Advanced Scholastic Analytics by Implementing Data Mining Techniques
Madhav Singh Solanki
Madhav Singh Solanki, Department of Computer Science and Engineering, Sanskriti University, (Uttar Pradesh), India.
Manuscript received on 04 October 2019 | Revised Manuscript received on 18 October 2019 | Manuscript Published on 26 December 2019 | PP: 95-98 | Volume-8 Issue-12S October 2019 | Retrieval Number: L102910812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1029.10812S19
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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: Over the past few years, the adaption of Edification management System in the sector of education has increased. Mining, clustering of essential data and finding out unique patterns from the field of education to research student’s behaviors and performance is widely recognized as Educational Data Mining (EDM) and it is a progressing profession involved with the production of new techniques for discovering distinctive and progressively large-scale information from educational environments and employing those techniques for deeper comprehend learning. It also provides an inherent understanding of teaching and learning processes for the efficient scheduling of education. This paper recommends the use of two information mining techniques in educational data. First, in admittance information, the association rule was implemented to discover some knowledge to support admission schedules. Second, a decision tree was implemented in grades and graduate student job information to estimate work type after graduation. The findings of this research provide an excellent understanding of admittance scheduling and work prediction.
Keywords: Educational Data Mining, Classification, Association Rule Mining Education Planning, Clustering, Scheduling.
Scope of the Article: Data Mining