Automating individualized formative feedback in large classes based on a directed concept graph

Abstract

Student learning outcomes within courses form the basis for course completion and time-to-graduation statistics, which are of great importance in education, particularly higher education. Budget pressures have led to large classes in which student-to-instructor interaction is very limited. Most of the current efforts to improve student progress in large classes, such as "learning analytics," (LA) focus on the aspects of student behavior that are found in the logs of Learning Management Systems (LMS), for example, frequency of signing in, time spent on each page, and grades. These are important, but are distant from providing help to the student making insufficient progress in a course. We describe a computer analytical methodology which includes a dissection of the concepts in the course, expressed as a directed graph, that are applied to test questions, and uses performance on these questions to provide formative feedback to each student in any course format: face-to-face, blended, flipped, or online. Each student receives individualized assistance in a scalable and affordable manner. It works with any class delivery technology, textbook, and learning management system.