Enhancement of Student Experience Management in Higher Education by Sentiment Analysis and Text Mining

Abstract

The objective of this case study is to illustrate how text mining of open-ended responses and sentimental expressions (positive or negative) from student survey could yield valuable information for improving student experience management (SEM). The concept of student SEM was borrowed from the notion of customer experience management (CEM), which aims for ongoing improvement of customer relations through understanding of the customer's point of view. With the advance of text mining technology, which is based upon artificial intelligence and machine learning, textual data that were previously underutilized are found to be valuable in CEM. To illustrate how text mining can be applied to SEM, the authors discuss an example from a campus-wide survey conducted at Arizona State University. The purpose of this survey was to better understand student experiences with instructional technology in order for administrators to make data-driven decisions on its implementation. Rather than imposing the researchers' preconceived suppositions on the students by using force-option survey items, researchers on this project chose to use open-ended questions in order to elicit a free emergence of themes from the students. The most valuable lesson learned from this study is that students perceive an ideal environment as a web of mutually supporting systems. Specifically, online access should be augmented by use of laptops and availability of course materials, whereas virtual classes should be balanced by human interactions.