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Wednesday, 14 February 2018

CHANGING STUDENT’S ATTITUDE USING DATA MINING

PREDICTING PERSUASIVE MESSAGE FOR CHANGING STUDENT’S ATTITUDE USING DATA MINING

Abstract:
This paper aims to predict the factors and build prediction models for the persuasive message changing student’s attitude by applying classification techniques. We used a questionnaire to collect data such as gender, age and their satisfaction with persuasive messages, obtained from students at Khon Kaen University. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. We compared the results obtained from three algorithms. The results shown that the average classification correct rate for the ID3 was higher than the CART and the C4.5 algorithms. The best efficiency is 98.04%, 97.27%, and 96.73%, respectively.
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