Abstract Resumen Background: Early prediction of students' learning performance using
data mining techniques is an important topic these days. The purpose of this literature …

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 …

Over the past ten years, we have worked in a collaboration between educators and
computer scientists at the University of Illinois to imagine futures for education in the context …

We introduce DAiSEE, the first multi-label video classification dataset comprising of 9068
video snippets captured from 112 users for recognizing the user affective states of boredom …

In the field of education, every institution stores a significant amount of data in digital form on
the academic performance of students. If this data is correctly analysed to discover any …

Adaptive systems refer to autonomous interactive systems that adjust their behavior and
functionality to environmental changes. In e-learning context, adaptive e-learning systems …

Although prior research has highlighted the significance of representations for mathematical
learning, there is still a lack of research on how students use multimodal external …

Extracting and understanding affective states of subjects through analysis of face videos is of
high consequence to advance the levels of interaction in human-computer interfaces. This …

Abstract e-Learning industry is rapidly changing and the current learning trends are based
on personalized, social and mobile learning, content reusability, cloud-based and talent …

The COVID-19 epidemic has had a huge impact on education, causing a quick move to
online learning environments. Students had to adjust to a virtual learning environment as a …