Online Learners' Navigational Patterns Based on Data Mining in Terms of Learning Achievement

Abstract

The aim of this study is to determine navigational patterns of university students in a learning management system (LMS). It also investigates whether online learners' navigational behaviors differ in terms of their academic achievement (pass, fail). The data for the study comes from 65 third grade students enrolled in online Computer Network and Communication lesson in a state university. As the online learning environment, Moodle, an open source software, is used in this study. Navigational log records derived from database were converted into sequential database format. According to students' achievement (pass, failure) at the end of the academic term, these data were divided in two tables. Page connections of the users were transformed into interaction themes namely, homepage, content, discussion, messaging, profile, assessment, feedback and ask the instructor. Data transformed to sequential patterns by the researchers were organized in navigational pattern graphics by taking frequency and ratio into consideration. To test the difference between obtained patterns ratio test was conducted by means of z statistics. The findings of the research revealed that first and second order navigational patterns of passed and failed students in the online learning environment had similar features, but passed students allocated more time to interaction process.