Education Data Mining to Predict Student Exam Grades in Vocational Institutes
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Date
2015-12
Authors
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Publisher
The British University in Dubai (BUiD)
Abstract
Data Mining (Data Knowledge Discovery) is the practice of examining large pre-existing databases in order to identify patterns, establishing relationships, and to generate new understandable and useful information. Educational Data Mining (EDM) is a new emergent field that concerns about applying data mining methods and algorithms on data residing in educational system repositories. It can be used in the purpose of discovering valuable knowledge such as learning environment, students’ performance, dropout, and even students at more risk.
In this thesis, classification modeling will be used as a data mining technique to predict students’ performance in the theoretical exam (Final Test) for computer science (CS) course.
The analysis shows that the best prediction accuracy is obtained when CART classification algorithm is used as a classifier. The analysis also shows that certain student assessments are ineffective in predicting the student performance in the final exam. Our analysis also shows certain student features such as the gender the campus are good predictors to student performance.
Description
DISSERTATION WITH DISTINCTION
Keywords
Educational Data Mining (EDM), student exam grades, vocational institutes, data mining, student performance