  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.
