Pedagogical Intervention Practices: Improving Learning Engagement Based on Early Prediction

Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning engagement is an open question. Furthermore, identifying students at risk of failure is only the first step in truly addressing this issue. It is important to create actionable predictive model in the real-world contexts to design interventions. In this paper, we first extracted features from students' learning activities and study habits to predict students' performance in the Kung Fu style competency education. Then, we proposed a TrAdaBoost-based transfer learning model, which was pretrained using the data of the former course iteration and applied to the current course iteration. Our results showed that the generalization ability of the prediction model across the teaching iterations is high, and the model can achieve relatively high precision even when the new data are not sufficient to train a model alone. This work helped in timely intervention toward the at-risk students. In addition, two intervention experiments with split-test were conducted separately in Fall 2017 and Summer 2018. The statistical tests showed that both behavior-based reminding intervention and error-related recommending intervention that based on early prediction played a positive role in improving the blended learning engagement.