Predicting Student Performance Using Classification Data Mining Techniques

The term education data mining deals with extracting knowledge out of academic database which can be used for providing suitable patterns to education managers, teachers, and students. Education is a progressing field and students need to put in extra efforts to keep the right move towards learning. This paper presents on approach to study the student data and implementing various data mining classification algorithms. Thus, finding out the best algorithm, that can help in evaluating the final grade of a student and finding the best fit for identification of possible results beforehand, so that appropriate interventions can be planned. For our research we collected the data from a reputed higher education institute related to a set of students pertaining to their current and previous academic records. The data were filtered, cleaned, and processed for training different data mining models to define classifications based on different criteria. This method may be considered useful in finding out the students who are at the state of high risk in a very early stage, thus allowing the educationists to provide the appropriate advice to learners in a timely manner