A Bayesian Analysis of Synchronous Distance Learning versus Matched Traditional Control in Graduate Biostatistics Courses. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Wick, Jo A.; Yeh, Hung-Wen; Gajewski, Byron J.
• Is Part Of: American statistician. Volume 71:Issue 2(2017)
• Page Start: 137
• Page End: 144
• Publication Date: 2017-04-03
• Subjects: Hierarchical models -- Online learning -- Statistics education; Statistics -- Periodicals; 001.42205
• DOI: 10.1080/00031305.2016.1247014
• Languages: English
• ISSNs: 0003-1305

Abstract

ABSTRACT: Distance learning can be useful for bridging geographical barriers to education in rural settings. However, empirical evidence on the equivalence of distance education and traditional face-to-face (F2F) instruction in statistics and biostatistics is mixed. Despite the difficulty in randomization, we minimized intra-instructor variation between F2F and online sections in seven graduate-level biostatistics service courses in a synchronous (live, real time) fashion; that is, for each course taught in a traditional F2F setting, a separate set of students were taught simultaneously via online learning technology, allowing for two-way interaction between instructor and students. Our primary objective was to compare student performance in the two courses that use these two teaching modes. We used a Bayesian hierarchical model to test equivalence of modes. The frequentist mixed model approach was also conducted for reference. The results of Bayesian and frequentist methods agree and suggest a difference of less than 1% in average final grades. Finally, we discuss barriers to instruction and learning using the applied online teaching technology.