Data Mining Applications: A comparative Study for Predicting Student's performance
This addresses the issue of improving educational quality through data-driven decision-making for teachers and institutions, but it is incremental as it uses existing methods on new data.
The paper tackles the problem of predicting student performance by applying decision tree algorithms to students' past performance data, resulting in a model that can identify dropouts and students needing special attention for teacher intervention.
Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.