First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms

Azwa, Abdul Aziz and Fadhilah, Ahmad (2014) First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms. In: 2nd International Conference on Intelligence and Computer Science (AICS 2014), 15-16 September 2014, Bandung, Indonesia.

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Abstract

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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