BiLSTM-Attention-Delta: A Novel Framework for Predicting Dropout in MOOCs Within Big Data Environments

The high dropout rate on online education platforms like MOOCs is a significant challenge for modern education systems. This wastes resources and diminishes the course's credibility, impacting educational goals and limiting learners' personal development opportunities. Research on predicting dropout rates in MOOCs has achieved significant milestones, with effective predictive models and analysis of influencing factors to reduce dropout rates. However, challenges remain in ensuring data quality, safeguarding personal information, enhancing model interpretability, and addressing implementation difficulties, especially in the context of big data. This study focuses on analyzing big data to develop an AI-powered intelligent education system capable of monitoring and predicting student learning behavior to reduce dropout rates, while also personalizing the learning process and improving the learner's experience. However, the process of extracting big data from MOOCs poses numerous challenges, including ensuring data quality, integrity, and the ability to handle diverse and massive data. Model interpretability and deployment are also complex, requiring rigorous technical solutions and data management to optimize learning quality and experience. To tackle data processing and deployment challenges, the study introduces the BiLSTM-Attention-Delta framework. This model improves dropout prediction by over 10% compared to baselines, optimizes training and prediction times, and leverages the Delta big data architecture (BDA) for effective deployment in MOOCs.