Flipping Online Analytics Classes: Achieving Parity with Their Face‐To‐Face Counterparts. (13th February 2020)

• Record Type: Journal Article
• Authors: Swart, William W.; MacLeod, Kenneth R.
• Other Names: Ozgur Ceyhun guestEditor.
• Is Part Of: Decision sciences journal of innovative education. Volume 18:Number 1(2020:Jan.)
• Page Start: 119
• Page End: 137
• Publication Date: 2020-02-13
• Subjects: Analytics -- Distance Learning -- and Problem‐Based Learning; Business education -- Periodicals; 658.00711
• DOI: 10.1111/dsji.12200
• Languages: English
• ISSNs: 1540-4595

Abstract

ABSTRACT: We show how the principles of flipped learning that have been successfully applied to analytics classes taught face‐to‐face (F2F) at the undergraduate and graduate levels were emulated in corresponding online classes. Student satisfaction in the online flipped analytics classes was compared to student satisfaction in the F2F flipped analytics classes. Data were collected between the Spring 2016 and Fall 2018 semesters and involved two instructors with a sample of 726 students. The results of an independent samples t ‐test showed that there was no significant difference in satisfaction between the online and F2F offerings. A binary logistics regression analysis on the data revealed that whether the flipped course was taught F2F or online had no significant effect on students recommending the course to their peers. The results suggest that flipped learning is transferrable to online analytics courses and yields student satisfaction at par with equivalent F2F flipped courses.