The ability to predict student performance in a course or program creates opportunities to
improve educational outcomes. With effective performance prediction approaches …

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 …

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 …

It is generally acknowledged that engagement plays a critical role in learning. Unfortunately,
the study of engagement has been stymied by a lack of valid and efficient measures. We …

Predictive models of student success in Massive Open Online Courses (MOOCs) are a
critical component of effective content personalization and adaptive interventions. In this …

Computerized adaptive testing (CAT), as a tool that can efficiently measure student's ability,
has been widely used in various standardized tests (eg, GMAT and GRE). The adaptivity of …

There has been growing interest in assessing computational thinking (CT) across diverse
learners beyond the traditional forms of tests and assignments. Learning games offer the …

In this paper, we investigate the correspondence between student affect and behavioural
engagement in a web-based tutoring platform throughout the school year and learning …

Abstract Information and communication technology (ICT)‐enhanced research methods
such as educational data mining (EDM) have allowed researchers to effectively model a …

Quantitative Field Observations (QFOs) of student behaviors and affective states in
classroom environments. Formerly known as the Baker Rodrigo Observation Method …