earticle

영어

Mining data in educational filed is an important and useful task for anybody related to educational institute. The useful information mined from the data can help with performance, guidance, teaching, planning and etc. for staff, students and instructors. Different educational data mining researches has been carried out on student data which includes the student’s basic achievements and educational background, academic scores and the amount of credit hours, but the relations between these are limited. Therefore in this paper we have described a system using data mining technologies such as decision tree. We have analyzed ID3 and J48 (C4.5) algorithms for predicting the scholarship winning chances on student data by translating the decision tree into “IF-THEN” rules and implementing these rules for prediction in our system called scholarship calculator. We have mined student data to calculate the chances of winning scholarship depending on their semester grades, position/rank of student in class, achievements, maximum and minimum amount of taken and allowed credit hours and extra curriculum activities. We found that ID3 works better even though J48 is faster in classifying data and creates a smaller tree than ID3. Because of ID3’s bigger tree it has more rule, more rules means more crosschecking and deeper decision, that’s why the predicted result was more accurate than J48. We have also described how our scholarship calculator works to predict and calculate the chances of winning scholarship. Performance evaluation is done and the results are also compared with already existing datasets. The developed system could be very useful in predicting student’s chances of winning scholarship from the first semester. It can help students to pinpoint the weak areas, which can be perfected with proper guidance from instructors and staff for better chances of winning scholarship.