Model predictive distributed learning processes to student performance within a contingency theory framework using partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA-PLS).
Higher education trends indicate a need and desire for students to complete a degree through online modalities. In some universities students taking online distance learning courses increased to 70% of enrollments. Interactive techniques within the online modalities are assumed to enhance student outcomes. The specific problem is an unknown generalizable and predictive application of online interactive processes (tools and techniques) to enhance adult learning performance. The purpose of this quantitative study of archival data is to statistically model predictive distributed learning processes to student performance within an contingency theory framework using partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA-PLS) of subpopulations (course designation, course degree level). Archival datasets are from two graduate and two undergraduate core courses over a year period within the Embry-Riddle Aeronautical University's College of Aeronautics.