Personalised Self-Directed Learning Recommendation System

Abstract

Abstract Modern educational systems have changed drastically bringing in knowledge anywhere as needed by the learner with the evolution of Internet. Availability of knowledge in public domain, capability of exchanging large amount of information and filtering relevant information quickly has enabled disruption to conventional educational system. Thus, future trends are looking towards E-Learning (Electronic Learning) and M-Learning (Mobile Learning) technologies over the Internet for their vast knowledge acquisition. In this paper, the work gives an elaborate context of learning strategies prevailing and emerging with the classification of e-learning Techniques. It majorly focuses on the features and variety of aspects with the e-learning and the choice of learning method involved and facilitate the adoption of new ways for personalized selection on learning resources for SDL (Self-Directed Learning) from the unstructured, large web-based environment. Thereby, proposes a Personalised Self-Directed Learning Recommendation System (PSDLR) based on the personal specifications of the SDL learner. The result offers insight into the perspectives and challenges of Self-Directed Learning based on cognitive and constructive characteristics which majorly incorporates web-based learning and gives path in finding appropriate solutions using machine learning techniques and ontology for the open problems in the respective fields with personalised recommendations and guidelines for future research.