Data Mining Techniques Applying on Educational Dataset to Evaluate Learner Performance Using Cluster Analysis

Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.