A Large Sample Comparison of Grade Based Student Learning Outcomes in Online vs. Face-to-Face Courses

Abstract

Comparisons of grade based learning outcomes between online and face-to-face course formats have become essential because the number of online courses, online programs and institutional student enrollments have seen rapid growth in recent years. Overall, online education is largely viewed by education professionals as being equivalent to instruction conducted face-to-face. However, the research investigating student performance in online versus face-to-face courses has been mixed and is often hampered by small samples or a lack of demographic and academic controls. This study utilizes a dataset that includes over 5,000 courses taught by over 100 faculty members over a period of ten academic terms at a large, public, four-year university. The unique scale of the dataset facilitates macro level understanding of course formats at an institutional level. Multiple regression was used to account for student demographic and academic corollaries—factors known to bias course format selection and grade based outcomes—to generate a robust test for differences in grade based learning outcomes that could be attributed to course format. The final model identified a statistical difference between course formats that translated into a negligible difference of less than 0.07 GPA points on a 4 point scale. The primary influence on individual course grades was student GPA. Interestingly, a model based interaction between course type and student GPA indicated a cumulative effect whereby students with higher GPAs will perform even better in online courses (or alternatively, struggling students perform worse when taking courses in an online format compared to a face-to-face format). These results indicate that, given the large scale university level, multi course, and student framework of the current study, there is little to no difference in grade based student performance between instructional modes for courses where both modes are applicable.