M.A. Thesis:

Educational consultation recommendation system based on collaborative filtering for improving learners' performance in e-learning

the education sector, the quality of learners' performance can have a great impact on the development of a country. Try to find out in this area Strategies for increasing the educational efficiency of students in educational institutions. However, there are some recommended systems in the field Different e-commerce is used to predict and recommend different items to the user according to his behavior, but few systems There are advisers who have been used to train and predict performance and improve user learning. In this work, introduce An advisory system has been developed in the field of education. As we know, in an appropriate educational system, students or students During a semester, they will be tested in a variety of ways, such as classroom activities, problem solving, midterm exam and final exam. With Paying attention to these scores that a student or student earns is a final score for him. In this area, a smart system that can Succession for better promotion and academic achievement, using these suggestions to know its strengths and weaken the weaknesses, Does not exist. For this reason, it has been decided that an advocate system based on intelligent classification (machine learning) Designed to be able to advise with the introduction of specific information and characteristics of the individual. So to classify the best features Different classification algorithms have been used and compared with other methods. In this regard, learners They are also involved in training and they exchange leaflets and educational files between themselves that can be used by the advocate system, Proposed the most suitable syllabus resources from these existing resources, which have been created for many years, than learners. Get the best of them.

Keywords: Advisory systems, Improving educational quality, classification, data mining