Identify at-risk students using clustering methods

International Conference on Big Data Analysis and Data Mining

May 04-05, 2015 Kentucky, USA

Abdul Mohsen Algarni

Scientific Tracks Abstracts: J Comput Sci Syst Biol

Abstract :

Data mining is used to convert raw data into useful information that could potentially have a great impact on the educational system. Data mining in educational data (EDM) is a developing method for extracting interesting, interpretable, useful, and novel information that can lead to better understanding of students and the settings in which they learn. With the increase in use of technology in education and the ability to store huge amount of data about student would lead to the importance of using EDM. EDM can be used in many different ways including identification of at-risk students, prioritization of learning needs for different groups of students, increasing graduation rates, effective assessment of institutional performance, maximization of campus resources, and optimization of subject curriculum renewal. Therefore, using data mining tools to improve the learning and teaching process has become essential, especially with the large amount of data available in the educational system. Data mining clustering methods can be used to group students in different groups based on their behavior to identifying at-risk students.

Biography :

Abdul Mohsen Algarni is an Assistant Professor at the King Khalid University. He has completed his PhD in Computer Science from Queensland University of Technology in 2011. He was nominated for the Outstanding Thesis Award at Queensland University of Technology. His research interests are system analysis, software development, professional programming. His skills and expertise are machine learning, information extraction, data mining and knowledge, discovery, text mining, web mining, pattern recognition, feature extraction, clustering, text classification, artificial intelligence, information science, algorithms and large scale data analysis.