A Personalized Learning Recommendation System Architecture for Learning Management System

Abstract

The information on the web is ever increasing and it is becoming difficult for students to find appropriate information or relevant learning material to satisfy their needs. Technology Enhanced Learning (TEL) is an area which covers all technologies that improve students learning. Effective Personal Learning Recommendation Systems (PLRS) will not only reduce this burden of information overload by recommending the relevant learning material to the students of their interest, but also provide them with "right" information at the "right" time and in the "right" way. In this paper, we first present a detailed analysis of existing TEL recommendation systems and identify the challenges that exist for developing and evaluating the datasets. Then, we propose an architecture for developing a PLRS that aims to support students via a Learning Management System (LMS) to find relevant material in order to enhance student learning experience. Also we proposes a methodology for building our own collaborative dataset via learning management systems (LMS) and educational repositories. This dataset will enhance student learning by recommending learning materials from the former student's competence qualifications. The proposed dataset offer information on the usage of more than 19,296 resources from 628 courses apart from data from social learner networks (forums, blogs, wikis and chats), which constitutes another 3,600 stored files Finally, we also present some future challenges and a roadmap for developing TEL PLRSs.

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