Coursera, Inc.

Emily Glassberg Sands

Chuong Do

Kang Chen

Airong Cai

Alan Hickey

Rachel Reddick

Hyun Bin Kim

Vinod Bakthavachalam

Techniques are described by which learner skill can be estimated over time, even in the presence of large data sets. A rating system can be used in which a learner's submission of a course assessment can be interpreted as a single match, where the learner and the course assessment are competing against each other. This approach can be applied to online learning platforms that include very many learners who have submitted assignments very many times in many courses. The described techniques provide reliable estimates of assessment difficulty and learner ability. The described scoring framework may be used as a basis for various applications that account for a learner's ability, such as adaptive diagnostic tests and personalized recommendations.

Using a glicko-based algorithm to measure in-course learning

In one general aspect, a computer-implemented method can include receiving, by a computer system, a name for a course, receiving, by the computer system, a selection of a skill, storing, in a first database included in the computer system, the skill tagged to the course, creating a skills graph including tagged skills for a plurality of courses including the course, the skills graph providing a graph of a skills hierarchy for the course, storing, in a second database included in the computer system, the skills graph, receiving, by the computer system, a query for the skill, and in response to receiving the query, traversing the skills graph, the traversing identifying the skill as a tagged skill for at least the course, and based on identifying the skill as a tagged skill for the course, identifying the name for the course.

Content discovery using a skills graph

Growing community of inventors

Menlo Park, CA, United States of America

Emily Glassberg Sands