A dynamic early warning method of student study failure risk based on fuzzy synthetic evaluation

Abstract

As more and more students fail in course studies, higher education is now facing challenges regarding increasingly lower course completion rates as well as overall graduation rates. However, failures in course studies is a comprehensive result of various factors, which is characterized by uncertainty. To deal with this issue, fuzzy sets theory and fuzzy logic are advantageous compared with traditional methods. In this study, based on dynamic student study process data, a fuzzy synthetic evaluation method for dynamic early warning student study failure risk is provided. For each student, three specific early warning factors: 1) student course participation, 2) assignment earned points, and 3) student attendance record, are selected as risk indicators, and the overall risk level is determined by a fuzzy synthetic evaluation approach, which can dynamically give the situation of risk as the evaluation time point changes. Finally, our obtained results show that the employed method is good for identifying at-risk students and exploring the risk reasons by showing the degrees of each early warning factors to the overall risk level. It is of significance for educators to timely apply corresponding strategic pedagogical interventions to help at-risk students avoid academic failure.