An adaptive user interface for open educational content

Abstract

For my Masters of Engineering thesis project, I have developed a framework of meta- data around open educational resources that are used by students to master a given concept. Based on the assumption that a course can be broken down into specfic concepts and that there are several resources available to students both on sites such as OpenCourseWare and also peer-managed resources like Wikipedia, I develop a model for "relevance" of a resource to the learning of a given concept, where relevance is defined by assignment to a concept by a teacher as well as usage data from students viewing the resources. I combine both active feedback (clicking a "like" button) and passive feedback (view duration and count across all users) to score each resource for a given concept. This score is then used to create an adaptive user interface that indicates which resources may be the most relevant for students learning a given concept based on their peers' feedback. In addition to designing this framework, I provide an implementation of the adaptive user interface on Apple's iPad device, and I provide an evaluation of the educational benefits of this adaptive interface by designing and executing a controlled user study on students in MIT's introductory physics course.