Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…

Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules with different characteristics, those designed from a course-specific perspective have recently provided better outcomes. Hence, the…

With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…

A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are…