Proposal Title

When Experience Meets Expectation: A Framework for Using Surveys and Learning Analytics to Understand and Predict Course Satisfaction

Proposal Abstract

Course satisfaction is what happens when experience meets expectation. Too often, however, course evaluation surveys are delivered at the end of a course, when it is too late to make mid-term course corrections, and/or are not designed with a view to understanding student expectations, which is crucial if survey results are going to be meaningful and actionable. The course evaluation process requires knowledge of the extent to which design elements are meeting student expectations, and in a way that is early enough to allow for responsiveness on the part of instructors and instructional designers.

We will present a flexible framework for course evaluation that includes a survey instrument, predictive analytics, and a methodology that allows the framework to be applied in a wide variety of blended and online learning environments. By correlating student behaviors to specific areas in which a course may fail to meet student expectations, it becomes possible to predict course dissatisfactions early enough to implement interventions that both improve the learning experience, and decrease frictions that impede student success.

Location

Room 1220 A

Publication Type and Release Option

Presentation (Open Access)

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Mar 25th, 2:00 PM Mar 25th, 2:45 PM

When Experience Meets Expectation: A Framework for Using Surveys and Learning Analytics to Understand and Predict Course Satisfaction

Room 1220 A

Course satisfaction is what happens when experience meets expectation. Too often, however, course evaluation surveys are delivered at the end of a course, when it is too late to make mid-term course corrections, and/or are not designed with a view to understanding student expectations, which is crucial if survey results are going to be meaningful and actionable. The course evaluation process requires knowledge of the extent to which design elements are meeting student expectations, and in a way that is early enough to allow for responsiveness on the part of instructors and instructional designers.

We will present a flexible framework for course evaluation that includes a survey instrument, predictive analytics, and a methodology that allows the framework to be applied in a wide variety of blended and online learning environments. By correlating student behaviors to specific areas in which a course may fail to meet student expectations, it becomes possible to predict course dissatisfactions early enough to implement interventions that both improve the learning experience, and decrease frictions that impede student success.