Open AccessUsing Data Mining for Improving Education of University Students – A Survey
Author(s) -
K. P. S. D. Kumarapathirana
Publication year - 2018
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol6.iss9.1155
Subject(s) - coaching , educational data mining , computer science , data science , learning management , open access journal , action (physics) , knowledge management , world wide web , psychology , physics , medline , quantum mechanics , political science , law , psychotherapist , scopus
Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.