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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. 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