (AWS), and an application for interfaces and server communication created in Unity3D. In these environments, ten patterns interact through various micro-services to respond to the consumption mode between the Open EDX Front End and the mobile application. Fragmentation into patterns makes this research reusable and adaptable for future online learning contexts.
AB - This research shows a flow of open, flexible, and adaptable computational processes to implement a learning assessment solution incorporated into a low-cost Massive Open Online Courses (MOOCs) platform. It considers the selection of questions made by an Artificial Intelligence (AI) engine, which receives suggestions and decisions from teachers, and which the student receives, as a virtual questionnaire in a mobile application, personalizing their learning needs in real-time. The AI is based on a forecasting engine, hosted on the remote Amazon Web Services (AWS) server, the Learning Management System (LMS) controls the assessments and the Course Management System (CMS) controls the process. This computational ecosystem is a solution that reduces the cost and the need for technical support when implementing a technology related to Machine Learning and visualization for any time and place in the LMS - CMS code. To facilitate learning portability, this ecosystem is described from three ecosystem environments, LMS-CMS (Open EDX), remote server (AWS), and an application for interfaces and server communication created in Unity3D. In these environments, ten patterns interact through various micro-services to respond to the consumption mode between the Open EDX Front End and the mobile application. Fragmentation into patterns makes this research reusable and adaptable for future online learning contexts.
KW - artificial intelligence
KW - assessment
KW - AWS
KW - MOOCs
KW - OpenEDX
KW - pattern
UR - https://www.scopus.com/pages/publications/85125887887
U2 - 10.1109/TALE52509.2021.9678911
DO - 10.1109/TALE52509.2021.9678911
M3 - Contribución a la conferencia
AN - SCOPUS:85125887887
T3 - TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings
SP - 487
EP - 493
BT - TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021
Y2 - 5 December 2021 through 8 December 2021
ER -