Improving students learning process by analyzing patterns produced with data mining methods

Employing data mining algorithms on previous student's records may give important results in defining new ways of learning and during the process of development of a new curriculum for educational institutions. Creating group profiles by analyzing certain attributes of the students helps in defining more specifically the needs of the students. Achieving this requires manipulation of data in a structured database, and most important a complete data set, having that incomplete data sets may produce unreliable outputs. This paper presents the results of different data mining algorithms applied on previous student's records to produce predicted success results, and the comparison with the real data in database. Results show that, even if there is lack of attributes, one may still apply certain data mining algorithms over school data to gain knowledge on the mainstream flow. Besides prediction, one can cluster data in order to get main characteristics on the student's performance. The experiment presented in this paper will emphasize that dividing data in fewer classes will result in higher cluster sum of squared errors, which in fact show that there exist big difference between data.