Processing and Understanding Moodle Log Data and Their Temporal Dimension

The increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students' online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that help to characterize what actions students take, when, and where (in which course and in which part of the course). Temporal analysis has been shown to be relevant in learning analytics (LA) research, and capturing time-on-task as a proxy to model learning behaviour, predict performance, and prevent drop-out has been the subject of several studies. In Moodle, one of the most used learning management systems, while most events are logged at their beginning, other events are recorded at their end. The duration of an event is usually calculated as the difference between two consecutive records assuming that a log records the action's starting time. Therefore, when an event is logged at its end, the difference between the starting and the ending event identifies their sum, not the duration of the first. Moreover, in the pursuit of a better user experience, increasingly more online learning platforms' functions are shifted to the client, with the unintended effect of reducing significant logs and conceivably misinterpreting student behaviour. The purpose of this study is to present Moodle's logging system to illustrate where the temporal dimension of Moodle log data can be difficult to interpret and how this knowledge can be used to improve data processing. Starting from the correct extraction of Moodle logs, we focus on factors to consider when preparing data for temporal dimensional analysis. Considering the significance of the correct interpretation of log data to the LA community, we intend to initiate a discussion on this domain understanding to prevent the loss of data-related knowledge.

Automated summary

The increased adoption of online learning environments has led to the availability of a large amount of educational log data, which can be analyzed to understand students' online learning behaviors. Temporal analysis is important in learning analytics research, and capturing time-on-task can help model learning behavior, predict performance, and prevent drop-out. However, there are challenges in interpreting Moodle log data due to the logging system and the shift of functions to the client. This study aims to present these challenges and discuss ways to improve data processing for better interpretation of log data.