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Predicting Student Performance using Data Mining

Mabel Christina1

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-10 , Page no. 172-177, Oct-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i10.172177

Online published on Oct 31, 2018

Copyright © Mabel Christina . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Mabel Christina, “Predicting Student Performance using Data Mining,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.172-177, 2018.

MLA Style Citation: Mabel Christina "Predicting Student Performance using Data Mining." International Journal of Computer Sciences and Engineering 6.10 (2018): 172-177.

APA Style Citation: Mabel Christina, (2018). Predicting Student Performance using Data Mining. International Journal of Computer Sciences and Engineering, 6(10), 172-177.

BibTex Style Citation:
@article{Christina_2018,
author = {Mabel Christina},
title = {Predicting Student Performance using Data Mining},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2018},
volume = {6},
Issue = {10},
month = {10},
year = {2018},
issn = {2347-2693},
pages = {172-177},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3000},
doi = {https://doi.org/10.26438/ijcse/v6i10.172177}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i10.172177}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3000
TI - Predicting Student Performance using Data Mining
T2 - International Journal of Computer Sciences and Engineering
AU - Mabel Christina
PY - 2018
DA - 2018/10/31
PB - IJCSE, Indore, INDIA
SP - 172-177
IS - 10
VL - 6
SN - 2347-2693
ER -

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Abstract

Data mining focuses on collection information from knowledge bases or data warehouses and therefore the info collected that had never been famous before, it`s valid and operational. today instructional data processing is associate rising discipline, involved with varied Approaches like Predicting student performance, Analysis and visual image of information, Providing feedback for supporting instructors, Recommendations for college students, Social network analysis and then thereon mechanically extracts that means from giant repositories of information generated by or associated with people`s learning activities in instructional setting. One of the most important challenges is to enhance the standard of the academic processes therefore on enhance student’s performance. Thus, it`s crucial to line new ways and plans for an improved management of the present processes. This model helps to predict student’s future learning outcomes mistreatment knowledge sets of senior students.

Key-Words / Index Term

Data Mining, Educational Data Mining

References

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