Learning analytics toolset for evaluating students’ performance in an E-learning Platform

Abstract

Learning analytics is the process of measuring, collecting, analyzing, and reporting data on learners and their environments in order to better understand and optimize learning and the environments in which it occurs. Assessment of students’ performance is a difficult and time-consuming undertaking for teachers and researchers in e-learning environments. The majority of LMS frameworks, whether commercial or open-source, access tracking and log analysis capabilities are insufficient. They also lack support for a variety of features such as evaluating participation levels, analyzing interactions, visually portraying current interactions, and semantic analysis of message content. Estimating the number of participants, non-participants, and lurkers in a continuing discourse is challenging and time-consuming for instructors and educationalists. Text mining, web page retrieval, and dialogue systems are all examples of text-related research and applications, semantic similarity measurements of text are becoming increasingly relevant. This study will present a unique learning analytics system for learning management systems (LMS) that will aid teachers and researchers in identifying and analyzing interaction patterns and participant knowledge acquisition in continuous online interactions.