# Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance
## 3 Methodology and Results  
### The Participants and Their Log Files  
We conducted an analysis, using a Markov Chain Model to explore the connections among students' transitions in Moodle and their final grades, aiming to offer recommendations for enhancing students' academic performance. This research comprised 153 first-year postgraduate students participating in a yearly module within an English-speaking Postgraduate Program focused on Data Science at the Hellenic Open University during the academic year 2021-2022. The module specifically addresses topics such as Linear Algebra, Calculus, and Statistics and Probabilities. The module, with the 153 enrolled students, featured a central module forum alongside individual forums for each class, with eight classes making up the entire module. All students were required to submit five written assignments and five quizzes. Quizzes were designed to assess students' understanding of the material and provide practice opportunities, whereas assignments evaluated students' comprehension and application of the module content through practical tasks and projects. All students were provided with the same main and supplementary educational material, ensuring consistency in learning. Adhering to a unified academic schedule, all students also participate in a final exam and, if necessary, a re-sit exam on the same designated dates, promoting a synchronized academic experience across the module. The students' interaction with the LMS depended significantly on the structure and variety of course components. Given the diversity and quantity of components, the probability of students visiting each space is unequal. This structure encourages a more comprehensive engagement with various course elements, potentially leading to diverse learning paths and interactions. The Hellenic Open University utilizes the Moodle platform for its online portal, which facilitates the dissemination of course materials, assignments, and quizzes and fosters discussions through forums. All 153 students enrolled in the module were registered users on Moodle. Log files were extracted in.CSV format, documenting students' actions within the learning environment. Actions attributed to tutors and administrators were intentionally omitted from the analysis to ensure that the focus remained solely on student interactions. Specifically, we excluded any log entries that involved content creation, course management, grading, and any other administrative or instructional activities. Additionally, any automated system actions, such as system maintenance logs or auto-generated notifications, were also excluded. This approach ensures that the data analyzed reflects only the direct interactions of students with the Learning Management System. Each row in the.CSV file represented a specific student action, providing detailed information such as time, event content, event name, and origin (see Table 1). Entries corresponding to students who minimally engaged in the module's learning activities (e.g., no submission of assignments or quizzes) were excluded from the.CSV file. In the extracted data, the column _affected user_ is almost empty, suggesting that most logged events are direct actions performed by users themselves, rather than actions that affect other users. The column _event context_ provides specific information about the context within which an event occurred in the Moodle environment. It typically includes details such as the location or activity within Moodle where the event took place. For instance, it might specify the course, module, forum, quiz, or any other relevant component where the action occurred. The event context helps us to understand the setting of the event within the Moodle platform. The column _component_ seems to categorize the part of the system where the event occurred, like forum, whereas the column _event name_ describes the type of the event, such as discussion viewed. The column _description_ provides context-specific details about the event itself, helping to interpret its significance.