Learning analytics system for assessing students' performance quality and text mining in online communication

Abstract

A challenging and demanding task for the teachers and researchers in e-learning environments is the assessment of students' performance. This paper is to present a new Learning analytics system for Learning Management Systems (LMS), that will aid and support teachers and researchers to understand and analyze interaction patterns and knowledge construction of the participants involved in ongoing online interactions. It is seamlessly integrated into Moodle. Learning Management Systems (LMS) does not include analytics tool for comprehensive audit logs of students' activities and log analysis capabilities interactions, also lack of good evaluation of participatory level and support for assessment of students' performance quality on LMS. Semantic similarity measures of text play an increasingly important role in text related research and applications in tasks such as text mining, web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for Messages texts in LMS. The system enables one to measure semantic similarity between texts exchanged during communication sessions, in order to find out the degree of coherence in a discussion tread. It is given as a value of relevance in numerical format.