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Prediction of college student academic performance using data mining techniques.


Abd Jalil, Azura and Mustapha, Aida and Santa, Dzulizah and Zain, Nurul Zaiha and Radwan, Rizalina (2013) Prediction of college student academic performance using data mining techniques. In: International Conference on Engineering Education 2013, 22-25 Dec. 2013, Madinah, Kingdom of Saudi Arabia. (pp. 268-271).


For every learning institution whether schools, colleges or universities, high student success rate is the strategic goal that mirrors the reputation of an institution. However, case-by-case analysis is a daunting task and does not take into account data from a number of previous semesters or trend of overall performance in a particular semester. This study attempts to predict the success rate of students’ academic performance by analyzing their examination results to secure a place at college level for the subsequent semester. The classification algorithms used are the Decision Tree, Naïve Bayesian, and Multilayer Perception with the highest classification accuracy by the Naive Bayes algorithm with accuracy of 95.3%. High accuracy demonstrates the ability of data mining classification task in helping the institutions to predict student performance and to identify group of weak students. This will in turn help to improve their performance at much earlier stage.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
Notes: Full text are available at Special Collection Division Office.
Keywords: Decision tree; Naïve Bayesian; Multilayer perception; Data mining; Prediction.
Depositing User: Erni Suraya Abdul Aziz
Date Deposited: 13 Jun 2014 01:03
Last Modified: 27 Jun 2014 07:49
URI: http://psasir.upm.edu.my/id/eprint/31371
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