Making Sense of Moodle Log Data

TL;DR

This paper discusses the challenges and potential misinterpretations in analyzing Moodle log data, emphasizing the importance of understanding data generation processes to avoid biased machine learning outcomes.

Contribution

It highlights issues related to data bias and loss of log information in Moodle, advocating for better understanding of data generation for responsible analysis.

Findings

Examples of Moodle log data and potential misinterpretations

Risks of biased datasets in machine learning models

Impact of client-side logging reduction on data quality

Abstract

Research is constantly engaged in finding more productive and powerful ways to support quality learning and teaching. However, although researchers and data scientists try to analyse educational data most transparently and responsibly, the risk of training machine learning algorithms on biased datasets is always around the corner and may lead to misinterpretations of student behaviour. This may happen in case of partial understanding of how learning log data is generated. Moreover, the pursuit of an ever friendlier user experience moves more and more Learning Management Systems functionality from the server to the client, but it tends to reduce significant logs as a side effect. This paper tries to focus on these issues showing some examples of learning log data extracted from Moodle and some possible misinterpretations that they hide with the aim to open the debate on data understanding and data knowledge loss.