An Intelligence Learner Management System using Learning Analytics and Machine learning

Abstract

Learner Management System (LMS) facilitates educational institutions to offer e-learning through web-based applications. LMS provides many benefits from cost saving to flexible learning opportunities independent of any location with cloud-based deployment. Hence, LMS organizes learning data and learners detail in a central repository, helps to improve resource allocation, and facilitates access to the learning resources. These benefits drive the LMS market growth at a rapid rate and it is now deployed across the industry of any size. Despite of these significant benefits of using LMS, the traditional LMS system cannot fully support with modern learning needs in terms of learners' progression, retention rate, prediction of assessment outcomes to improve overall teaching and learning experience. Learning analytics can effectively support for a better learning experience by analyzing and correlating learner data to predicate the future needs. This work as a part of the Knowledge Transfer Project (KTP) develops an intelligence Learner Management System(iLMS) that integrates learning analytics into the learner management system. We use Machine Learning(ML) techniques for descriptive, predictive, and prescriptive analytics of learners’ data. This paper presents the key iLMS features including user interfaces, reports and learning analytics.