Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan

The paper focuses on prediction of student learning performance in object oriented programming course using data mining technique based on a dataset obtained from Kolej Poly-Tech Mara (KPTM), Kuantan. The objective was to identify and implement the most accurate algorithm for the KPTM dataset and to...

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Main Authors:	Mohd Hanis, Rani, Abdullah, Embong
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/5099/ http://umpir.ump.edu.my/id/eprint/5099/1/35-UMP.pdf

id	ump-5099
recordtype	eprints
spelling	ump-50992018-05-02T07:01:08Z http://umpir.ump.edu.my/id/eprint/5099/ Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan Mohd Hanis, Rani Abdullah, Embong QA75 Electronic computers. Computer science The paper focuses on prediction of student learning performance in object oriented programming course using data mining technique based on a dataset obtained from Kolej Poly-Tech Mara (KPTM), Kuantan. The objective was to identify and implement the most accurate algorithm for the KPTM dataset and to come up with a good prediction model using decision tree technique. The most relevant rules were identified from the model. The dataset was run through some pre-processing such as data cleaning, data reduction and discretization. The experiments were conducted using machine learning software Weka 3.6.9. The first experiment was to test the clean dataset with seven classification techniques. Accuracy plays an important role to prove the best classification technique by using correctly classified instance as an indicator. Using 10-fold cross validation for each algorithm, it was found that decision tree was the best algorithm with 83.6944% correctness. The second experiment was conducted to find the best model among the percentage split where the best percentage split produced the best model accuracy. The experiment with 50% of data training and 50% of data testing in percentage split produced higher accuracy where the percentage of correctly classified instance was 76.2557%. The rules were extracted from the model and after the analyses were conducted the result showed that the domain factors of student performance were class attendance and the performance of the previous semester. 2013-08-20 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5099/1/35-UMP.pdf Mohd Hanis, Rani and Abdullah, Embong (2013) Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan. In: 3rd International Conference on Software Engineering & Computer Systems (ICSECS - 2013), 20-22 August 2013 , Universiti Malaysia Pahang. pp. 11-8.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Mohd Hanis, Rani Abdullah, Embong Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
description	The paper focuses on prediction of student learning performance in object oriented programming course using data mining technique based on a dataset obtained from Kolej Poly-Tech Mara (KPTM), Kuantan. The objective was to identify and implement the most accurate algorithm for the KPTM dataset and to come up with a good prediction model using decision tree technique. The most relevant rules were identified from the model. The dataset was run through some pre-processing such as data cleaning, data reduction and discretization. The experiments were conducted using machine learning software Weka 3.6.9. The first experiment was to test the clean dataset with seven classification techniques. Accuracy plays an important role to prove the best classification technique by using correctly classified instance as an indicator. Using 10-fold cross validation for each algorithm, it was found that decision tree was the best algorithm with 83.6944% correctness. The second experiment was conducted to find the best model among the percentage split where the best percentage split produced the best model accuracy. The experiment with 50% of data training and 50% of data testing in percentage split produced higher accuracy where the percentage of correctly classified instance was 76.2557%. The rules were extracted from the model and after the analyses were conducted the result showed that the domain factors of student performance were class attendance and the performance of the previous semester.
format	Conference or Workshop Item
author	Mohd Hanis, Rani Abdullah, Embong
author_facet	Mohd Hanis, Rani Abdullah, Embong
author_sort	Mohd Hanis, Rani
title	Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
title_short	Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
title_full	Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
title_fullStr	Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
title_full_unstemmed	Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
title_sort	predicting student performance in object oriented programming using decision tree : a case at kolej poly-tech mara, kuantan
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/5099/ http://umpir.ump.edu.my/id/eprint/5099/1/35-UMP.pdf
first_indexed	2023-09-18T22:00:15Z
last_indexed	2023-09-18T22:00:15Z
_version_	1777414350621900800

Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan

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