Journal of Information Organization Wang Lei, Chen Xin 15 4 2025 https://doi.org/10.6025/jio/2025/15/4/163-170 https://www.dline.info/jio/fulltext/v15n4/jiov15n4_1.pdf The paper titled “Research on Gaussian Mixture Computational Learning Mode Based on MOOC Online Education” explores the integration of Gaussian Mixture Models (GMMs) and the Expectation Maximization (EM) algorithm into a blended learning framework centered on MOOCs (Massive Open Online Courses). The authors propose using GMMs to model complex learning behaviors and environmental variations, particularly in video based educational content, by distinguishing background (typical) from foreground (atypical) patterns. The EM algorithm is employed to estimate model parameters via iterative unsupervised learning, thereby improving convergence and adaptability compared to conventional methods such as K-means. The study also emphasizes a student centered blended learning approach, combining micro courses, MOOCs, VR/AR technologies, and social media platforms to enhance engagement and comprehension. An experiment involving a “Business Etiquette” course demonstrates that blended MOOC based learning increases student satisfaction, motivation, and outcomes, despite minor challenges like internet access or digital literacy. Simulations in MATLAB using synthetic Gaussian data validate the efficacy of the proposed computational model, demonstrating that adaptive learning rates and prior probability estimation significantly improve algorithm performance. The paper concludes that the GMM-EM framework offers a flexible, scalable solution for modeling educational data and optimizing online learning environments, advocating for careful parameter tuning to avoid ambiguity. Overall, the research bridges computational statistics and modern pedagogy to advance personalized, data driven online education.