Adaptation using Machine Learning for Personalized eLearning Environment Based on Students Preference
John martin A1, Maria Dominic M.2
1A. John Martin Holds M.C.A., M.Phil. B. Ed in Computer Science.
2Dr. M. Maria Dominic Obtained his B. Sc, M. Sc, M. Phil and Ph. D in Computer Science.
Manuscript received on 17 August 2019 | Revised Manuscript received on 24 August 2019 | Manuscript published on 30 August 2019 | PP: 4064-4069 | Volume-8 Issue-10, August 2019 | Retrieval Number: J98190881019/2019©BEIESP | DOI: 10.35940/ijitee.J9819.0881019
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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: Information gathering is a lifelong process of human being and the learning takes place from womb to tomb. Learners acquire, recognize, map the gathered information to knowledge and use it in day to day life. Advancement in ICT and the utilization of ICT in teaching and learning process has contributed an exponential growth. An eLearning solution has almost reached maturity, where the teaching and learning community have the proper digital infrastructure, smart phones, tablet computers and the best software platform. An innovation in teaching and learning sector has become an integral part and is mandatory. Hence the challenge is to suggest quality and appropriate learning materials to the learners. The research aims to categorize the learner according to their learning ability and to find the learning path to facilitate the learner to have appropriate and quality learning objects with the help machine learning techniques. The focus of this work is to come up with a system architecture which predicts and adapts the learner style, find the learning path and to provide the suitable learning objects in eLearning environment based on their preferences. Personalization will assist learner to improve their learning performance.
Keywords: Adaptive Learning System, Clustering, eLearning, Personalization, Learning Object.
Scope of the Article: Machine Learning