Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal

Abstract

Massive Open Online Courses (MOOCs) are a type of Learning Management Systems (LMSs), but it seems that the influence of the instructor in these systems is minimal or simply lacking. These systems present the learning content and materials to all learners attending the course in the same way and fail to offer individualized instruction that recognizes the individual differences and needs of the learners. It is reported that such problems can be eliminated by making the new generation intelligent learning systems. However, there is still an ongoing search for making such systems intelligent and a conceptual discussion concerning them. Integrating an intelligent tutoring system (ITS) with learning analytics, this study seeks to design and present the framework of an ITS with open access that a) identifies the learning needs of learners through adaptive mastery testing and guides learners based on these needs, b) overcomes learning deficiencies, monitors learners' interactions with content through learning analytics and offers suggestions, c) supports learning with dynamic assessment processes and d) tests learners’ learning competencies. This article aims to explain the conceptual and system framework for the design of an adaptive, dynamic, intelligent tutoring system (SMIT), supported by learning analytics, which is a product of the project, which aims to integrate LMS and ITS, on the idea of how to make systems such as MOOCs smarter. In line with the findings obtained from the research, various suggestions were made for the design of smart Moocs. © 2022 Elsevier B.V., All rights reserved.