Student Dropout Prediction & Educational Data Mining
Mahesh Mardolkar1, N Kumaran2
1Mahesh Mardolkar, Research Scholar, Department of Computer & Information Science, Annamalai University, Chidambaram, (Tamil Nadu), India.
2Dr. N. Kumaran, Department of Computer & Information Science, Annamalai University, Chidambaram, (Tamil Nadu), India.
Manuscript received on November 26, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 5190-5192 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4246129219/2019©BEIESP | DOI: 10.35940/ijeat.B4246.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Educational data like students performance is very important to study and analyze and to improve the quality of education. The study concerned to data mining techniques with educational data is known as Educational Data Mining (EDM). This study finds knowledge and interesting patterns in educational organization. Students performance are the subject mainly concerned to find the qualitative model based on student’s personal and social factors then classify and predict the student performance. Proper counseling to underperforming students can reduce dropout ratio and help them to continue their studies.
Keywords: Data Mining, Education, Patterns, Performance, Student.