Learning Analytics or Educational Data Mining? This is the Question...

Abstract

In full expansion, a vital area such as education could not remain indifferent to the use of information and communication technology. Over the past two decades we have witnessed the emergence and development of e-learning systems, the proliferation of MOOCs, and generally the rise of Technology Enhanced Education. All of these contributed to generation and storage of unprecedented volumes of data concerning all areas of learning.

At the same time, domains such as data mining and big data analytics have emerged and developed. Their applications in education have spawned new areas of research such as educational data mining or learning analytics.

As an interdisciplinary research area Educational Data Mining (EDM) aims to explore data from educational environment to build models based on which students' behavior and results are better understood. In fact, EDM is a complex process that consists of a few steps grouped in three stages: data preprocessing, modelling and postprocessing. It transforms raw data from educational environments in useful information that could influence in a positive way the educational process.

According to Society for Learning Analytics Research (SoLAR) which took over the wording of the first International Conference on Learning Analytics and Knowledge, learning analytics is ”the measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs” (Siemens, 2011).

This paper proposes a comparative study of the two concepts: EDM and learning analytics.

Due to certain voices in the scientific environment that claim that the two terms refer to the same thing, we want to emphasize the similarities and differences between them, and how each one can serve to raise the quality in educational processes.