In general, research confirms that learning is more effective when students obtain feedback regarding their learning progress. Currently, new versions of e-learning platforms include indicators that provide some static feedback mechanisms and help both learners and educators in planning their learning strategies. This paper explains the usage of indicators in current e-learning systems, generates a taxonomy for their classification, and studies their influence on student performance. Also, it provides a study which is based on the combination of a user-based evaluation process that facilitates data collection and data mining algorithms to infer association rules between learning variables and performance. The results highlight how procrastination influences negative learning performance and how time-related indicators are tightly coupled with students' performance in e-learning platforms.

learning analytics, feedback, educational data mining, procrastination