Predicting students’ satisfaction using a decision tree (original) (raw)

Tertiary Education and Management, 2019

Abstract

This research focuses on students’ satisfaction and on how students’ satisfaction relates to their performance and involvement in study activities in the e-classroom. Our research is a case study at the course level of a business and economics study programme at a private higher education institution in Slovenia. The study is based on decision-tree induction, a highly used algorithm in a variety of domains for knowledge discovery and pattern recognition using a data mining approach. The results revealed that students are less satisfied with a course when both the requirements for the involvement in the e-classroom and the workload are both high. Further, the average grade might not be of crucial importance when addressing student satisfaction. In our case, students are much more satisfied with a course when the average grades are high and when the workload is not so elevated and when a part of the workload moves to the e-classroom.

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