Mining educational big data to develop an early alert dynamic model of academically at-risk students: A proof of concept

Educational big data has great predictive power to identify academically at-risk students. Based on fully mining educational big data, this study developed an early alert dynamic model of academically at-risk students to confirm and extend this proposition. An empirical test was carried out to validate the reliability and validity of the dynamic model by using data analysis methods. The results proved that the developed model could precisely identify academically at-risk students, scientifically assess their academic risk status, and dynamically provide personalised interventions. The overall prediction success rate of the developed model was 80.8%. In addition, application reflections were discussed to shed light on future implementation.