A Fast Measure for Identifying At-Risk Students in Computer Science

摘要

How do we identify students who are at risk of failing our courses? Waiting to accumulate sufficient assessed work incurs a substantial lag in identifying students who need assistance. We want to provide students with support and guidance as soon as possible to reduce the risk of failure or disengagement. In small classes we can monitor students more directly and mark graded assessments to provide feedback in a relatively short time but large class sizes, where it is most easy for students to disappear and ultimately drop out, pose a much greater challenge. We need reliable and scalable mechanisms for identifying at-risk students as quickly as possible, before they disengage, drop out or fail. The volumes of student information retained in data warehouse and business intelligence systems are often not available to lecturing staff, who can only observe the course-level marks for previous study and participation behaviour in the current course, based on attendance and assignment submission. We have identified a measure of "at-risk" behaviour that depends upon the timeliness of initial submissions of any marked activity. By analysing four years of electronic submissions over our school's student body we have extracted over 220,000 individual records, spanning over 1900 students, to establish that; early electronic submission behaviour provides can provide a reliable indicator of future behaviour. By measuring the impact on a student's Grade Point Average (GPA) we can show that knowledge of assignment submission and current course level provides a reliable guide to student performance.