This research aims to improve student learning effectiveness, efficiency, and enjoyment in online courses by using online learning data to provide constructive feedback to course developers and instructors. The feedback provides assistance with the design and improvement of course activities and interactive instruction. Online course development is often guided primarily by the intuition of the instructor. This research seeks to enable course improvements using a data-driven approach by developing methods to measure the effects of course redesign.
This project integrates diagnostic feedback on course content and activities, analytic methods and course-inspection tools for discovering barriers to student learning and opportunities for course improvement, and authoring tools to translate discoveries to course improvements. An Integrated Development Environment with Analytics (IDEA) that allows course developers and instructors to validate courseware contents before actual use will be developed. Once the courseware becomes available online and used by students, IDEA will support the use of logged data and analytics to discover barriers to learning, to hypothesize modifications in the underlying cognitive model of learning, and to evaluate and select the modifications that best predict the data. IDEA will provide performance profiling to developers and instructors to summarize students' learning and identify issues to be improved by analyzing learning curves and modifying knowledge component models. IDEA will also provide developers with a tight connection between performance profiling and courseware contents so that developers can review and modify troublesome courseware contents.
