الفهرس 
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Abstract
This thesis is combining the main learning styles for introducing PLSM, and using it to apply data mining methods for establishing and extracting knowledge. Gain ratio is used as feature selection method to determine most relevant features for domain goal. Then decision tree classifier is applied on selected features to extract rules and build knowledge base. Accuracy of extracted rules is estimated to fix rules which have high accuracy. PLSM features were ranked based on gain ratio related to three domain goals. Teacher should be taken in consideration features’ priority when courses are designed at began. Result of first domain goal ”creative thinking” showed that global style has the highest gain, while visual style has the lowest gain. Result of second domain goal ”online impression” showed that sequential style has the highest gain, while explorer style has the lowest gain. Result of third domain goal ”Most relevant subject” showed that global style has the highest gain, while adaptor style has the lowest gain. The heads of relevant topics (not exceeding 10) The thesis is restructured in 5 chapters. Firstly introduction which is displayed study in terms of: background, problem statement, objectives, questions, and significance. Following chapter included three sections: first section: introduces data mining terminology, needs data mining domain for confluence of multiple disciplines, view knowledge discovery process models to inferred steps in recent study, focusing on educational data mining method used already here, data dimensionality reduction, Rule-Based Classification, Classifier Evaluation ,and several machine learning described. Second section, an introduction of learning styles is presented; describing used learning styles models taxonomies, clarify models are used in proposed model, implications of learning styles in education, criticisms, and challenges in learning styles field. Third section was covered e-learning courses, presents e-Learning definition, types, process designing of e-learning courses are described, e-learning courses practices, tools, techniques, and technologies, and e-learning challenges is provided. Chapter 3: proposed advisory system procedures are described in details which were derived from knowledge discovery process models (KDP). Chapter 4: Presents applications of proposed system procedures and their results. Chapter 5: includes conclusions and future work.