Student Learning Management System Interactions and Performance via a Learning Analytics Perspective

Student Learning Management System Interactions and Performance via a Learning Analytics Perspective by Gina Ricker MS, Kaplan University, 2013 BA, University of Maryland, Baltimore County, 2011 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Education, Technology and Design Walden University May 2019 Abstract Enrollment in full-time, virtual, K-12 schools is increasing while mathematics performance in these institutions is lacking compared to national averages. Scholarly literature lacks research studies using learning analytics to better predict student outcomes via student learning management system (LMS) interactions, specifically in the low performing area of middle school mathematics. The theoretical framework for this study was a combination of Hrastinski’s theory of online learning as online participation and Moore’s 3 types of interactions model of online student behavior. The purpose of this study was to address the current research gap in the full-time, K-12 eLearning field and determine whether 2 types of student LMS interactions could predict mathematics course performance. The research questions were developed to determine whether student clicks navigating course content page(s) or the number of times a student accessed resources predicted student performance in a full-time, virtual, mathematics course after student demographic variables were controlled for. This quantitative study used archived data from 238 seventh grade Math 7B students enrolled from January 8th–10th to May 22nd– 25th in two Midwestern, virtual, K-12 schools. Hierarchical regressions were used to test the 2 research questions. Student clicks navigating the course content pages were found to predict student performance after the effects of student demographic covariates were controlled for. Similarly, the number of times a student accessed resources also predicted student performance. The findings from this study can be used to advise actionable changes in student support, build informative student activity dashboards, and predict student outcomes for a more insightful, data-driven, learning experience in the future.Enrollment in full-time, virtual, K-12 schools is increasing while mathematics performance in these institutions is lacking compared to national averages. Scholarly literature lacks research studies using learning analytics to better predict student outcomes via student learning management system (LMS) interactions, specifically in the low performing area of middle school mathematics. The theoretical framework for this study was a combination of Hrastinski’s theory of online learning as online participation and Moore’s 3 types of interactions model of online student behavior. The purpose of this study was to address the current research gap in the full-time, K-12 eLearning field and determine whether 2 types of student LMS interactions could predict mathematics course performance. The research questions were developed to determine whether student clicks navigating course content page(s) or the number of times a student accessed resources predicted student performance in a full-time, virtual, mathematics course after student demographic variables were controlled for. This quantitative study used archived data from 238 seventh grade Math 7B students enrolled from January 8th–10th to May 22nd– 25th in two Midwestern, virtual, K-12 schools. Hierarchical regressions were used to test the 2 research questions. Student clicks navigating the course content pages were found to predict student performance after the effects of student demographic covariates were controlled for. Similarly, the number of times a student accessed resources also predicted student performance. The findings from this study can be used to advise actionable changes in student support, build informative student activity dashboards, and predict student outcomes for a more insightful, data-driven, learning experience in the future. Student Learning Management System Interactions and Performance via a Learning Analytics Perspective by Gina Ricker MS, Kaplan University, 2013 BA, University of Maryland, Baltimore County, 2011 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Education Technology and Design Walden University May 2019 Dedication It is with gratitude and passion that I dedicate this dissertation to my mother, who was taken by cancer before she was able to see my first college commencement. While she may not witness this culmination of my knowledge and hard work, I know she would be proud of my unrelenting spirit and indefatigable drive that has fueled my success. I will continue to approach future endeavors with such enthusiasm. Acknowledgments My family and friends deserve special recognition for supporting me along this journey and my never-ending quest for knowledge. Without them, I would not be the confident, strong, and determined human I am today. I would also like to thank my committee for their unquestionable patience and invaluable knowledge which carried me through the dissertation process. Lastly, I would like to acknowledge Dr. Alyssa Walters who provided me with consistent, genuine personal and professional advice, support, and objectivity when I needed it most. To all of you, I have a deep appreciation for your support and investment in education research. From my heart, I thank you all.