Educational and technology based learning is the turning point of learning and so is utilizing networks for design, presentation, selection, management and development which includes learners, specialists and content providers. A large volume of data which is produced in user interaction with learning management systems, student selected courses and their course grades are stored. These data include valuable information for studying, analyzing student behavior and offering consulting services. Electronic learning systems need virtual consultants and online associate specialists because of user-teacher distance and lack of related assistance in order to help students make better decisions and improve the learning quality level. This study aims at gaining more experience than is acquired by an associate specialist and dean by means of data mining. It also uses the data mining results to conduct educational guidance in electronic learning systems. It finds hidden patterns in student's course selection and predicts their final grades. The research also investigates the effect of activity, entrance method, time of attendance, and semester in electronic learning systems.