Learning Analytics

The Problem:

Simple, clear learning analytics—the use of data and models to predict student progress and performance, and the ability to act on that information—are rarely available to those directly responsible for a student’s success: the student, instructor and advisor. This lack of on-demand access hampers instructors’ ability to take action on performance issues arising in a course or program of study, and students’ ability to understand their progress.

The Challenge:

First, address and identify barriers to the adoption of learning analytics. Then foster the development and implementation of models and applications that extend the value of learning analytics to those directly involved in student learning success.

Intended Outcomes:

• Models/applications/interfaces for providing students, instructors and advisors with access to easy-to-use, actionable learning performance information and predictions at the level of the individual student, course and student’s program of study.
• Guidelines and resources for integrating such models/applications/interfaces in an institution’s overall learning analytics approach at scale (i.e., across the depth and breadth of the institution’s academic program).
• Evidence-based frameworks and roadmaps for the adoption of comprehensive learning analytics models that incorporate direct access to learning performance information by frontline users, such that institutions which have yet to implement comprehensive learning analytics models may more readily and reliably do so.