A Comprehensive Framework for Adaptive E-Learning Management System
Shinoj Robert1, Maria Dominic2

1Shinoj Robert, Research Scholar, Department of Computer Science, Sacred Heart College, Tirupattur, India.
2M. Maria Dominic, Assistant Professor, Department of Computer Science, Sacred Heart College, Tirupattur, India.

Manuscript received on November 13, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 4846-4854 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7824129219/2019©BEIESP | DOI: 10.35940/ijitee.B7824.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The various e-Learning systems in recent times found to be ineffective in directing the individual learning style with the preferred learning abilities. Focusing on this inadequacy, this paper proposes a comprehensive study from the different styles on the existing frameworks engaging an adaptive learning algorithm that applies a data stream technique using Machine Learning for a better learning path. The adaptation process insists or allow the user to modify the parameters and to adapt the behaviour according to the system assumption. When the system adapts to a personal concept and responds, it is said to be a personalized process. This concentrate and generates more ideas and style towards student-centric learning and creating a new learning path for an individual or group of learners. This generic framework is grounded on four-dimension. This framework tailors the learning style according to the individual learning types by creating a specific learning objective by incorporating learning modules, personalization and learner Noesis with technologies 
Keywords: e-Learning, learning Abilities, Machine Learning, adaptive learning, Data Stream, User Profile.
Scope of the Article: Machine Learning