Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data

Abstract

Analytics in education has been received much attention over the past decade. It is necessary to maintain high retention rate in any institutions of higher education, therefore several attempts on the application of analytics have been done for this problem. To detect students at high drop-out risk early and intervene them effectively, utilizing the educational big data can be useful. In this paper, an automatic detection method of academically at-risk students by using log data of learning management systems is considered. Some well-known machine learning methods are used to build a predictive model of student performance evaluated by GPA. By using actual data set, we investigate an availability of the proposed method and discuss its ability to early detection of off-task behavior. The experimental results indicated that some characteristics of behavior about learning which affect the learning outcomes can be detected with only the online log data. Furthermore, comparative importance of explanatory variables obtained by the approach would help to estimate which variable affects comparatively to the learning outcome and it can be used in institutional research.