DATA MINING TECHNIQUES FOR PREDICTING ACHIEVEMENT OF STUDENTS BY BLENDED LEARNING INSTRUCTION
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Published:
Apr 4, 2018
Keywords:
Prediction Model Blended Learning Instruction Data Mining Classification
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Abstract
The purpose of this research is to develop the a data mining model for predicting learning achievement of students based on Blended Learning Instruction. The data that is used to create model was gathered from the learner’s exercise scores throughout semesters. There are 20 variables. The data is separated into 2 sets using the feature selection techniques; 1) Information Gain (IG) and 2) Gain Ration (GR). After the feature selection process, the datais reduced in order to evaluate the predicting model’s performance by using 10-fold Cross Validation. The experimenting results showed that the highest accuracy rate is the K-Nearest Neighbor as 86.13%, the Decision Tree as 81.74%, the Rule Base as 81.67%, and Naïve Bays 55.05%.
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บทความวิจัย
References
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Mladenic, D., & Grobelnik, M. (1999). Feature Selection for Unbalanced Class Distribution and Naive Bayes. Paper presented at the Proceedings of the Sixteenth International Conference on Machine Learning.
Pal, S. (2012). Mining Educational Data Using Classification to Decrease Dropout Rate of Students. CoRR, abs/1206.3078.
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Quinlan, J. R. (1990). Decision trees and decision-making. Systems, Man and Cybernetics, IEEE Transactions on, 20(2), 339-346.
Rokach, L., & Maimon, O. Z. (2008). Data mining with decision trees : theroy and applications. Singapore ; Hackensack, NJ: World Scientific.
Tan, P. N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining: Pearson Addison Wesley.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques: Elsevier Science.
Yadav, S. K., & Pal, S. (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification.