This paper aims to provide the reader with a comprehensive background for understanding
current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its …

Educational data mining (EDM) is an emerging interdisciplinary research area that deals
with the development of methods to explore data originating in an educational context. EDM …

In recent years, there has been increasing interest in using the methods of educational data
mining (EDM) and learning analytics (LA) to study and measure learner cognition. In this …

Data mining techniques are used to extract useful knowledge from raw data. The extracted
knowledge is valuable and significantly affects the decision maker. Educational data mining …

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon
methodologies for extracting useful knowledge from data and there are several useful KDD …

Learning to reason through organic reaction mechanisms is challenging for students
because of the volume of reactions covered in introductory organic chemistry and the …

Rare association rules are those that only appear infrequently even though they are highly
associated with very specific data. In consequence, these rules can be very appropriate for …

The increasing amount of data from web systems data is becoming one of the most valuable
resources for information retrieval and knowledge discovery. The huge content of …

Pervasive and ubiquitous computing have the potential to make huge changes in the ways
that we will learn, throughout our lives. This paper presents a vision for the lifelong user …

This chapter describes three characteristics of Learning Analytics work that distinguish it
from prior educational research to give readers a concise overview of what makes learning …