Quality of Student Learning: An Inference Approach for Measuring Quality of Online Education

• In Event: AERA Poster Session 4
  In Poster Session: Emerging Avenues for Research in Online Learning

Abstract

The quality of online higher education is difficult to evaluate. Many quality standards and benchmarks have been defined, but there is little scientific evaluation in the literature. This paper proposes a quantitative approach for evaluating the quality of online English composition courses by analyzing students’ learning outcomes, including their academic performance and self-reported measures of satisfaction and learning experiences. Four types of learners (Low Attainment, Positive Experience, Mixed Experience, and Negative Experience Learners) were identified, each with different perceptions of the quality of their online learning. This study proposes the quality of online courses can be inferred by the distribution of these learner types and offers a baseline of course quality that can support educators as they improve their courses

Authors

• Ying-Ying Kuo, George Mason University

• Jessica R. Matthews, George Mason University