Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review.

Background: Early prediction of students’ learning performance using data mining techniques is an important topic these days. The purpose of this literature review is to provide an overview of the current state of research in that area.

Method: We conducted a literature review following a two-step procedure, looking for papers using the major search engines and selection based on certain criteria.

Improving the portability of predicting students' performance models by using ontologies.

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level attributes used to train them, which reduces the models' portability. To solve this issue, the use of high-level attributes with more semantic meaning, such as ontologies, may be very useful.