The Silicon-Based Classroom and Carbon-Based Logic–Cognition and Prediction of Online Learning Behavior

The registered user base of Massive Open Online Course (MOOC) platforms has surpassed 320 million, yet certification rates remain persistently low at 6.8%. This stark contradiction between the expansion of digital education scale and the collapse of quality reveals a systemic deficiency in integrating dynamic learning behavioral indicators within online education systems. Leveraging multidimensional MOOC datasets, this study innovatively develops a dynamic prediction model using the XGBoost algorithm. Empirical results demonstrate the model's exceptional performance in certification rate prediction, achieving industry-leading metrics: Root Mean Square Error (0.12), Coefficient of Determination (0.86), and Mean Absolute Error (0.09). Critical findings include: (1) Content Engagement Rate (CER) exhibits a strong positive correlation with certification likelihood <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\beta$</tex> =0.82, p<0.01); (2) Course duration manifests a distinctive nonlinear effect—short-term courses (<4 weeks) attain peak certification rates of 23.7% (95% CI: 22.4%-25.1%), followed by exponential decay as duration extends, stabilizing beyond 8 weeks <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sigma$</tex> <0.5). The proposed data-driven framework not only enables proactive certification rate forecasting but also establishes an early-warning threshold configuration mechanism, thereby providing an AI-powered decision support system for dynamic optimization of pedagogical strategies and resource allocation in online education ecosystems.