Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study

For nearly six decades, researchers have been studying the issues of student persistence and retention in higher education. Despite the decades of research and projects to improve retention, overall retention figures have remained between 45% and 50%. With the contentious debates over the Higher Education Reauthorization Act in 2005 and increasingly constrained federal and state budgets, the demand for increased accountability from the students, families, and the legislature has required higher education institutions to renew their focus on issues of academic quality, cost effectiveness, student retention, and graduation rates. This research expands traditional retention and academic success studies by introducing additional student behavioral data from the course management system (CMS). By examining more than 70,000 records from the CMS, the researcher sought to determine which information, if any, was the strongest indicators of student success. The initial portion of this study reduced the twenty CMS variables into five factors. Subsequent portions of the study utilized regression techniques to identify the key variables necessary to predict academic success. The reduced main model accurately predicted nearly sixty-six percent of the students needing help. A more focused freshmen-only model was able to accurately predict nearly eighty percent of the students needing help. All of the models were validated through the use of an additional sample. While much work remains in the use of analytics to predict student success, this study provides an initial validation that institutions can become more effective by utilizing the course management system as a predictor of academic success. Through the use of academic data, students, faculty members, and academic advisors can be better equipped with additional information to improve the effectiveness of their portion of the academic environment. While additional work remains to develop this research study into a scalable, institutional solution, the framework has been set for utilizing academic information to improve the institution’s academic success and retention efforts. Considering the growing interest in retention within higher education, it is hoped that additional research will conducted to enhance the model within this study, providing academic decision-makers access to a more comprehensive set of tools and information