What Satisfies Students? Mining Student-Opinion Data with Regression and Decision Tree Analysis

Analysis of student-opinion data reveals that social integration has more effect on the satisfaction of students who are less academically engaged, and decision tree analysis reveals that faculty preparedness emerges as a principal determinant of satisfaction.

Abstract

To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The three measures have different predictors and cannot be used interchangeably. Academic experiences are influential. In particular, faculty preparedness, which has a well-known relationship to student achievement, emerges as a principal determinant of satisfaction. Social integration and pre-enrollment opinions are also important. Campus services and facilities have limited effects, and students' demographic characteristics are not significant predictors. Decision tree analysis reveals that social integration has more effect on the satisfaction of students who are less academically engaged.