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Analysis and Prediction of Student’s Academic Performance in University Courses

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 160 - Number 4
Year of Publication: 2017
Authors:
Garima Sharma, Santosh K. Vishwakarma
10.5120/ijca2017913045

Garima Sharma and Santosh K Vishwakarma. Analysis and Prediction of Student’s Academic Performance in University Courses. International Journal of Computer Applications 160(4):40-44, February 2017. BibTeX

@article{10.5120/ijca2017913045,
	author = {Garima Sharma and Santosh K. Vishwakarma},
	title = {Analysis and Prediction of Student’s Academic Performance in University Courses},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {4},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {40-44},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume160/number4/27065-2017913045},
	doi = {10.5120/ijca2017913045},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Management of huge amount of data has always been a matter of concern. With the increase in awareness towards education, the amount of data in educational institutes is also increasing. The increasing growth of educational databases, have given rise to a new field of data mining, known as Educational Data Mining (EDM). With the help of this one can predict the academic performance of a student that can help the students, their instructors and also their guardians to take necessary actions beforehand to improve the future performance of a student. This paper deals with the implementation of ID3 decision tree algorithm to build a predictive model based on the previous performances of a student. The dataset used in this paper is the semester data of the students of a private institute of India. Rapidminer, an open source software platform is used to obtain the results.

References

  1. Han, J., Kamber, M., Pei, J.. Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann; 3rd ed.; 2012. ISBN 978-0-12- 381479-1.
  2. Pujari, K., A.. Data Mining Techniques. ISBN-10:8173713804.
  3. Scheuer, O. & McLaren, B.M. (2011). Educational Data Mining. In the Encyclopedia of the Sciences of Learning, Springer.
  4. Altujjar, Y., Altamimi,W., Al-Turaiki, I., Al-Razgan, M., Predicting Critical Courses Affecting Students Performance: A Case Study. Procedia Computer Science 82 ( 2016 ) 65 71.
  5. Badr, G., Algobail, A., Almutairi, H., Almutery, M., Predicting Students Performance in University Courses: A Case Study and Tool in KSU Mathematics Department. Procedia Computer Science 82 ( 2016 ) 80 89.
  6. Elakia, Gayathri, Aarthi, Naren J., Application of Data Mining in Educational Database for Predicting Behavioural Patterns of the Students. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4649-4652.
  7. Shahiri,A., M., Husain,W., Rashid,N.,A., A Review on Predicting Students Performance using Data Mining Techniques. Procedia Computer Science 72 ( 2015 ) 414 422.
  8. Rapidminer Studio Documentation http://docs.rapidminer.com/studio/

Keywords

Data Mining, EDM, KDD, Classification, Decision tree, ID3, Student’s academic performance prediction