Processing and Understanding Moodle Log Data and Their Temporal Dimension
DOI:
https://doi.org/10.18608/jla.2023.7867Keywords:
Learning log data, Educational log data, Moodle log data collection, Time-on-task, Temporal dimension, data and tools reportAbstract
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.
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