Educational Data Mining (EDM):

Educational Data Mining is about improving learning outcomes by mining and analyzing data collected as we teach. Just as in scientific and business fields of study, educational researchers see the potential to dramatically improve learning through this type of research.
The educational data mining community is using the large amounts of data to validate research findings at scale. It also helps predictions on student knowledge, dropout, and motivational state become much more accurate with additional data. By mining large amounts of data we gain a broader understanding of specific groups of students, which leads to better adaptivity and personalization for individuals.
It has many goals :
1-Predicting students' future learning behavior –
this goal can be achieved by creating student models that incorporate the learner's characteristics, including detailed information such as their knowledge, behaviours and motivation to learn.
2-Discovering or improving domain models
3-Studying the effects of educational support that can be achieved through learning systems.
4-Advancing scientific knowledge about learning and learners by building and incorporating student models, the field of EDM research and the technology and software used.

See: https://www.cmu.edu/datalab/getting-started/what-is-edm.html