Universities are awash with data about students that could function as early warning signs that a student may need help. These data sources range from the highly personal, for example tutors observing that a student appears to be having a bad day, to the highly systematised, for example automatic early warnings based on a metric …

Continue reading Diagnosing student ‘risk’: categorising learning analytics to prevent early withdrawal