Andere Kunden interessierten sich auch für
![Predictive Analytics in Educational Data Mining With Machine Learning]( "Predictive Analytics in Educational Data Mining With Machine Learning")
Predictive Analytics in Educational Data Mining With Machine Learning45,99 €![DATA ANALYTICS-1]( "DATA ANALYTICS-1")
DATA ANALYTICS-124,99 €![DATA ANALYTICS ON LEADERSHIP ROLES AND ITS IMPACT IN EDUCATION]( "DATA ANALYTICS ON LEADERSHIP ROLES AND ITS IMPACT IN EDUCATION")
DATA ANALYTICS ON LEADERSHIP ROLES AND ITS IMPACT IN EDUCATION40,99 €![Burnout Intervention Mechanisms for Online Learning Processes Enabled by Predictive Learning Analytics]( "Burnout Intervention Mechanisms for Online Learning Processes Enabled by Predictive Learning Analytics")
Burnout Intervention Mechanisms for Online Learning Processes Enabled by Predictive Learning Analytics176,99 €![Openness and Innovation in E-Learning. Deploying Learning Analytics to Support Study Success in Higher Education]( "Openness and Innovation in E-Learning. Deploying Learning Analytics to Support Study Success in Higher Education")
Openness and Innovation in E-Learning. Deploying Learning Analytics to Support Study Success in Higher Education15,95 €![AI AND STUDENT LEARNING IN EMERGING HIGHER EDUCATION SYSTEMS]( "AI AND STUDENT LEARNING IN EMERGING HIGHER EDUCATION SYSTEMS")
AI AND STUDENT LEARNING IN EMERGING HIGHER EDUCATION SYSTEMS71,99 €![Visual Analytics]( "Visual Analytics")
Visual Analytics14,99 €
This book investigates the pressing issues of learner engagement and academic attrition in online education environments. With a focus on technical learners in Karnataka, India, the research introduces the EDU Insight framework to analyze key behavioral and demographic factors impacting student performance. It proposes a score prediction model using random forest and synthetic data augmentation through tabular GANs to forecast learner outcomes with high accuracy. Additionally, a hybrid ensemble learning approach incorporating weighted classifiers and meta-learners is developed to further refine predictive performance. To support personalized learning, an autoencoder-based collaborative filtering recommendation system is introduced, tailoring course suggestions based on learner behavior and demographics. The study's integrated use of learning analytics and machine learning contributes novel methodologies for predictive accuracy, data privacy, and personalized learning interventions in online education systems.