Purpose: This paper aims to investigate the relationship between the students' digital activities and their academic performance through two stages. In the first stage, students' digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the…

Scholarly interests in developing personalized learning analytics dashboards (LADs) in universities have been increasing. LADs are data visualization tools for both teachers and learners that allow them to support student success and improve teaching and learning. In most LADs, however, a teacher-centric, institutional view drives their designs,…

Online learning systems allow learners to freely access learning contents and record their interactions throughout their engagement with the content. By using data mining techniques on the student log data of those systems, it is possible to examine learning behavior and reveal navigation patterns through learning contents. This study was aimed at…

Collaboration is a complex, multidimensional process; however, details of how multimodal features intersect and mediate group interactions have not been fully unpacked. Characterizing and analyzing the temporal patterns based on multimodal features is a challenging yet important work to advance our understanding of computer-supported collaborative…

Purpose: The paper aims to present a new two stage local causal learning algorithm -- HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm,…