First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms
The research on educational field that involves Data Mining techniques is rapidly increasing. Applying Data Mining techniques in an educational environment are known as Educational Data Mining that aims to discover hidden knowledge and patterns about students’ behaviour. This research aims to dev...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/471/1/FH03-FIK-14-01924.pdf http://eprints.unisza.edu.my/471/2/FH03-FIK-14-01925.pdf http://eprints.unisza.edu.my/471/ |
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| Summary: | The research on educational field that involves Data Mining techniques is rapidly increasing.
Applying Data Mining techniques in an educational environment are known as Educational Data
Mining that aims to discover hidden knowledge and patterns about students’ behaviour. This research
aims to develop Students’ Academic Performance prediction models for the first semester Bachelor of
Computer Science from Universiti Sultan Zainal Abidin (UniSZA)by using three selected
classification methods; Naïve Bayes, Rule Based, and Decision Tree. The comparative analysis is also
conducted to discover the best classification model for prediction. From the experiment, the models
develop using Rule Based and Decision Tree algorithm shows the best result compared to the model
develop from the Naïve Bayes algorithm. Five independent parameters(gender, race, hometown,
family income, university entry mode) have been selected to conduct this study. These parameters are
chosen based on prior research studies including from social sciences domains. The result discovers
the race is a most influence parameter to the students’ performance followed by family income,
gender, university entry mode, and hometown location parameters. The prediction model can be used
to classify the students so the lecturer can take an early action to improve students’ performance. |
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