This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…

One of the most challenging tasks in the field of Educational Data Mining (EDM) is to cluster students directly based on system-student sequential moment-to-moment interactive trajectories. The objective of this study is to build a general temporal clustering framework that captures the distinct characteristics of students' sequential behaviors…

Given the wide range of ability (academic, linguistic and cultural) in classrooms differentiated instruction is often difficult to manage. District and building level leadership can play an important role by providing the vision and support needed to implement Whole School Cluster Grouping (WSCG), the innovative scheduling approach described in…