Correlation Structure Among Key Constructs in Online Blended Learning: A Multivariate Analysis
Wang Lei, Chen Xin
Journal of Information Organization, Vol. 15, No. 4, 2025.
DOI: https://doi.org/10.6025/jio/2025/15/4/163-170
Link: https://www.dline.info/jio/fulltext/v15n4/jiov15n4_1.pdf
Abstract:
The paper titled â€œResearch on Gaussian Mixture Computational Learning Mode Based on MOOC Online
Educationâ€ 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 â€œBusiness Etiquetteâ€ 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.