Can we use Decision Tree for the predication models ??? Better or Worse ??

In most of my previous posts were focused on use of various data mining or machine learning approaches on educational data to understand the learning patterns of different students. Educational data mining is a new emerging practice of data mining that can be applied on the data related to the field of education. Process of transforming raw educational data which are collected by education learning systems could be used to take informed decisions on students learning problems. The various techniques of data mining like classification, clustering and rule mining can be applied to bring out various hidden patterns from the educational data.

 

In overall student learning environments upstanding the overall student performance will be beneficial in examinations which is playing a vital role in any student’s life. The marks obtained by the student in the examinations for different course modules will decide the overall grade he obtain in finally. Therefore it is becoming essential for any tutor to understand whether different students will pass or fail in the examinations in beforehand they face them. Based on these predictions tutors can help the students in prior to the examinations and extra efforts can be taken to improve their studies and help them to pass the examinations.

 

S. Anupama Kumar and  M Vijayalakshmi [2011] have tried  to predict the student overall performance based on their internal assessments in the learning environment. In their research they considered five course modules which were offered in a semester and overall student count for the selected analysis were about 117. The algorithms they used for this research is J48 and ID3 decision tree algorithms. According to their discussion the accuracy level of the J48 was higher than the ID3 algorithm since it has predict more correct prediction results than the ID3 algorithm. Based on the different accuracy levels on each of the decision tree they created they concluded that classification techniques can be applied on educational data for predicting the student’s outcome and improve their results and the efficiency of various decision tree algorithms can be analyzed based on their accuracy and time taken to derive the tree. Finally they argue that the application of data mining brings a lot of advantages in higher learning institutions so that these techniques can be applied in the areas of education to optimize the resources allocations as needed with the student learning capacity .

Reference: S. A. Kumar and M. N. Vijayalakshmi, “Efficiency of decision trees in predicting student’s academic performance,” in First International Conference on Computer Science, Engineering and Applications, CS and IT, 2011, vol. 2, pp. 335–343.