Using Decision Trees for Predicting Student Academic Performance
In this study, authors Anupama Kumar and Dr. Vijayalakshmi use decision trees to predict student academic performance. Data will be used to help tutors make better decisions regarding student success and academic needs.
The authors look to find a way use a combined method of decision trees and rule mining to predict student performance in upcoming tests, quizzes, etc for the use of tutors to create a better way to teach these students. In this study, the authors use a decision tree algorithm called C4.5 to predict whether students will pass or fail said exam. The outcome of the decision tree analysis was then used in a comparative analysis which stated the prediction helped students who were struggling, and challenged excelling students to reach higher levels of success.
Decision trees are a successful way to predict student outcome and improve their results based on prior educational data. Decision tree algorithms can be better rated for efficiency based on their accuracy and time taken to derive the tree.
Kumar, S. Anupama, Vijayalakshmi, Dr., (2009). Efficiency of decision trees in predicting student’s academic performance. Retrieved from http://airccj.org/CSCP/vol1/csit1230.pdf