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Application of Machine Learning Algorithm for Predicting Students Skill

J.Suganya 1 , T. Chakravarthy2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-3 , Page no. 286-290, Mar-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i3.286290

Online published on Mar 31, 2019

Copyright © J.Suganya, T. Chakravarthy . 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: J.Suganya, T. Chakravarthy, “Application of Machine Learning Algorithm for Predicting Students Skill,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.286-290, 2019.

MLA Style Citation: J.Suganya, T. Chakravarthy "Application of Machine Learning Algorithm for Predicting Students Skill." International Journal of Computer Sciences and Engineering 7.3 (2019): 286-290.

APA Style Citation: J.Suganya, T. Chakravarthy, (2019). Application of Machine Learning Algorithm for Predicting Students Skill. International Journal of Computer Sciences and Engineering, 7(3), 286-290.

BibTex Style Citation:
@article{Chakravarthy_2019,
author = {J.Suganya, T. Chakravarthy},
title = {Application of Machine Learning Algorithm for Predicting Students Skill},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {286-290},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3832},
doi = {https://doi.org/10.26438/ijcse/v7i3.286290}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.286290}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3832
TI - Application of Machine Learning Algorithm for Predicting Students Skill
T2 - International Journal of Computer Sciences and Engineering
AU - J.Suganya, T. Chakravarthy
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 286-290
IS - 3
VL - 7
SN - 2347-2693
ER -

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Abstract

The accurate prediction of student cognitive skill is important, for improving student academic performance.In this paper, a model is proposed to predict the students’ performance in an academic organization. A machine learning algorithm Naïve Bayes is used for prediction. Further, the importance of different cognitive factor is considered, in order to determine which of these are correlated with student performance. Result proves that Naïve Bayes algorithm provides more accuracy over other methods for comparison and prediction.

Key-Words / Index Term

Cognitive skills, Students’ performance, Machine learning, Naïve Bayes

References

[1] Y. Wang, Y. Wang, S. Patel, and D. PatelA, "LayeredReference Model of the Brain (LRMB)," IEEETransactions on Systems, Man, and Cybernetics Part C:Applications and Reviews, vol. 36, March 2006.
[2] -F. Rodriguez, F. Ramos, and Y. Wang, "CognitiveComputational Models of Emotions," presented at theCognitive Informatics & Cognitive Computing (lCCI*CC), 2011 10th IEEE International Conference on, 2011.
[3] Y. Wang, Y. Wang, S. Patel, and D. Patel A, "LayeredReference Model of the Brain (LRMB)," IEEETransactions on Systems, Man, and Cybernetics Part C:Applications And Reviews, vol. 36, March 2006.
[4] L.-F. Rodriguez, F. Ramos, and Y. Wang, "CognitiveComputational Models of Emotions," presented at theCognitive Informatics & Cognitive Computing (ICCI*CC), 2011 10th IEEE International Conference on, 2011.
[5] A. J. Karran, S. Fairclough, and K. Gilleade, "AFramework for Psychophysiological Classification withina Cultural Heritage Context Using Interest," ACMTransactions on Computer-Human Interaction, Vol. 21,No. 6, Article 34, Publication date: January 2015.
[6] G. Matthews, J. Carlos P. Gonzalez, A. N. Fellner, G. J.Funke, A. K. Emo, M. Zeidner, and R. D. Roberts,"Individual Differences in Facial Emotion Processing:167Trait Emotional Intelligence, Cognitive Ability, or
Transient Stress?," Journal of Psycho-educationalAssessment 2015, Vol. 33(1) 68-82
[7] Ruth A. Lamont, Hannah J. Swift, and Dominic Abrams,"A Review and Meta-Analysis of Age-Based StereotypeThreat: NegativeStereotypes, Not Facts, Do the Damage,"Psychology and Aging 2015 http://dx.doi.org/10.1037/a0038586.
[8] P. Winkielman, P. Niedenthal, J. Wielgosz, J. Eelen, andL. C. Kavanagh,"Embodiment of Cognition and Emotion," 2015 by the American Psychological Association
http://dx.doi.org/1O.1037/14341-004.
[9] J. M. Tien and J. P. Burnes, "On the Perceived Speed ofTime Over Time," presented at the Systems, Man, andCybernetics, 2000 IEEE International Conference on,Nashville, TN, 2000.
[10] A.-A. Samadani and Z. Moussavi, "The Effect of Aging onHuman Brain Spatial Processing Performance," presentedat the 34th Annual International Conference of the IEEEEMBS, San Diego, California USA, 2012.
[11] D. C. Park, "The basic mechanisms accounting for agerelateddecline in cognitive function," in Cognitive aging:
A primer ii, ed, 2000, pp. 3-19.
[12] C. Ayoub, E. O`Connor, G. Rappolt-Schlictmann, C.Vallotton, H. Raikes, and R. Chazan-Cohen, "Cognitiveskill performance among young children living in poverty:Risk, change, and the promotive effects of Early HeadStart," Early Childhood Research Quarterly., vol. 24, pp.289-305, 2009.
[13] Y. Jing, S. Jing, C. Huajian, S. Chuangang, and L. Yan,"The gender difference in distraction of background musicand noise on the cognitive task performance," presentedat the Natural Computation (ICNC), 2012 EighthInternational Conference on, Chongqing, 2012.
[14] D. Mokeddem, and H.Belbachir, Distributedclassification using class-association rules miningalgorithm`," `Book Distributed classification classassociation rules mining algorithm`(lEEE, edn.), pp334-337.
[15] S. Nasehi and H. Pourghassem, "Optimal EEG-basedEmotion Recognition Algorithm Using GaborFeatures," WSEAS Transactions on Signal Processing,vol. 8, July 2012.
[16] Sz. L. T6th I, D. Sztah61, K. Vicsi I, "Speech EmotionPerception by Human and Machine," Laboratoryof Speech Acoustics, Budapest University of Technology and Economics, Department of Telecommunications andMedia Informatics, Stoczek u. 2, 1111 Budapest, Hungary.