Optimization of Intelligent Systems for The Development of Algorithms Based on Graph Theory
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This research involves the study and analysis of information and data relating to enrolled and transient students on MOOC platforms, by applying graph theory to track students’ learning pathways and educational potential in order to identify their levels and difficulties, The study focuses on developing an applied scientific framework for use in unsupervised learning, utilising the Hidden Markov Model (HMM). This process benefits lecturers in scientific disciplines and supports intelligent systems that help students improve their practical and academic performance through the use of computers. In this case, we treat learning instances as observable events, and we can model the learning process using the HMM model.
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