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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/5099/1/35-UMP.pdf
http://umpir.ump.edu.my/id/eprint/5099/
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spelling my.ump.umpir.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)
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/5099/1/35-UMP.pdf
http://umpir.ump.edu.my/id/eprint/5099/
_version_ 1643665131601657856
score 13.211869

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