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Activity Number: 608
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #308444
Title: A Bayesian Assessment of Distance Learning in Graduate Biostatistics Courses
Author(s): Jo A. Wick*+ and Henry Yeh and Byron Gajewski
Companies: University of Kansas Medical Center and University of Kansas Medical Center and Univ of Kansas-Medical Center
Keywords: meta-analysis ; hierarchical model ; equivalence test ; Bayesian modeling
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 instruction in statistics is mixed and its interpretation is not clear. Despite the difficulties associated with performing a randomized controlled experiment for evaluating learning, we have minimized the intra-instructor variation by providing courses offered in the traditional setting to a set of students simultaneously via online technology that allows for two-way interaction between instructor and all students enrolled in the course. Our primary objective was to compare student performance between the two teaching modes among seven graduate-level statistics courses. We present a novel comparison using a Bayesian hierarchical model to perform a meta-analysis of equivalence. The frequentist mixed model approach was also conducted for reference. The results of Bayesian and frequentist methods agree and suggest equivalence in student performance. Discussion is given to barriers to instruction and learning using the applied online teaching technology.


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