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Revisiting Predictive Value of BlackBoard Learn Analytics: Determining Communicative Avenues That Best Engage Online Learners
PROCEEDING

, University of Houston-Downtown, United States

EdMedia + Innovate Learning, in Amsterdam, Netherlands ISBN 978-1-939797-42-1 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC

Abstract

Biology 1310/1312 core life and physical science courses allow completion of core requirements. Over 13,000 undergraduates may select these science electives. Thus, these first-year barrier courses offer fruitful learning and innovation possibilities for all non-science majors therby contributing to higher UHD retention and graduation rates. UHD Biology 1310/1312 courses clearly impact student success metrics just as “gateway” courses impact retention/graduation. BlackBoard Learn (BBL) analytics offers ways to understand and predict the effectiveness of communicative avenues intentionally integrated into the BBL course as learning strategies. By using quantitative data analytics within BBL and correlation statistics, instructors potentially can predict which streams of communication contribute to student success and ease of understanding of course intent for the users/learners. Analytics such as these can influence course redesign, student success, and inform course redesign thereby increasing course effectiveness. Instructors using BBL analytics support intentional student learning factors.

Citation

Parker, M.J. (2019). Revisiting Predictive Value of BlackBoard Learn Analytics: Determining Communicative Avenues That Best Engage Online Learners. In J. Theo Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1290-1296). Amsterdam, Netherlands: Association for the Advancement of Computing in Education (AACE). Retrieved January 19, 2021 from .