Learners’ Performance Evaluation Measurement Using Learning Analytics in Moodle

Abstract

In recent years, the development of online environments has been rising exponentially and educators and learners are moving toward online learning systems. These online learning systems are open-source applications that have their advantages and disadvantages. Moodle is one of the widely used open-source learning platforms used by most institutions all over the world. Even though moodle provides a good framework for learning, it is static with minimal functionalities. The need for student preferences and their contexts is required for understanding and optimizing learning environments in a better way. The paper presents an approach to collect and retrieve student behaviors from the log files and table of moodle and classify learning preferences using the standard Naïve Bayes classifier based on the standard Felder Silverman learning style model. The retrieved learning preference based on students’ behaviors and actions supports educators to view the preferences of students and to improve and enhance their teaching.