Increasing Student Retention in the Course of Data Structure Through the Implementation of Data Mining



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With its strong commitment in supporting students to complete a high quality education within four years, this research wishes to extend this commitment and shed light on improving students' performances in class. In doing so, it is going to use the data collected from the course "Data Structure" to examine students' sense of their own achievement, in order to better understand their learning outcomes. It aims to propose a framework to predict individual's class performance, so instructors can take early actions and provide students with the help needed to succeed. This research shows that despite the exam difficulties, students have a general idea how well they will perform on the exam. This means the likelihood for un-prepared students to have good fortune and receive exceptionally good grades is extremely low. Thus, low self-assessment responses give out the first signs of dropping and failing, and students should trust their intuitions and hold themselves responsible for seeking help. On the other hand, students with high self-assessment responses and an accurate sense of how well they will perform, tend to have stable performance over time.