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

Abstract

The aim of this study is to explore navigational patterns of university students in a learning management system (LMS). After a close review of the literature, a scarcity of research on the relation between online learners’ navigational patterns and their learning performance was found. To contribute to this research area, the study aims to examine whether there is a potential difference in navigational patterns of the learners in terms of their academic achievement (pass, fail). The data for the study comes from 65 university students enrolled in online Computer Network and Communication. Navigational log records derived from the database were converted into sequential database format. According to students’ achievement (pass, failure) at the end of the academic term, these data were divided into 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 into sequential patterns by the researchers were organized in navigational pattern graphics by taking frequency and ratio into consideration. The z test was used to test the significance of the difference between the ratios calculated by the researchers. The findings of the research revealed that although learners differ in terms of their achievement, they draw upon similar processes in the online learning environments. Nevertheless, it was observed that students differ from each other when considering their system interaction durations. According to this, learning agents, interventional feedbacks, and leaderboards can be used to keep failed students in the online learning environment. Studies were also proposed on the ordering of these LMS navigational themes, which are important in the e-learning process. Findings from these studies can guide designers and researchers in the design of adaptive e-learning environments, which are also called next-generation digital learning environments. © Springer Nature Switzerland AG 2019.