# Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance
###### Abstract  
In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students' transitions within Moodle generate digital trace data that outline learners' self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students' trajectory in a course during an academic year, we analyzed data on students' actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users' eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students' learning paths, tutors' guidance, and course structures in the Moodle environment.  
[table]cap=top
users' interactions with Moodle. Analyzing user browsing patterns throughout a course provides insights into online learning environment dynamics [11; 12]. Datasets derived from student participation in educational environments are powerful tools for researchers, facilitating the discovery of hidden patterns through analytics techniques [13; 14]. In our study, we aim to present students' action sequences within their institutional LMS, Moodle. The study involved 153 participants who attended an English-speaking postgraduate program in Data Science at the Hellenic Open University. The participants were all enrolled in the same module, focused on Data Science.  
To analyze these action sequences in Moodle, we utilized a Markov Chain Model, a valuable model for analyzing data where sequences matter [15; 16; 17]. While Hidden Markov Chain Models have been used to model students' actions regarding engagement in the learning process, we opted for a Markov Chain Model to focus on observable events occurring in Moodle, rather than unobservable influences. Events (states) in Moodle include viewing or downloading a folder, visiting the academic schedule, submitting an assignment, completing a quiz, etc. Transition matrices were constructed based on users' transitions in Moodle from one event to another to identify steady states and the principal eigenvector, indicating long-term state probabilities. By tracing students' self-directed learning paths, our primary aim was to identify states and transitions that significantly impact users' final grades, which were used as a proxy of their academic performance. By calculating each student's eigenvector, an innovative approach that encapsulates the probabilities of being in specific states, we aimed to explore potential correlations between these states and their final grades. Although we used many different metrics, we did not find statistically significant correlations. Nonetheless, the aim of our study was to propose a new research strategy that can be easily used, even in very large courses, to investigate these patterns and better understand students' engagement and its potential impact on their final grades. This research contributes to the broader understanding of student interactions with online learning platforms and informs future studies and interventions aimed at enhancing educational outcomes.